From b001302436ebe6d10c032c755b8d4bb87ce1a7e8 Mon Sep 17 00:00:00 2001 From: Martin Schuck Date: Mon, 24 Jun 2024 00:34:43 +0200 Subject: [PATCH] Finish primary refactor. Add tests. --- pyproject.toml | 3 +- safe_control_gym/__init__.py | 2 +- safe_control_gym/envs/__init__.py | 8 +- safe_control_gym/envs/assets/gate.obj | 48037 ---------------- safe_control_gym/envs/assets/gate.urdf | 150 +- safe_control_gym/envs/assets/low_portal.urdf | 170 - safe_control_gym/envs/assets/obstacle.urdf | 46 +- safe_control_gym/envs/assets/portal.urdf | 171 - safe_control_gym/envs/assets/sphere.urdf | 24 - safe_control_gym/envs/base_aviary.py | 620 - safe_control_gym/envs/constants.py | 4 - safe_control_gym/envs/constraints.py | 18 +- safe_control_gym/envs/disturbances.py | 285 +- safe_control_gym/envs/drone.py | 62 +- safe_control_gym/envs/drone_racing_env.py | 208 + safe_control_gym/envs/drone_sim.py | 488 +- safe_control_gym/envs/firmware_wrapper.py | 121 - safe_control_gym/envs/physics.py | 262 + safe_control_gym/envs/quadrotor.py | 691 - safe_control_gym/envs/quadrotor.yaml | 40 - safe_control_gym/envs/quadrotor_utils.py | 243 +- safe_control_gym/envs/symbolic.py | 94 + safe_control_gym/envs/utils.py | 9 +- .../math_and_models/symbolic_systems.py | 6 +- .../math_and_models/transformations.py | 193 +- safe_control_gym/utils/registration.py | 145 - {safe_control_gym/utils => tests}/__init__.py | 0 tests/integration_tests/config/test_sim.toml | 99 + tests/integration_tests/test_sim.py | 21 + tests/scripts/tracking.py | 84 - tests/scripts/tracking.yaml | 29 - tests/scripts/verbose_api.py | 156 - tests/scripts/verbose_api.yaml | 186 - tests/test_build.py | 6 - tests/test_competition/test_competition.py | 7 - tests/test_examples/test_examples.py | 12 - 36 files changed, 1529 insertions(+), 51171 deletions(-) delete mode 100644 safe_control_gym/envs/assets/gate.obj delete mode 100644 safe_control_gym/envs/assets/low_portal.urdf delete mode 100644 safe_control_gym/envs/assets/portal.urdf delete mode 100644 safe_control_gym/envs/assets/sphere.urdf delete mode 100644 safe_control_gym/envs/base_aviary.py create mode 100644 safe_control_gym/envs/drone_racing_env.py delete mode 100644 safe_control_gym/envs/firmware_wrapper.py create mode 100644 safe_control_gym/envs/physics.py delete mode 100644 safe_control_gym/envs/quadrotor.py delete mode 100644 safe_control_gym/envs/quadrotor.yaml create mode 100644 safe_control_gym/envs/symbolic.py delete mode 100644 safe_control_gym/utils/registration.py rename {safe_control_gym/utils => tests}/__init__.py (100%) create mode 100644 tests/integration_tests/config/test_sim.toml create mode 100644 tests/integration_tests/test_sim.py delete mode 100644 tests/scripts/tracking.py delete mode 100644 tests/scripts/tracking.yaml delete mode 100644 tests/scripts/verbose_api.py delete mode 100644 tests/scripts/verbose_api.yaml delete mode 100644 tests/test_build.py delete mode 100644 tests/test_competition/test_competition.py delete mode 100644 tests/test_examples/test_examples.py diff --git a/pyproject.toml b/pyproject.toml index 3195501a0..5a04f8634 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,10 +6,11 @@ authors = ["Your Name "] license = "MIT" [tool.poetry.dependencies] +# TODO: Clean up dependencies python = "^3.8.10" matplotlib = "^3.5.1" Pillow = "^9.0.0" -munch = "^2.5.0" +munch = "^4.0.0" PyYAML = "^6.0" imageio = "^2.14.1" dict-deep = "^4.1.2" diff --git a/safe_control_gym/__init__.py b/safe_control_gym/__init__.py index 7247fee8d..3b0d91371 100644 --- a/safe_control_gym/__init__.py +++ b/safe_control_gym/__init__.py @@ -1 +1 @@ -from safe_control_gym.envs import * +import safe_control_gym.envs # noqa: F401, load envs into gymnasium registry diff --git a/safe_control_gym/envs/__init__.py b/safe_control_gym/envs/__init__.py index 7cf284c00..da781d6e4 100644 --- a/safe_control_gym/envs/__init__.py +++ b/safe_control_gym/envs/__init__.py @@ -3,12 +3,12 @@ from gymnasium import register register( - id="quadrotor", - entry_point="safe_control_gym.envs.quadrotor:Quadrotor", + id="drone_sim", + entry_point="safe_control_gym.envs.drone_sim:DroneSim", ) register( - id="firmware", - entry_point="safe_control_gym.envs.firmware_wrapper:FirmwareWrapper", + id="drone_racing-v0", + entry_point="safe_control_gym.envs.drone_racing_env:DroneRacingEnv", max_episode_steps=900, # 30 seconds * 30 Hz ) diff --git a/safe_control_gym/envs/assets/gate.obj b/safe_control_gym/envs/assets/gate.obj deleted file mode 100644 index 1c2f56409..000000000 --- a/safe_control_gym/envs/assets/gate.obj +++ /dev/null @@ -1,48037 +0,0 @@ -# 3ds Max Wavefront OBJ Exporter v0.97b - (c)2007 guruware -# File Created: 06.12.2012 23:41:57 - -# -# object 18370_Shinto_Torii_Gate_v1 -# - -v -2.1931 -0.2909 4.6632 -v -2.1931 -0.2909 4.6034 -v -2.1056 -0.2909 4.5973 -v -2.1056 -0.2909 4.6571 -v -2.1932 -0.2909 4.7341 -v -2.1056 -0.2909 4.7281 -v -2.2899 -0.2909 4.6719 -v -2.2899 -0.2909 4.6122 -v -2.2899 -0.2908 4.7426 -v -2.1932 -0.2908 4.8649 -v -2.1932 -0.2908 4.8050 -v -2.1056 -0.2908 4.7991 -v -2.1056 -0.2908 4.8589 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18311/1487/5161 10436/796/5161 10434/799/5621 18307/1496/5621 -f 18312/1488/5161 18311/1487/5161 18307/1496/5621 18309/1494/5621 -f 18086/1463/5621 18313/1492/5621 18314/1493/4501 18087/1466/4499 -f 18092/1454/5161 18315/1491/5161 18313/1492/5621 18086/1463/5621 -f 18313/1492/5621 18309/1494/5621 18310/1495/4499 18314/1493/4501 -f 18315/1491/5161 18312/1488/5161 18309/1494/5621 18313/1492/5621 -f 18316/1489/5164 18101/1448/5622 18104/1449/5165 18317/1490/5165 -f 18318/1485/5164 18316/1489/5164 18317/1490/5165 18319/1486/5165 -f 18315/1491/5161 18092/1454/5161 18101/1448/5622 18316/1489/5164 -f 18312/1488/5161 18315/1491/5161 18316/1489/5164 18318/1485/5164 -f 10439/792/5164 18320/1483/5164 18321/1484/5165 10440/795/5165 -f 10436/796/5161 18311/1487/5161 18320/1483/5164 10439/792/5164 -f 18320/1483/5164 18318/1485/5164 18319/1486/5165 18321/1484/5165 -f 18311/1487/5161 18312/1488/5161 18318/1485/5164 18320/1483/5164 -f 18322/1509/5176 10448/819/5625 10440/795/5165 18321/1484/5165 -f 18323/1508/5176 18322/1509/5176 18321/1484/5165 18319/1486/5165 -f 18324/1502/5178 10450/816/5624 10448/819/5625 18322/1509/5176 -f 18325/1503/5178 18324/1502/5178 18322/1509/5176 18323/1508/5176 -f 18106/1481/5625 18326/1507/5176 18317/1490/5165 18104/1449/5165 -f 18109/1474/5624 18327/1506/5178 18326/1507/5176 18106/1481/5625 -f 18326/1507/5176 18323/1508/5176 18319/1486/5165 18317/1490/5165 -f 18327/1506/5178 18325/1503/5178 18323/1508/5176 18326/1507/5176 -f 18328/1504/5623 18115/1468/5180 17971/1469/138 18227/1505/141 -f 18329/1500/5623 18328/1504/5623 18227/1505/141 18223/1501/140 -f 18327/1506/5178 18109/1474/5624 18115/1468/5180 18328/1504/5623 -f 18325/1503/5178 18327/1506/5178 18328/1504/5623 18329/1500/5623 -f 10452/812/5180 18330/1498/5180 18221/1499/139 2148/815/141 -f 10450/816/5624 18324/1502/5178 18330/1498/5180 10452/812/5180 -f 18330/1498/5180 18329/1500/5623 18223/1501/140 18221/1499/139 -f 18324/1502/5178 18325/1503/5178 18329/1500/5623 18330/1498/5180 -f 18331/1483/5781 11304/792/5781 11307/795/5784 18332/1484/5784 -f 18333/1485/5781 18331/1483/5781 18332/1484/5784 18334/1486/5784 -f 18335/1487/5785 11308/796/5785 11304/792/5781 18331/1483/5781 -f 18336/1488/5785 18335/1487/5785 18331/1483/5781 18333/1485/5781 -f 18118/1448/5781 18337/1489/5781 18338/1490/5784 18119/1449/5784 -f 18124/1454/5785 18339/1491/5785 18337/1489/5781 18118/1448/5781 -f 18337/1489/5781 18333/1485/5781 18334/1486/5784 18338/1490/5784 -f 18339/1491/5785 18336/1488/5785 18333/1485/5781 18337/1489/5781 -f 18340/1492/5788 18131/1463/5788 18087/1466/4499 18314/1493/4501 -f 18341/1494/5788 18340/1492/5788 18314/1493/4501 18310/1495/4499 -f 18339/1491/5785 18124/1454/5785 18131/1463/5788 18340/1492/5788 -f 18336/1488/5785 18339/1491/5785 18340/1492/5788 18341/1494/5788 -f 11311/799/5788 18342/1496/5788 18308/1497/4501 10433/800/4501 -f 11308/796/5785 18335/1487/5785 18342/1496/5788 11311/799/5788 -f 18342/1496/5788 18341/1494/5788 18310/1495/4499 18308/1497/4501 -f 18335/1487/5785 18336/1488/5785 18341/1494/5788 18342/1496/5788 -f 18343/1498/5798 11318/812/5798 3107/815/1636 18255/1499/1852 -f 18344/1500/6120 18343/1498/5798 18255/1499/1852 18253/1501/1636 -f 18345/1502/6121 11320/816/5800 11318/812/5798 18343/1498/5798 -f 18346/1503/6121 18345/1502/6121 18343/1498/5798 18344/1500/6120 -f 18134/1468/5798 18347/1504/6120 18251/1505/1636 18016/1469/1855 -f 18137/1474/6121 18348/1506/5800 18347/1504/6120 18134/1468/5798 -f 18347/1504/6120 18344/1500/6120 18253/1501/1636 18251/1505/1636 -f 18348/1506/5800 18346/1503/6121 18344/1500/6120 18347/1504/6120 -f 18349/1507/5803 18143/1481/5803 18119/1449/5784 18338/1490/5784 -f 18350/1508/6122 18349/1507/5803 18338/1490/5784 18334/1486/5784 -f 18348/1506/5800 18137/1474/6121 18143/1481/5803 18349/1507/5803 -f 18346/1503/6121 18348/1506/5800 18349/1507/5803 18350/1508/6122 -f 11323/819/5803 18351/1509/6122 18332/1484/5784 11307/795/5784 -f 11320/816/5800 18345/1502/6121 18351/1509/6122 11323/819/5803 -f 18351/1509/6122 18350/1508/6122 18334/1486/5784 18332/1484/5784 -f 18345/1502/6121 18346/1503/6121 18350/1508/6122 18351/1509/6122 -f 18352/1483/5781 14340/792/5781 14343/795/5784 18353/1484/5784 -f 18354/1485/5781 18352/1483/5781 18353/1484/5784 18355/1486/5784 -f 18356/1487/5785 14344/796/5785 14340/792/5781 18352/1483/5781 -f 18357/1488/5785 18356/1487/5785 18352/1483/5781 18354/1485/5781 -f 18146/1448/5781 18358/1489/5781 18359/1490/5784 18147/1449/5784 -f 18152/1454/5785 18360/1491/9714 18358/1489/5781 18146/1448/5781 -f 18358/1489/5781 18354/1485/5781 18355/1486/5784 18359/1490/5784 -f 18360/1491/9714 18357/1488/5785 18354/1485/5781 18358/1489/5781 -f 18361/1492/5788 18161/1463/5788 18164/1466/4499 18362/1493/4499 -f 18363/1494/5788 18361/1492/5788 18362/1493/4499 18364/1495/4499 -f 18360/1491/9714 18152/1454/5785 18161/1463/5788 18361/1492/5788 -f 18357/1488/5785 18360/1491/9714 18361/1492/5788 18363/1494/5788 -f 14347/799/5788 18365/1496/5788 18366/1497/4499 14348/800/4499 -f 14344/796/5785 18356/1487/5785 18365/1496/5788 14347/799/5788 -f 18365/1496/5788 18363/1494/5788 18364/1495/4499 18366/1497/4499 -f 18356/1487/5785 18357/1488/5785 18363/1494/5788 18365/1496/5788 -f 18367/1498/6120 14358/812/6120 6372/815/1636 18282/1499/1855 -f 18368/1500/5798 18367/1498/6120 18282/1499/1855 18280/1501/1852 -f 18369/1502/5800 14360/816/5800 14358/812/6120 18367/1498/6120 -f 18370/1503/5800 18369/1502/5800 18367/1498/6120 18368/1500/5798 -f 18166/1468/6120 18371/1504/6120 18278/1505/1636 18052/1469/1636 -f 18169/1474/5800 18372/1506/6121 18371/1504/6120 18166/1468/6120 -f 18371/1504/6120 18368/1500/5798 18280/1501/1852 18278/1505/1636 -f 18372/1506/6121 18370/1503/5800 18368/1500/5798 18371/1504/6120 -f 18373/1507/5803 18175/1481/5803 18147/1449/5784 18359/1490/5784 -f 18374/1508/5803 18373/1507/5803 18359/1490/5784 18355/1486/5784 -f 18372/1506/6121 18169/1474/5800 18175/1481/5803 18373/1507/5803 -f 18370/1503/5800 18372/1506/6121 18373/1507/5803 18374/1508/5803 -f 14363/819/5803 18375/1509/5803 18353/1484/5784 14343/795/5784 -f 14360/816/5800 18369/1502/5800 18375/1509/5803 14363/819/5803 -f 18375/1509/5803 18374/1508/5803 18355/1486/5784 18353/1484/5784 -f 18369/1502/5800 18370/1503/5800 18374/1508/5803 18375/1509/5803 -f 18376/1496/5621 15220/799/5621 14348/800/4499 18366/1497/4499 -f 18377/1494/5621 18376/1496/5621 18366/1497/4499 18364/1495/4499 -f 18378/1487/5161 15222/796/5161 15220/799/5621 18376/1496/5621 -f 18379/1488/5161 18378/1487/5161 18376/1496/5621 18377/1494/5621 -f 18178/1463/5621 18380/1492/5621 18362/1493/4499 18164/1466/4499 -f 18181/1454/5161 18381/1491/9715 18380/1492/5621 18178/1463/5621 -f 18380/1492/5621 18377/1494/5621 18364/1495/4499 18362/1493/4499 -f 18381/1491/9715 18379/1488/5161 18377/1494/5621 18380/1492/5621 -f 18382/1489/5164 18189/1448/5622 18192/1449/9713 18383/1490/5165 -f 18384/1485/5164 18382/1489/5164 18383/1490/5165 18385/1486/9713 -f 18381/1491/9715 18181/1454/5161 18189/1448/5622 18382/1489/5164 -f 18379/1488/5161 18381/1491/9715 18382/1489/5164 18384/1485/5164 -f 15225/792/5164 18386/1483/5164 18387/1484/9713 15226/795/5165 -f 15222/796/5161 18378/1487/5161 18386/1483/5164 15225/792/5164 -f 18386/1483/5164 18384/1485/5164 18385/1486/9713 18387/1484/9713 -f 18378/1487/5161 18379/1488/5161 18384/1485/5164 18386/1483/5164 -f 18388/1509/5625 15234/819/5176 15226/795/5165 18387/1484/9713 -f 18389/1508/5625 18388/1509/5625 18387/1484/9713 18385/1486/9713 -f 18390/1502/5624 15236/816/5178 15234/819/5176 18388/1509/5625 -f 18391/1503/5624 18390/1502/5624 18388/1509/5625 18389/1508/5625 -f 18194/1481/5176 18392/1507/5176 18383/1490/5165 18192/1449/9713 -f 18197/1474/5178 18393/1506/5178 18392/1507/5176 18194/1481/5176 -f 18392/1507/5176 18389/1508/5625 18385/1486/9713 18383/1490/5165 -f 18393/1506/5178 18391/1503/5624 18389/1508/5625 18392/1507/5176 -f 18394/1504/5623 18203/1468/5180 18071/1469/138 18302/1505/139 -f 18395/1500/5180 18394/1504/5623 18302/1505/139 18298/1501/138 -f 18393/1506/5178 18197/1474/5178 18203/1468/5180 18394/1504/5623 -f 18391/1503/5624 18393/1506/5178 18394/1504/5623 18395/1500/5180 -f 15238/812/5623 18396/1498/5623 18296/1499/139 7319/815/138 -f 15236/816/5178 18390/1502/5624 18396/1498/5623 15238/812/5623 -f 18396/1498/5623 18395/1500/5180 18298/1501/138 18296/1499/139 -f 18390/1502/5624 18391/1503/5624 18395/1500/5180 18396/1498/5623 -# 18400 polygons - diff --git a/safe_control_gym/envs/assets/gate.urdf b/safe_control_gym/envs/assets/gate.urdf index d531445a8..f2213b7f6 100644 --- a/safe_control_gym/envs/assets/gate.urdf +++ b/safe_control_gym/envs/assets/gate.urdf @@ -3,30 +3,164 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - - + + - + + + + + + + + + - \ No newline at end of file diff --git a/safe_control_gym/envs/assets/low_portal.urdf b/safe_control_gym/envs/assets/low_portal.urdf deleted file mode 100644 index 38c937a03..000000000 --- a/safe_control_gym/envs/assets/low_portal.urdf +++ /dev/null @@ -1,170 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/safe_control_gym/envs/assets/obstacle.urdf b/safe_control_gym/envs/assets/obstacle.urdf index f63a9e49d..8722afaea 100644 --- a/safe_control_gym/envs/assets/obstacle.urdf +++ b/safe_control_gym/envs/assets/obstacle.urdf @@ -1,13 +1,30 @@ - + - + + + + + + + + + + + + + + + + + + - + @@ -26,34 +43,43 @@ - - + + - + - - + + + - + + - + + + + + + + + diff --git a/safe_control_gym/envs/assets/portal.urdf b/safe_control_gym/envs/assets/portal.urdf deleted file mode 100644 index 4ff52392e..000000000 --- a/safe_control_gym/envs/assets/portal.urdf +++ /dev/null @@ -1,171 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/safe_control_gym/envs/assets/sphere.urdf b/safe_control_gym/envs/assets/sphere.urdf deleted file mode 100644 index 121064fc7..000000000 --- a/safe_control_gym/envs/assets/sphere.urdf +++ /dev/null @@ -1,24 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/safe_control_gym/envs/base_aviary.py b/safe_control_gym/envs/base_aviary.py deleted file mode 100644 index bd96ad2f1..000000000 --- a/safe_control_gym/envs/base_aviary.py +++ /dev/null @@ -1,620 +0,0 @@ -"""BaseAviary module. - -This module contains the superclass of the Quadrotor environment, BaseAviary. -BaseAviary implements most of the integration with PyBullet. - -The module also contains enumerations for drone models, PyBullet physics updates -image types captured by PyBullet's camera rendering. -""" - -import os -import time -from pathlib import Path -import logging - -import pkgutil -import xml.etree.ElementTree as etxml -from enum import Enum -import numpy as np -import pybullet as p -import pybullet_data - -from safe_control_gym.envs.drone_sim import DroneSim - -logger = logging.getLogger(__name__) -egl = pkgutil.get_loader("eglRenderer") - - -class DroneModel(str, Enum): - """Drone models enumeration class.""" - - CF2X = "cf2x" # Bitcraze Craziflie 2.0 in the X configuration. - - -class Physics(str, Enum): - """Physics implementations enumeration class.""" - - PYB = "pyb" # Base PyBullet physics update. - DYN = "dyn" # Update with an explicit model of the dynamics. - PYB_GND = "pyb_gnd" # PyBullet physics update with ground effect. - PYB_DRAG = "pyb_drag" # PyBullet physics update with drag. - PYB_DW = "pyb_dw" # PyBullet physics update with downwash. - PYB_GND_DRAG_DW = ( - "pyb_gnd_drag_dw" # PyBullet physics update with ground effect, drag, and downwash. - ) - - -class BaseAviary(DroneSim): - """Base class for "drone aviary" Gym environments.""" - - NAME = "base_aviary" - URDF_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets") - - def __init__( - self, - drone_model: DroneModel = DroneModel.CF2X, - n_drones: int = 1, - physics: Physics = Physics.PYB, - record=False, - gui=False, - verbose=False, - camera_view=[5, -40, -55, 1.5, 2, 0.5], - **kwargs, - ): - """Initialization of a generic aviary environment. - - Args: - drone_model (DroneModel, optional): The desired drone type (detailed in an .urdf file - in folder `assets`). - n_drones: The desired number of drones in the simulation. - physics (Physics, optional): The desired implementation of PyBullet physics/custom - dynamics. - record (bool, optional): Whether to save a video of the simulation in folder - `files/videos/`. - gui (bool, optional): Whether to use PyBullet's GUI. - verbose (bool, optional): If to suppress environment print statetments. - - """ - self._recording_id = None - # Constants. - self.GRAVITY_ACC = 9.8 - self.RAD2DEG = 180 / np.pi - self.DEG2RAD = np.pi / 180 - # Parameters. - self.DRONE_MODEL = DroneModel(drone_model) - self.URDF_PATH = os.path.join(self.URDF_DIR, self.DRONE_MODEL.value + ".urdf") - self.n_drones = n_drones - self.PHYSICS = Physics(physics) - self.RECORD = record - # Load the drone properties from the .urdf file. - ( - self.MASS, - self.L, - self.THRUST2WEIGHT_RATIO, - self.J, - self.J_INV, - self.KF, - self.KM, - self.COLLISION_H, - self.COLLISION_R, - self.COLLISION_Z_OFFSET, - self.MAX_SPEED_KMH, - self.GND_EFF_COEFF, - self.PROP_RADIUS, - self.DRAG_COEFF, - self.DW_COEFF_1, - self.DW_COEFF_2, - self.DW_COEFF_3, - self.PWM2RPM_SCALE, - self.PWM2RPM_CONST, - self.MIN_PWM, - self.MAX_PWM, - ) = self._parse_urdf_parameters(self.URDF_PATH) - self.GROUND_PLANE_Z = 0 - # Compute constants. - self.GRAVITY = self.GRAVITY_ACC * self.MASS - self.HOVER_RPM = np.sqrt(self.GRAVITY / (4 * self.KF)) - self.MAX_RPM = np.sqrt((self.THRUST2WEIGHT_RATIO * self.GRAVITY) / (4 * self.KF)) - self.MAX_THRUST = 4 * self.KF * self.MAX_RPM**2 - self.MAX_XY_TORQUE = self.L * self.KF * self.MAX_RPM**2 - self.MAX_Z_TORQUE = 2 * self.KM * self.MAX_RPM**2 - self.GND_EFF_H_CLIP = ( - 0.25 - * self.PROP_RADIUS - * np.sqrt((15 * self.MAX_RPM**2 * self.KF * self.GND_EFF_COEFF) / self.MAX_THRUST) - ) - # BenchmarkEnv constructor. - - super().__init__( - seed=kwargs["seed"], - gui=gui, - sim_freq=kwargs["sim_freq"], - ctrl_freq=kwargs["ctrl_freq"], - constraints=kwargs["constraints"], - disturbances=kwargs["disturbances"], - reseed=kwargs["reseed"], - ) - # Connect to PyBullet. - self.PYB_CLIENT = -1 - if gui: - # With debug GUI. - self.PYB_CLIENT = p.connect(p.GUI) # p.connect(p.GUI, options="--opengl2") - p.resetDebugVisualizerCamera( - cameraDistance=camera_view[0], - cameraYaw=camera_view[1], - cameraPitch=camera_view[2], - cameraTargetPosition=[camera_view[3], camera_view[4], camera_view[5]], - physicsClientId=self.PYB_CLIENT, - ) - ret = p.getDebugVisualizerCamera(physicsClientId=self.PYB_CLIENT) - if verbose: - print("viewMatrix", ret[2]) - print("projectionMatrix", ret[3]) - else: - # Without debug GUI. - self.PYB_CLIENT = p.connect(p.DIRECT) - # Uncomment the following line to use EGL Render Plugin # - # Instead of TinyRender (CPU-based) in PYB's Direct mode - # if platform == "linux": - # p.setAdditionalSearchPath(pybullet_data.getDataPath()) - # plugin = p.loadPlugin(egl.get_filename(), "_eglRendererPlugin") - # print("plugin=", plugin) - self.RENDER_WIDTH = int(640) - self.RENDER_HEIGHT = int(480) - self.FRAME_PER_SEC = 24 - self.CAPTURE_FREQ = int(self.sim_settings.sim_freq / self.FRAME_PER_SEC) - self.CAM_VIEW = p.computeViewMatrixFromYawPitchRoll( - distance=3, - yaw=-30, - pitch=-30, - roll=0, - cameraTargetPosition=[0, 0, 0], - upAxisIndex=2, - physicsClientId=self.PYB_CLIENT, - ) - self.CAM_PRO = p.computeProjectionMatrixFOV( - fov=60.0, aspect=self.RENDER_WIDTH / self.RENDER_HEIGHT, nearVal=0.1, farVal=1000.0 - ) - # Set default initial poses when loading drone's urdf model. - # can be overriden later for specific tasks (as sub-classes) in reset() - self.INIT_XYZS = ( - np.vstack( - [ - np.array([x * 4 * self.L for x in range(self.n_drones)]), - np.array([y * 4 * self.L for y in range(self.n_drones)]), - np.ones(self.n_drones) * (self.COLLISION_H / 2 - self.COLLISION_Z_OFFSET), - ] - ) - .transpose() - .reshape(self.n_drones, 3) - ) - self.INIT_RPYS = np.zeros((self.n_drones, 3)) - - def close(self): - """Terminates the environment.""" - if self._recording_id is not None: - p.stopStateLogging(self._recording_id, physicsClientId=self.PYB_CLIENT) - if self.PYB_CLIENT >= 0: - p.disconnect(physicsClientId=self.PYB_CLIENT) - self.PYB_CLIENT = -1 - - def _reset_simulation(self): - """Housekeeping function. - - Allocation and zero-ing of the variables and PyBullet's parameters/objects - in the `reset()` function. - - """ - # Initialize/reset counters and zero-valued variables. - self.RESET_TIME = time.time() - self.first_render_call = True - self.X_AX = -1 * np.ones(self.n_drones) - self.Y_AX = -1 * np.ones(self.n_drones) - self.Z_AX = -1 * np.ones(self.n_drones) - self.GUI_INPUT_TEXT = -1 * np.ones(self.n_drones) - self.USE_GUI_RPM = False - self.last_input_switch = 0 - self.last_clipped_action = np.zeros((self.n_drones, 4)) - self.gui_input = np.zeros(4) - # Initialize the drones kinematic information. - self.pos = np.zeros((self.n_drones, 3)) - self.quat = np.zeros((self.n_drones, 4)) - self.rpy = np.zeros((self.n_drones, 3)) - self.vel = np.zeros((self.n_drones, 3)) - self.ang_v = np.zeros((self.n_drones, 3)) - if self.PHYSICS == Physics.DYN: - self.rpy_rates = np.zeros((self.n_drones, 3)) - # Set PyBullet's parameters. - p.resetSimulation(physicsClientId=self.PYB_CLIENT) - p.setGravity(0, 0, -self.GRAVITY_ACC, physicsClientId=self.PYB_CLIENT) - p.setRealTimeSimulation(0, physicsClientId=self.PYB_CLIENT) - p.setTimeStep(1 / self.sim_settings.sim_freq, physicsClientId=self.PYB_CLIENT) - p.setAdditionalSearchPath(pybullet_data.getDataPath(), physicsClientId=self.PYB_CLIENT) - # Load ground plane, drone and obstacles models. - self.PLANE_ID = p.loadURDF( - "plane.urdf", [0, 0, self.GROUND_PLANE_Z], physicsClientId=self.PYB_CLIENT - ) - self.DRONE_IDS = np.array( - [ - p.loadURDF( - self.URDF_PATH, - self.INIT_XYZS[i, :], - p.getQuaternionFromEuler(self.INIT_RPYS[i, :]), - flags=p.URDF_USE_INERTIA_FROM_FILE, # Use URDF inertia tensor. - physicsClientId=self.PYB_CLIENT, - ) - for i in range(self.n_drones) - ] - ) - # Remove default damping. - for i in range(self.n_drones): - p.changeDynamics(self.DRONE_IDS[i], -1, linearDamping=0, angularDamping=0) - # Update and store the drones kinematic information. - self._update_and_store_kinematic_information() - - def _advance_simulation(self, clipped_action, disturbance_force=None): - """Advances the environment by one simulation step. - - Args: - clipped_action (ndarray): The input action for one or more drones, - as RPMs by the specific implementation of - `_preprocess_action()` in each subclass. - disturbance_force (ndarray, optional): Disturbance force, applied to all drones. - - """ - clipped_action = np.reshape(clipped_action, (self.n_drones, 4)) - # Repeat for as many as the aggregate physics steps. - for _ in range(self.sim_settings.sim_freq // self.sim_settings.ctrl_freq): - # Update and store the drones kinematic info for certain - # Between aggregate steps for certain types of update. - if self.sim_settings.sim_freq // self.sim_settings.ctrl_freq > 1 and self.PHYSICS in [ - Physics.DYN, - Physics.PYB_GND, - Physics.PYB_DRAG, - Physics.PYB_DW, - Physics.PYB_GND_DRAG_DW, - ]: - self._update_and_store_kinematic_information() - # Step the simulation using the desired physics update. - for i in range(self.n_drones): - if self.PHYSICS == Physics.PYB: - self._physics(clipped_action[i, :], i) - elif self.PHYSICS == Physics.DYN: - self._dynamics(clipped_action[i, :], i) - elif self.PHYSICS == Physics.PYB_GND: - self._physics(clipped_action[i, :], i) - self._ground_effect(clipped_action[i, :], i) - elif self.PHYSICS == Physics.PYB_DRAG: - self._physics(clipped_action[i, :], i) - self._drag(self.last_clipped_action[i, :], i) - elif self.PHYSICS == Physics.PYB_DW: - self._physics(clipped_action[i, :], i) - self._downwash(i) - elif self.PHYSICS == Physics.PYB_GND_DRAG_DW: - self._physics(clipped_action[i, :], i) - self._ground_effect(clipped_action[i, :], i) - self._drag(self.last_clipped_action[i, :], i) - self._downwash(i) - # Apply disturbance - if disturbance_force is not None: - pos = self._get_drone_state_vector(i)[:3] - p.applyExternalForce( - self.DRONE_IDS[i], - linkIndex=4, # Link attached to the quadrotor's center of mass. - forceObj=disturbance_force, - posObj=pos, - flags=p.WORLD_FRAME, - physicsClientId=self.PYB_CLIENT, - ) - # PyBullet computes the new state, unless Physics.DYN. - if self.PHYSICS != Physics.DYN: - p.stepSimulation(physicsClientId=self.PYB_CLIENT) - # Save the last applied action (e.g. to compute drag). - self.last_clipped_action = clipped_action - # Update and store the drones kinematic information. - self._update_and_store_kinematic_information() - - def render(self, mode="human", close=False): - """Prints a textual output of the environment. - - Args: - mode (str, optional): Unused. - close (bool, optional): Unused. - - """ - - def _update_and_store_kinematic_information(self): - """Updates and stores the drones kinematic information. - - This method is meant to limit the number of calls to PyBullet in each step - and improve performance (at the expense of memory). - - """ - for i in range(self.n_drones): - self.pos[i], self.quat[i] = p.getBasePositionAndOrientation( - self.DRONE_IDS[i], physicsClientId=self.PYB_CLIENT - ) - self.rpy[i] = p.getEulerFromQuaternion(self.quat[i]) - self.vel[i], self.ang_v[i] = p.getBaseVelocity( - self.DRONE_IDS[i], physicsClientId=self.PYB_CLIENT - ) - - def record(self, path: Path): - """Start the recording of a video output. - - The format of the video output is .mp4, if GUI is True, or .png, otherwise. - The video is saved under folder `files/videos`. - - """ - if not self.sim_settings.gui: - logger.warning("Cannot record video without GUI.") - return - self._recording_id = p.startStateLogging( - loggingType=p.STATE_LOGGING_VIDEO_MP4, - fileName=str(path.absolute()), - physicsClientId=self.PYB_CLIENT, - ) - - def _get_drone_state_vector(self, nth_drone): - """Returns the state vector of the n-th drone. - - Args: - nth_drone (int): The ordinal number/position of the desired drone in list self.DRONE_IDS. - - Returns: - ndarray. (20,)-shaped array of floats containing the state vector of the n-th drone. - Check the only line in this method and `_update_and_store_kinematic_information()` - to understand its format. - - """ - state = np.hstack( - [ - self.pos[nth_drone, :], - self.quat[nth_drone, :], - self.rpy[nth_drone, :], - self.vel[nth_drone, :], - self.ang_v[nth_drone, :], - self.last_clipped_action[nth_drone, :], - ] - ) - return state.reshape( - 20, - ) - - def _physics(self, rpm, nth_drone): - """Base PyBullet physics implementation. - - Args: - rpm (ndarray): (4)-shaped array of ints containing the RPMs values of the 4 motors. - nth_drone (int): The ordinal number/position of the desired drone in list self.DRONE_IDS. - - """ - forces = np.array(rpm**2) * self.KF - torques = np.array(rpm**2) * self.KM - # z_torque = (-torques[0] + torques[1] - torques[2] + torques[3]) - z_torque = torques[0] - torques[1] + torques[2] - torques[3] - for i in range(4): - p.applyExternalForce( - self.DRONE_IDS[nth_drone], - i, - forceObj=[0, 0, forces[i]], - posObj=[0, 0, 0], - flags=p.LINK_FRAME, - physicsClientId=self.PYB_CLIENT, - ) - p.applyExternalTorque( - self.DRONE_IDS[nth_drone], - 4, - torqueObj=[0, 0, z_torque], - flags=p.LINK_FRAME, - physicsClientId=self.PYB_CLIENT, - ) - - def _ground_effect(self, rpm, nth_drone): - """PyBullet implementation of a ground effect model. - - Inspired by the analytical model used for comparison in (Shi et al., 2019). - - Args: - rpm (ndarray): (4)-shaped array of ints containing the RPMs values of the 4 motors. - nth_drone (int): The ordinal number/position of the desired drone in list self.DRONE_IDS. - - """ - # Kin. info of all links (propellers and center of mass) - s = p.getLinkStates( - self.DRONE_IDS[nth_drone], - linkIndices=[0, 1, 2, 3], - computeLinkVelocity=1, - computeForwardKinematics=1, - physicsClientId=self.PYB_CLIENT, - ) - prop_heights = np.array([s[0][0][2], s[1][0][2], s[2][0][2], s[3][0][2]]) - # Simple, per-propeller ground effects. - prop_heights = np.clip(prop_heights, self.GND_EFF_H_CLIP, np.inf) - gnd_effects = ( - np.array(rpm**2) - * self.KF - * self.GND_EFF_COEFF - * (self.PROP_RADIUS / (4 * prop_heights)) ** 2 - ) - if ( - np.abs(self.rpy[nth_drone, 0]) < np.pi / 2 - and np.abs(self.rpy[nth_drone, 1]) < np.pi / 2 - ): - for i in range(4): - p.applyExternalForce( - self.DRONE_IDS[nth_drone], - i, - forceObj=[0, 0, gnd_effects[i]], - posObj=[0, 0, 0], - flags=p.LINK_FRAME, - physicsClientId=self.PYB_CLIENT, - ) - # TODO: a more realistic model accounting for the drone's - # Attitude and its z-axis velocity in the world frame. - - def _drag(self, rpm, nth_drone): - """PyBullet implementation of a drag model. - - Based on the the system identification in (Forster, 2015). - - Args: - rpm (ndarray): (4)-shaped array of ints containing the RPMs values of the 4 motors. - nth_drone (int): The ordinal number/position of the desired drone in list self.DRONE_IDS. - - """ - # Rotation matrix of the base. - base_rot = np.array(p.getMatrixFromQuaternion(self.quat[nth_drone, :])).reshape(3, 3) - # Simple draft model applied to the base/center of mass # - drag_factors = -1 * self.DRAG_COEFF * np.sum(np.array(2 * np.pi * rpm / 60)) - drag = np.dot(base_rot, drag_factors * np.array(self.vel[nth_drone, :])) - p.applyExternalForce( - self.DRONE_IDS[nth_drone], - 4, - forceObj=drag, - posObj=[0, 0, 0], - flags=p.LINK_FRAME, - physicsClientId=self.PYB_CLIENT, - ) - - def _downwash(self, nth_drone): - """PyBullet implementation of a ground effect model. - - Based on experiments conducted at the Dynamic Systems Lab by SiQi Zhou. - - Args: - nth_drone (int): The ordinal number/position of the desired drone in list self.DRONE_IDS. - - """ - for i in range(self.n_drones): - delta_z = self.pos[i, 2] - self.pos[nth_drone, 2] - delta_xy = np.linalg.norm( - np.array(self.pos[i, 0:2]) - np.array(self.pos[nth_drone, 0:2]) - ) - if delta_z > 0 and delta_xy < 10: # Ignore drones more than 10 meters away - alpha = self.DW_COEFF_1 * (self.PROP_RADIUS / (4 * delta_z)) ** 2 - beta = self.DW_COEFF_2 * delta_z + self.DW_COEFF_3 - downwash = [0, 0, -alpha * np.exp(-0.5 * (delta_xy / beta) ** 2)] - p.applyExternalForce( - self.DRONE_IDS[nth_drone], - 4, - forceObj=downwash, - posObj=[0, 0, 0], - flags=p.LINK_FRAME, - physicsClientId=self.PYB_CLIENT, - ) - - def _dynamics(self, rpm, nth_drone): - """Explicit dynamics implementation. - - Based on code written at the Dynamic Systems Lab by James Xu. - - Args: - rpm (ndarray): (4)-shaped array of ints containing the RPMs values of the 4 motors. - nth_drone (int): The ordinal number/position of the desired drone in list self.DRONE_IDS. - - """ - # Current state. - pos = self.pos[nth_drone, :] - quat = self.quat[nth_drone, :] - rpy = self.rpy[nth_drone, :] - vel = self.vel[nth_drone, :] - rpy_rates = self.rpy_rates[nth_drone, :] - rotation = np.array(p.getMatrixFromQuaternion(quat)).reshape(3, 3) - # Compute forces and torques. - forces = np.array(rpm**2) * self.KF - thrust = np.array([0, 0, np.sum(forces)]) - thrust_world_frame = np.dot(rotation, thrust) - force_world_frame = thrust_world_frame - np.array([0, 0, self.GRAVITY]) - z_torques = np.array(rpm**2) * self.KM - # z_torque = (-z_torques[0] + z_torques[1] - z_torques[2] + z_torques[3]) - z_torque = z_torques[0] - z_torques[1] + z_torques[2] - z_torques[3] - if self.DRONE_MODEL == DroneModel.CF2X: - x_torque = (forces[0] + forces[1] - forces[2] - forces[3]) * (self.L / np.sqrt(2)) - y_torque = (-forces[0] + forces[1] + forces[2] - forces[3]) * (self.L / np.sqrt(2)) - elif self.DRONE_MODEL == DroneModel.CF2P: - x_torque = (forces[1] - forces[3]) * self.L - y_torque = (-forces[0] + forces[2]) * self.L - torques = np.array([x_torque, y_torque, z_torque]) - torques = torques - np.cross(rpy_rates, np.dot(self.J, rpy_rates)) - rpy_rates_deriv = np.dot(self.J_INV, torques) - no_pybullet_dyn_accs = force_world_frame / self.MASS - # Update state. - vel = vel + no_pybullet_dyn_accs / self.sim_settings.sim_freq - rpy_rates = rpy_rates + rpy_rates_deriv / self.sim_settings.sim_freq - pos = pos + vel / self.sim_settings.sim_freq - rpy = rpy + rpy_rates / self.sim_settings.sim_freq - # Set PyBullet's state. - p.resetBasePositionAndOrientation( - self.DRONE_IDS[nth_drone], - pos, - p.getQuaternionFromEuler(rpy), - physicsClientId=self.PYB_CLIENT, - ) - # Note: the base's velocity only stored and not used # - p.resetBaseVelocity( - self.DRONE_IDS[nth_drone], - vel, - rpy_rates, # ang_vel not computed by DYN - physicsClientId=self.PYB_CLIENT, - ) - # Store the roll, pitch, yaw rates for the next step # - self.rpy_rates[nth_drone, :] = rpy_rates - - def _parse_urdf_parameters(self, file_name): - """Loads parameters from an URDF file. - - This method is nothing more than a custom XML parser for the .urdf - files in folder `assets/`. - """ - URDF_TREE = etxml.parse(file_name).getroot() - M = float(URDF_TREE[1][0][1].attrib["value"]) - L = float(URDF_TREE[0].attrib["arm"]) - THRUST2WEIGHT_RATIO = float(URDF_TREE[0].attrib["thrust2weight"]) - IXX = float(URDF_TREE[1][0][2].attrib["ixx"]) - IYY = float(URDF_TREE[1][0][2].attrib["iyy"]) - IZZ = float(URDF_TREE[1][0][2].attrib["izz"]) - J = np.diag([IXX, IYY, IZZ]) - J_INV = np.linalg.inv(J) - KF = float(URDF_TREE[0].attrib["kf"]) - KM = float(URDF_TREE[0].attrib["km"]) - COLLISION_H = float(URDF_TREE[1][2][1][0].attrib["length"]) - COLLISION_R = float(URDF_TREE[1][2][1][0].attrib["radius"]) - COLLISION_SHAPE_OFFSETS = [float(s) for s in URDF_TREE[1][2][0].attrib["xyz"].split(" ")] - COLLISION_Z_OFFSET = COLLISION_SHAPE_OFFSETS[2] - MAX_SPEED_KMH = float(URDF_TREE[0].attrib["max_speed_kmh"]) - GND_EFF_COEFF = float(URDF_TREE[0].attrib["gnd_eff_coeff"]) - PROP_RADIUS = float(URDF_TREE[0].attrib["prop_radius"]) - DRAG_COEFF_XY = float(URDF_TREE[0].attrib["drag_coeff_xy"]) - DRAG_COEFF_Z = float(URDF_TREE[0].attrib["drag_coeff_z"]) - DRAG_COEFF = np.array([DRAG_COEFF_XY, DRAG_COEFF_XY, DRAG_COEFF_Z]) - DW_COEFF_1 = float(URDF_TREE[0].attrib["dw_coeff_1"]) - DW_COEFF_2 = float(URDF_TREE[0].attrib["dw_coeff_2"]) - DW_COEFF_3 = float(URDF_TREE[0].attrib["dw_coeff_3"]) - PWM2RPM_SCALE = float(URDF_TREE[0].attrib["pwm2rpm_scale"]) - PWM2RPM_CONST = float(URDF_TREE[0].attrib["pwm2rpm_const"]) - MIN_PWM = float(URDF_TREE[0].attrib["pwm_min"]) - MAX_PWM = float(URDF_TREE[0].attrib["pwm_max"]) - return ( - M, - L, - THRUST2WEIGHT_RATIO, - J, - J_INV, - KF, - KM, - COLLISION_H, - COLLISION_R, - COLLISION_Z_OFFSET, - MAX_SPEED_KMH, - GND_EFF_COEFF, - PROP_RADIUS, - DRAG_COEFF, - DW_COEFF_1, - DW_COEFF_2, - DW_COEFF_3, - PWM2RPM_SCALE, - PWM2RPM_CONST, - MIN_PWM, - MAX_PWM, - ) diff --git a/safe_control_gym/envs/constants.py b/safe_control_gym/envs/constants.py index 94cb6213a..fec325e24 100644 --- a/safe_control_gym/envs/constants.py +++ b/safe_control_gym/envs/constants.py @@ -4,10 +4,6 @@ RAD_TO_DEG = 180 / math.pi -@dataclass -class SimConstants: ... - - @dataclass class DroneConstants: firmware_freq: int = 500 # Firmware frequency in Hz diff --git a/safe_control_gym/envs/constraints.py b/safe_control_gym/envs/constraints.py index 9d5e72245..e5e1e86d1 100644 --- a/safe_control_gym/envs/constraints.py +++ b/safe_control_gym/envs/constraints.py @@ -77,7 +77,7 @@ def __init__( def reset(self): """Clears up the constraint state (if any).""" - def symbolic_model(self): + def symbolic(self): """Create the symbolic form of the constraint function.""" raise NotImplementedError @@ -203,7 +203,7 @@ def __init__( ) self.check_tolerance_shape() - def symbolic_model(self): + def symbolic(self): """Gets the symbolic form of the constraint function. Returns: @@ -257,7 +257,7 @@ def __init__( self.sym_func = lambda x: self.A @ self.constraint_filter @ x - self.b self.check_tolerance_shape() - def symbolic_model(self): + def symbolic(self): """Gets the symbolic form of the constraint function. Returns: @@ -394,16 +394,14 @@ def __len__(self) -> int: """Get the constraint list length.""" return len(self.constraints) - def symbolic_model( - self, state_models: bool = True, input_models: bool = True - ) -> list[Callable]: + def symbolic(self, state_models: bool = True, input_models: bool = True) -> list[Callable]: """Return all the symbolic models the constraints.""" assert state_models or input_models, "Select at least one of state_models or input_models." if state_models and input_models: - return [con.symbolic_model() for con in self.constraints] + return [con.symbolic() for con in self.constraints] if state_models: - return [con.symbolic_model() for con in self.state_constraints] - return [con.symbolic_model() for con in self.input_constraints] + return [con.symbolic() for con in self.state_constraints] + return [con.symbolic() for con in self.input_constraints] def value( self, @@ -464,6 +462,6 @@ def from_specs( assert isinstance(constraint, dict), "Each constraint must be specified as a dict." assert "type" in constraint.keys(), "Each constraint must have a 'type' key" c_class = getattr(sys.modules[__name__], constraint["type"]) - kwargs = {key: constraint[key] for key in constraint if key != "type"} + kwargs = {k: v for k, v in constraint.items() if k != "type"} constraint_list.append(c_class(state_space, action_space, **kwargs)) return ConstraintList(constraint_list) diff --git a/safe_control_gym/envs/disturbances.py b/safe_control_gym/envs/disturbances.py index 79f0ffb17..ed7be19ce 100644 --- a/safe_control_gym/envs/disturbances.py +++ b/safe_control_gym/envs/disturbances.py @@ -1,255 +1,120 @@ """Disturbances.""" +from __future__ import annotations + +import sys + import numpy as np +import numpy.typing as npt class Disturbance: """Base class for disturbance or noise applied to inputs or dyanmics.""" - def __init__(self, env, dim, mask=None, **kwargs): + def __init__(self, dim: int, mask: npt.NDArray[np.bool_] | None = None): self.dim = dim self.mask = mask + self.np_random = np.random.default_rng() if mask is not None: self.mask = np.asarray(mask) assert self.dim == len(self.mask) + self.step = 0 - def reset(self, env): - pass + def reset(self): + self.step = 0 - def apply(self, target, env): + def step(self): + self.step += 1 + + def apply(self, target: npt.NDArray[np.float_]) -> npt.NDArray[np.float_]: """Default is identity.""" return target - def seed(self, env): - """Reset seed from env.""" - self.np_random = env.np_random - - -class DisturbanceList: - """Combine list of disturbances as one.""" - - def __init__(self, disturbances): - """Initialization of the list of disturbances.""" - self.disturbances = disturbances - - def reset(self, env): - """Sequentially reset disturbances.""" - for disturb in self.disturbances: - disturb.reset(env) - - def apply(self, target, env): - """Sequentially apply disturbances.""" - disturbed = target - for disturb in self.disturbances: - disturbed = disturb.apply(disturbed, env) - return disturbed - - def seed(self, env): + def seed(self, seed: int | None = None): """Reset seed from env.""" - for disturb in self.disturbances: - disturb.seed(env) - + self.np_random = np.random.default_rng(seed) -class ImpulseDisturbance(Disturbance): - """Impulse applied during a short time interval. - Examples: - * single step, square (duration=1, decay_rate=1): ______|-|_______ - * multiple step, square (duration>1, decay_rate=1): ______|-----|_____ - * multiple step, triangle (duration>1, decay_rate<1): ______/\_____ - - """ +class UniformNoise(Disturbance): + """i.i.d uniform noise ~ U(low, high) per time step.""" def __init__( - self, env, dim, mask=None, magnitude=1, step_offset=None, duration=1, decay_rate=1, **kwargs + self, + dim: int, + mask: npt.NDArray[np.bool_] | None = None, + low: float = 0.0, + high: float = 1.0, ): - super().__init__(env, dim, mask) - self.magnitude = magnitude - self.step_offset = step_offset - self.max_step = int(env.EPISODE_LEN_SEC / env.CTRL_TIMESTEP) - # Specify shape of the impulse. - assert duration >= 1 - assert decay_rate > 0 and decay_rate <= 1 - self.duration = duration - self.decay_rate = decay_rate - - def reset(self, env): - if self.step_offset is None: - self.current_step_offset = self.np_random.randint(self.max_step) - else: - self.current_step_offset = self.step_offset - self.current_peak_step = int(self.current_step_offset + self.duration / 2) - - def apply(self, target, env): - noise = 0 - if env.ctrl_step_counter >= self.current_step_offset: - peak_offset = np.abs(env.ctrl_step_counter - self.current_peak_step) - if peak_offset < self.duration / 2: - decay = self.decay_rate**peak_offset - else: - decay = 0 - noise = self.magnitude * decay - if self.mask is not None: - noise *= self.mask - disturbed = target + noise - return disturbed - - -class StepDisturbance(Disturbance): - """Constant disturbance at all time steps (but after offset). - - Applied after offset step (randomized or given): _______|--------- - - """ + super().__init__(dim, mask) + assert isinstance(low, (float, list, np.ndarray)), "low must be float or list." + assert isinstance(high, (float, list, np.ndarray)), "high must be float or list." + self.low = np.array([low] * self.dim) if isinstance(low, float) else np.asarray(low) + self.high = np.array([high] * self.dim) if isinstance(high, float) else np.asarray(high) - def __init__(self, env, dim, mask=None, magnitude=1, step_offset=None, **kwargs): - super().__init__(env, dim, mask) - self.magnitude = magnitude - self.step_offset = step_offset - self.max_step = int(env.EPISODE_LEN_SEC / env.CTRL_TIMESTEP) - - def reset(self, env): - if self.step_offset is None: - self.current_step_offset = self.np_random.randint(self.max_step) - else: - self.current_step_offset = self.step_offset - - def apply(self, target, env): - noise = 0 - if env.ctrl_step_counter >= self.current_step_offset: - noise = self.magnitude - if self.mask is not None: - noise *= self.mask - disturbed = target + noise - return disturbed - - -class UniformNoise(Disturbance): - """i.i.d uniform noise ~ U(low, high) per time step.""" - - def __init__(self, env, dim, mask=None, low=0.0, high=1.0, **kwargs): - super().__init__(env, dim, mask) - - # uniform distribution bounds - if isinstance(low, float): - self.low = np.asarray([low] * self.dim) - elif isinstance(low, list): - self.low = np.asarray(low) - else: - raise ValueError( - "[ERROR] UniformNoise.__init__(): low must be specified as a float or list." - ) - - if isinstance(high, float): - self.high = np.asarray([high] * self.dim) - elif isinstance(low, list): - self.high = np.asarray(high) - else: - raise ValueError( - "[ERROR] UniformNoise.__init__(): high must be specified as a float or list." - ) - - def apply(self, target, env): + def apply(self, target: npt.NDArray[np.float_]) -> npt.NDArray[np.float_]: noise = self.np_random.uniform(self.low, self.high, size=self.dim) if self.mask is not None: noise *= self.mask - disturbed = target + noise - return disturbed + return target + noise class WhiteNoise(Disturbance): """I.i.d Gaussian noise per time step.""" - def __init__(self, env, dim, mask=None, std=1.0, **kwargs): - super().__init__(env, dim, mask) - # I.i.d gaussian variance. - if isinstance(std, float): - self.std = np.asarray([std] * self.dim) - elif isinstance(std, list): - self.std = np.asarray(std) - else: - raise ValueError( - "[ERROR] WhiteNoise.__init__(): std must be specified as a float or list." - ) + def __init__( + self, + dim: int, + mask: npt.NDArray[np.bool_] | None = None, + std: float | npt.NDArray[np.float_] = 1.0, + ): + super().__init__(dim, mask) + assert isinstance(std, (float, list, np.ndarray)), "std must be float or list." + self.std = np.array([std] * self.dim) if isinstance(std, float) else np.asarray(std) assert self.dim == len(self.std), "std shape should be the same as dim." - def apply(self, target, env): + def apply(self, target: npt.NDArray[np.float_]) -> npt.NDArray[np.float_]: noise = self.np_random.normal(0, self.std, size=self.dim) - # # TODO: hack for debug - # noise = np.clip(noise, -self.std, self.std) - if self.mask is not None: noise *= self.mask - disturbed = target + noise - return disturbed - + return target + noise -class BrownianNoise(Disturbance): - """Simple random walk noise.""" - - def __init__(self): - super().__init__() +class DisturbanceList: + """Combine list of disturbances as one.""" -class PeriodicNoise(Disturbance): - """Sinuisodal noise.""" + def __init__(self, disturbances: list[Disturbance]): + """Initialization of the list of disturbances.""" + self.disturbances = disturbances - def __init__(self, env, dim, mask=None, scale=1.0, frequency=1.0, **kwargs): - super().__init__(env, dim) - # Sine function parameters. - self.scale = scale - self.frequency = frequency + def reset(self): + """Sequentially reset disturbances.""" + for disturb in self.disturbances: + disturb.reset() - def apply(self, target, env): - phase = self.np_random.uniform(low=-np.pi, high=np.pi, size=self.dim) - t = env.pyb_step_counter * env.PYB_TIMESTEP - noise = self.scale * np.sin(2 * np.pi * self.frequency * t + phase) - if self.mask is not None: - noise *= self.mask - disturbed = target + noise + def apply(self, target: npt.NDArray[np.float_]) -> npt.NDArray[np.float_]: + """Sequentially apply disturbances.""" + disturbed = target + for disturb in self.disturbances: + disturbed = disturb.apply(disturbed) return disturbed - -class StateDependentDisturbance(Disturbance): - """Time varying and state varying, e.g. friction. - - Here to provide an explicit form, can also enable friction in simulator directly. - - """ - - def __init__(self, env, dim, mask=None, **kwargs): - super().__init__() - - -DISTURBANCE_TYPES = { - "impulse": ImpulseDisturbance, - "step": StepDisturbance, - "uniform": UniformNoise, - "white_noise": WhiteNoise, - "periodic": PeriodicNoise, -} - - -def create_disturbance_list(disturbance_specs, shared_args, env): - """Creates a DisturbanceList from yaml disturbance specification. - - Args: - disturbance_specs (list): List of dicts defining the disturbances info. - shared_args (dict): args shared across the disturbances in the list. - env (BenchmarkEnv): Env for which the constraints will be applied - """ - disturb_list = [] - # Each disturbance for the mode. - for disturb in disturbance_specs: - assert ( - "disturbance_func" in disturb.keys() - ), "[ERROR]: Every distrubance must specify a disturbance_func." - disturb_func = disturb["disturbance_func"] - assert ( - disturb_func in DISTURBANCE_TYPES - ), "[ERROR] in BenchmarkEnv._setup_disturbances(), disturbance type not available." - disturb_cls = DISTURBANCE_TYPES[disturb_func] - cfg = {key: disturb[key] for key in disturb if key != "disturbance_func"} - disturb = disturb_cls(env, **shared_args, **cfg) - disturb_list.append(disturb) - return DisturbanceList(disturb_list) + def seed(self, seed: int | None = None): + """Reset seed from env.""" + for disturb in self.disturbances: + disturb.seed(seed) + + @staticmethod + def from_specs(disturbance_specs: list[dict]) -> DisturbanceList: + """Create a DisturbanceList from a list of disturbance specifications. + + Args: + disturbance_specs: List of dicts defining the disturbances info. + """ + disturb_list = [] + # Each disturbance for the mode. + for disturb in disturbance_specs: + assert isinstance(disturb, dict), "Each disturbance must be specified as dict." + assert "type" in disturb.keys(), "Each distrubance must have a 'type' key." + d_class = getattr(sys.modules[__name__], disturb["type"]) + disturb_list.append(d_class(**{k: v for k, v in disturb.items() if k != "type"})) + return DisturbanceList(disturb_list) diff --git a/safe_control_gym/envs/drone.py b/safe_control_gym/envs/drone.py index 7b8c26cad..552ed2366 100644 --- a/safe_control_gym/envs/drone.py +++ b/safe_control_gym/envs/drone.py @@ -6,7 +6,7 @@ from types import ModuleType from dataclasses import dataclass from pathlib import Path - +import copy from xml.etree import ElementTree import numpy as np @@ -23,6 +23,7 @@ def __init__(self, controller: Literal["pid", "mellinger"]): self.firmware = self._load_firmware() self.firmware_freq = Constants.firmware_freq self.params = DroneParams.from_urdf(Path(__file__).resolve().parent / "assets/cf2x.urdf") + self.nominal_params = copy.deepcopy(self.params) # Store parameters without disturbances # Initialize firmware states self._state = self.firmware.state_t() self._control = self.firmware.control_t() @@ -34,7 +35,9 @@ def __init__(self, controller: Literal["pid", "mellinger"]): assert controller in ["pid", "mellinger"], f"Invalid controller {controller}." self._controller = controller # Helper variables for the controller + self.desired_thrust = np.zeros(4) # Desired thrust for each motor self._pwms = np.zeros(4) # PWM signals for each motor + self.rpm = np.zeros(4) # RPM for each motor self._tick = 0 # Current controller tick self._n_tumble = 0 # Number of consecutive steps the drone is tumbling self._last_att_ctrl_call = 0 # Last time attitude controller was called @@ -43,25 +46,38 @@ def __init__(self, controller: Literal["pid", "mellinger"]): self._last_rpy = np.zeros(3) self._fullstate_cmd = True # Disables high level commander if True + self.init_state = { + "pos": np.zeros(3), + "rpy": np.zeros(3), + "vel": np.zeros(3), + "ang_vel": np.zeros(3), + } + self.state = self.init_state.copy() + self.id = -1 + def reset( self, - pos: npt.NDArray[np.float64] = np.array([0.0, 0.0, 0.0]), - rpy: npt.NDArray[np.float64] = np.array([0.0, 0.0, 0.0]), - vel: npt.NDArray[np.float64] = np.array([0.0, 0.0, 0.0]), + pos: npt.NDArray[np.float64] | None = None, + rpy: npt.NDArray[np.float64] | None = None, + vel: npt.NDArray[np.float64] | None = None, ): """Reset the drone state and controllers. Args: - pos: Initial position. - rpy: Initial roll, pitch, yaw. - vel: Initial velocity. + pos: Initial position of the drone. If None, uses the values from `init_state`. + rpy: Initial roll, pitch, yaw of the drone. If None, uses the values from `init_state`. + vel: Initial velocity of the drone. If None, uses the values from `init_state`. """ + self.params = copy.deepcopy(self.nominal_params) self._reset_firmware_states() self._reset_low_pass_filters() self._reset_helper_variables() self._reset_controller() # Initilaize high level commander self.firmware.crtpCommanderHighLevelInit() + pos = self.init_state["pos"] if pos is None else pos + rpy = self.init_state["rpy"] if rpy is None else rpy + vel = self.init_state["vel"] if vel is None else vel self._update_state(0, pos, rpy * RAD_TO_DEG, vel, np.array([0, 0, 1.0])) self._last_vel[...], self._last_rpy[...] = vel, rpy self.firmware.crtpCommanderHighLevelTellState(self._state) @@ -81,8 +97,7 @@ def step_controller( # Estimate rates rotation_rates = (rpy - self._last_rpy) * Constants.firmware_freq # body coord, rad/s self._last_rpy = rpy - # TODO: Convert to real acc, not multiple of g - acc = (vel - self._last_vel) * Constants.firmware_freq / 9.8 + np.array([0, 0, 1]) + acc = (vel - self._last_vel) * Constants.firmware_freq / 9.81 + np.array([0, 0, 1]) self._last_vel = vel # Update state timestamp = int(self._tick / Constants.firmware_freq * 1e3) @@ -98,7 +113,7 @@ def step_controller( if not success: self._pwms[...] = 0 return np.zeros(4) - return self._pwms_to_action(self._pwms) + return self._pwms_to_thrust(self._pwms) @property def tick(self) -> int: @@ -127,7 +142,7 @@ def _update_state( for name, value in zip(("x", "y", "z"), acc): setattr(self._state.acc, name, value) - def _pwms_to_action(self, pwms: npt.NDArray[np.float64]) -> npt.NDArray[np.float64]: + def _pwms_to_thrust(self, pwms: npt.NDArray[np.float64]) -> npt.NDArray[np.float64]: return self.params.kf * (self.params.pwm2rpm_scale * pwms + self.params.pwm2rpm_const) ** 2 def full_state_cmd( @@ -137,7 +152,6 @@ def full_state_cmd( acc: npt.NDArray[np.float64], yaw: float, rpy_rate: npt.NDArray[np.float64], - timestep: float, ): """Send a full state command to the controller. @@ -150,9 +164,8 @@ def full_state_cmd( acc: [x, y, z] acceleration of the CF (m/s^2) yaw: yaw of the CF (rad) rpy_rate: roll, pitch, yaw rates (rad/s) - timestep: simulation time when command is sent (s) """ - timestep = self._tick / Constants.firmware_freq # TODO: Adopt for all commands, remove arg + timestep = self._tick / Constants.firmware_freq self.firmware.crtpCommanderHighLevelStop() # Resets planner object self.firmware.crtpCommanderHighLevelUpdateTime(timestep) @@ -269,6 +282,8 @@ def _reset_helper_variables(self): self._last_pos_ctrl_call = 0 self._last_vel = np.zeros(3) self._last_rpy = np.zeros(3) + self.rpm[...] = 0 + self.desired_thrust[...] = 0 def _reset_controller(self): if self._controller == "pid": @@ -367,8 +382,6 @@ def _update_setpoint(self, timestep: float): self.firmware.crtpCommanderHighLevelUpdateTime(timestep) self.firmware.crtpCommanderHighLevelGetSetpoint(self._setpoint, self._state) - # region Utils - def _load_firmware(self) -> ModuleType: """Load the firmware module. @@ -381,8 +394,6 @@ def _load_firmware(self) -> ModuleType: spec.loader.exec_module(mod) return mod - # endregion - @dataclass class DroneParams: @@ -409,12 +420,23 @@ class DroneParams: # Defaults are calculated in __post_init__ according to the other parameters min_thrust: float = 0.0 max_thrust: float = 0.0 + min_rpm: float = 0.0 + max_rpm: float = 0.0 + gnd_eff_min_height_clip: float = 0.0 J_inv: npt.NDArray[np.float64] = np.zeros((3, 3)) def __post_init__(self): self.J_inv = np.linalg.inv(self.J) - self.min_thrust = self.kf * (self.pwm2rpm_scale * self.min_pwm + self.pwm2rpm_const) ** 2 - self.max_thrust = self.kf * (self.pwm2rpm_scale * self.max_pwm + self.pwm2rpm_const) ** 2 + self.min_rpm = self.pwm2rpm_scale * self.min_pwm + self.pwm2rpm_const + self.max_rpm = self.pwm2rpm_scale * self.max_pwm + self.pwm2rpm_const + # TODO: Check if this computation is correct. Some use kf, others 4*kf + self.min_thrust = self.kf * self.min_rpm**2 + self.max_thrust = self.kf * self.max_rpm**2 + self.gnd_eff_min_height_clip = ( + 0.25 + * self.prop_radius + * np.sqrt((15 * self.max_rpm**2 * self.kf * self.gnd_eff_coeff) / self.max_thrust) + ) @staticmethod def from_urdf(path: Path) -> DroneParams: diff --git a/safe_control_gym/envs/drone_racing_env.py b/safe_control_gym/envs/drone_racing_env.py new file mode 100644 index 000000000..ff84f3acf --- /dev/null +++ b/safe_control_gym/envs/drone_racing_env.py @@ -0,0 +1,208 @@ +from __future__ import annotations + +import numpy as np +import logging + +import gymnasium +from safe_control_gym.envs.drone_sim import DroneSim +from safe_control_gym.envs.drone import Drone +from scipy.spatial.transform import Rotation as R +import importlib.util +import pybullet as p +import time + +spec = importlib.util.find_spec("pycffirmware") +firm = importlib.util.module_from_spec(spec) +spec.loader.exec_module(firm) +# import pycffirmware as firm + +logger = logging.getLogger(__name__) + + +class DroneRacingEnv(gymnasium.Env): + CONTROLLER = "mellinger" # specifies controller type + + def __init__(self, config: dict): + """Initialize the DroneRacingEnv. + + Args: + config: Configuration dictionary for the environment. + + Attributes: + env: safe-control environment + """ + super().__init__() + self.config = config + self.drone = Drone(self.CONTROLLER) + self.step_freq = config.env.freq + self.sim = DroneSim( + track=config.env.track, + sim_freq=config.sim.sim_freq, + ctrl_freq=config.sim.ctrl_freq, + constraints=config.env.constraints, + disturbances=config.env.disturbances, + gui=config.sim.gui, + n_drones=1, + physics=config.sim.physics, + ) + self.sim.seed(config.env.seed) + self.action_space = gymnasium.spaces.Box(low=-1, high=1, shape=(4,)) + fm = np.finfo(np.float32).max + low = np.array([-5, -fm, -5, -fm, -0.25, -fm, -np.pi, -np.pi, -np.pi, -fm, -fm, -fm]) + high = np.array([5, fm, 5, fm, 2.5, fm, np.pi, np.pi, np.pi, fm, fm, fm]) + self.observation_space = gymnasium.spaces.Box(low=low, high=high, dtype=np.float32) + self.target_gate = 0 + self.symbolic = config.env.symbolic + self._steps = 0 + self._debug_time = 0 # TODO: Remove this + + def reset(self, *, seed: int | None = None, options: dict | None = None): + if self.config.env.reseed: + self.sim.seed(self.config.env.seed) + if seed is not None: + self.sim.seed(seed) + self.sim.reset() + self.target_gate = 0 + self._debug_time = 0 # TODO: Remove this + self._steps = 0 + obs = self.obs + pos = obs["pos"] + rpy = obs["rpy"] + vel = obs["vel"] + ang_vel = obs["ang_vel"] + self.drone.reset(pos, rpy, vel) + obs = np.concatenate( + [np.array([pos[0], vel[0], pos[1], vel[1], pos[2], vel[2]]), rpy, ang_vel] + ) + if self.sim.n_drones > 1: + raise NotImplementedError("Firmware wrapper does not support multiple drones.") + return obs.astype(np.float32), self.info + + def step(self, action: np.ndarray): + """Step the firmware_wrapper class and its environment. + + This function should be called once at the rate of ctrl_freq. Step processes and high level + commands, and runs the firmware loop and simulator according to the frequencies set. + + Args: + action: Full-state command [x_des, y_des, z_des, yaw_des] to follow. + """ + zeros = np.zeros(3, dtype=np.float64) + action = action.astype(np.float64) # Drone firmware expects float64 + pos, yaw = action[:3], action[3] + self.drone.full_state_cmd(pos, zeros, zeros, yaw, zeros) + + thrust = self.drone.desired_thrust + collision = False + while self.drone.tick / self.drone.firmware_freq < (self._steps + 1) / self.step_freq: + self.sim.step(thrust) + t1 = time.perf_counter() + self.check_gate_progress() + t2 = time.perf_counter() + self._debug_time += t2 - t1 + pos = self.sim.drone.state["pos"] + rpy = self.sim.drone.state["rpy"] + vel = self.sim.drone.state["vel"] + ang_vel = self.sim.drone.state["ang_vel"] + + obs = self.obs + pos, vel, rpy, ang_vel = obs["pos"], obs["vel"], obs["rpy"], obs["ang_vel"] + obs = np.concatenate( + [np.array([pos[0], vel[0], pos[1], vel[1], pos[2], vel[2]]), rpy, ang_vel] + ) + if self.sim.collisions: + collision = True + thrust = self.drone.step_controller(pos, rpy, vel)[::-1] + self.sim.drone.desired_thrust[:] = thrust + self._steps += 1 + terminated = self.terminated or collision + if terminated: + print(f"Time spent in check_gate_progress: {self._debug_time:.5f} s") + return obs.astype(np.float32), self.reward, terminated, False, self.info + + @property + def obs(self): + obs = self.sim.drone.state.copy() + # Convert angular velocity to body frame + obs["ang_vel"] = R.from_euler("XYZ", obs["rpy"]).as_matrix().T @ obs["ang_vel"] + if "observation" in self.sim.disturbances: + obs = self.sim.disturbances["observation"].apply(obs) + return obs + + @property + def reward(self) -> float: + return -1 + + @property + def terminated(self) -> bool: + if self.sim.drone.state not in self.sim.state_space: + return True # Drone is out of bounds + if self.sim.collisions: + return True + if self.target_gate == -1: # Drone has passed all gates + return True + return False + + @property + def info(self): + info = {} + VISIBILITY_RANGE = 0.45 # TODO: Make this a parameter + info["collisions"] = self.sim.collisions + gates = self.sim.gates + info["target_gate"] = self.target_gate if self.target_gate < len(gates) else -1 + # Add the gate and obstacle poses to the info. If gates or obstacles are in sensor range, + # use the actual pose, otherwise use the nominal pose. + in_range = self.sim.in_range(gates, self.sim.drone, VISIBILITY_RANGE) + gates_pos = np.stack([g["nominal_pos"] for g in gates.values()]) + gates_pos[in_range] = np.stack([g["pos"] for g in gates.values()])[in_range] + gates_rpy = np.stack([g["nominal_rpy"] for g in gates.values()]) + gates_rpy[in_range] = np.stack([g["rpy"] for g in gates.values()])[in_range] + info["gates.pos"] = gates_pos + info["gates.rpy"] = gates_rpy + info["gates.in_range"] = in_range + + obstacles = self.sim.obstacles + in_range = self.sim.in_range(obstacles, self.sim.drone, VISIBILITY_RANGE) + obstacles_pos = np.stack([o["nominal_pos"] for o in obstacles.values()]) + obstacles_pos[in_range] = np.stack([o["pos"] for o in obstacles.values()])[in_range] + info["obstacles.pos"] = obstacles_pos + info["obstacles.in_range"] = in_range + + c_value = self.sim.constraints.value(self.sim.state, self.sim.drone.desired_thrust) + info["constraint.values"] = c_value + info["constraint.violation"] = self.sim.constraints.is_violated( + self.sim.state, self.sim.drone.desired_thrust, c_value=c_value + ) + if self.symbolic: + info["symbolic.model"] = self.sim.symbolic() + info["symbolic.constraints"] = self.sim.constraints.symbolic() + return info + + def check_gate_progress(self): + # TODO: Check with an analytical solution instead of ray + if self.sim.n_gates > 0 and self.target_gate < self.sim.n_gates and self.target_gate != -1: + x, y, z = self.sim.gates[self.target_gate]["pos"] + _, _, ry = self.sim.gates[self.target_gate]["rpy"] + half_length = 0.1875 # Obstacle URDF dependent. TODO: Make this a parameter + delta_x = 0.05 * np.cos(ry) + delta_y = 0.05 * np.sin(ry) + fr = [[x, y, z - half_length]] + to = [[x, y, z + half_length]] + for i in [1, 2, 3]: + fr.append([x + i * delta_x, y + i * delta_y, z - half_length]) + fr.append([x - i * delta_x, y - i * delta_y, z - half_length]) + to.append([x + i * delta_x, y + i * delta_y, z + half_length]) + to.append([x - i * delta_x, y - i * delta_y, z + half_length]) + rays = p.rayTestBatch( + rayFromPositions=fr, rayToPositions=to, physicsClientId=self.sim.pyb_client + ) + if any(r[2] < 0.9999 and r[0] == self.sim.drone.id for r in rays): + self.target_gate += 1 + if self.target_gate == self.sim.n_gates: + self.target_gate = -1 + + def render(self): + self.sim.render() + + def close(self): + self.sim.close() diff --git a/safe_control_gym/envs/drone_sim.py b/safe_control_gym/envs/drone_sim.py index 6b8fffd53..499561a9e 100644 --- a/safe_control_gym/envs/drone_sim.py +++ b/safe_control_gym/envs/drone_sim.py @@ -1,7 +1,7 @@ -"""Base environment class module. - -This module also contains enumerations for cost functions, tasks, disturbances, and quadrotor types. +"""Quadrotor environment using PyBullet physics. +Based on UTIAS Dynamic Systems Lab's gym-pybullet-drones: + * https://github.com/utiasDSL/gym-pybullet-drones """ from __future__ import annotations @@ -11,12 +11,26 @@ import gymnasium from gymnasium import spaces from gymnasium.utils import seeding -import copy -import math +from safe_control_gym.envs.physics import PhysicsMode, Physics + +import pybullet as p +import pybullet_data +from pathlib import Path +import logging +from safe_control_gym.envs.utils import map2pi +import numpy.typing as npt +from scipy.spatial.transform import Rotation as R from safe_control_gym.envs.constraints import ConstraintList -from safe_control_gym.envs.disturbances import create_disturbance_list +from safe_control_gym.envs.disturbances import DisturbanceList from safe_control_gym.envs.drone import Drone +from safe_control_gym.envs.physics import Constants + +from safe_control_gym.math_and_models.symbolic_systems import SymbolicModel +from safe_control_gym.envs.quadrotor_utils import thrust2pwm +from safe_control_gym.envs.symbolic import symbolic + +logger = logging.getLogger(__name__) @dataclass @@ -26,23 +40,48 @@ class SimSettings: sim_freq: int = 500 ctrl_freq: int = 500 gui: bool = False + pybullet_id: int = 0 + # Camera settings + render_resolution: tuple[int, int] = (640, 480) + camera_view: tuple[float, ...] = (0,) * 16 + camera_projection: tuple[float, ...] = (0,) * 16 def __post_init__(self): assert self.sim_freq % self.ctrl_freq == 0, "sim_freq must be divisible by ctrl_freq." + self.camera_projection = p.computeProjectionMatrixFOV( + fov=60.0, + aspect=self.render_resolution[0] / self.render_resolution[1], + nearVal=0.1, + farVal=1000.0, + ) + self.camera_view = p.computeViewMatrixFromYawPitchRoll( + distance=3, + yaw=-30, + pitch=-30, + roll=0, + cameraTargetPosition=[0, 0, 0], + upAxisIndex=2, + physicsClientId=0, + ) class DroneSim(gymnasium.Env): """Drone simulation based on gym-pybullet-drones.""" + URDF_DIR = Path(__file__).resolve().parent / "assets" + def __init__( self, + track: dict, sim_freq: int = 500, ctrl_freq: int = 500, - constraints=None, - disturbances=None, + constraints: list[dict] = [], + disturbances: dict = {}, + randomization: dict = {}, gui: bool = False, - seed: int | None = None, - reseed: bool = False, + camera_view: tuple[float, ...] = (5.0, -40.0, -40.0, 0.5, -1.0, 0.5), + n_drones: int = 1, + physics: PhysicsMode = PhysicsMode.PYB, ): """Initialization method for BenchmarkEnv. @@ -52,93 +91,200 @@ def __init__( ctrl_freq: The frequency at which the environment steps. constraints: Dictionary to specify the constraints being used. disturbances: Dictionary to specify disturbances being used. - seed: Seed for the random number generator. - reseed: Option to reseed the environment on each reset with the same seed. """ - self.sim_settings = SimSettings(sim_freq, ctrl_freq, gui) + assert n_drones == 1, "Only one drone is supported at the moment." self.drone = Drone(controller="mellinger") + self.n_drones = n_drones + self.pyb_client = p.connect(p.GUI if gui else p.DIRECT) + self.settings = SimSettings(sim_freq, ctrl_freq, gui, pybullet_id=self.pyb_client) + self.physics = Physics(self.pyb_client, 1 / sim_freq, PhysicsMode(physics)) # Create action, observation and state spaces. min_thrust, max_thrust = self.drone.params.min_thrust, self.drone.params.max_thrust self.action_space = spaces.Box(low=min_thrust, high=max_thrust, shape=(4,)) - - # pos: x, y, z in meters - # rpy: roll, pitch, yaw in radians - # vel: vx, vy, vz in m/s - # ang_vel: p, q, r in rad/s + # pos in meters, rpy in radians, vel in m/s ang_vel in rad/s rpy_max = np.array([85 / 180 * np.pi, 85 / 180 * np.pi, np.pi], np.float32) # Yaw unbounded max_flt = np.full(3, np.finfo(np.float32).max, np.float32) + pos_low, pos_high = np.array([-5, -5, 0]), np.array([5, 5, 2.5]) self.observation_space = spaces.Dict( { - "pos": spaces.Box(low=np.array([-5, -5, 0]), high=np.array([5, 5, 2.5])), + "pos": spaces.Box(low=pos_low, high=pos_high), "rpy": spaces.Box(low=-rpy_max, high=rpy_max), "vel": spaces.Box(low=-max_flt, high=max_flt), "ang_vel": spaces.Box(low=-max_flt, high=max_flt), } ) - self.state_space = self.observation_space + # State space uses 64-bit floats for better compatibility with pycffirmware. + self.state_space = spaces.Dict( + { + "pos": spaces.Box(low=pos_low, high=pos_high, dtype=np.float64), + "rpy": spaces.Box(low=-rpy_max, high=rpy_max, dtype=np.float64), + "vel": spaces.Box(low=-max_flt, high=max_flt, dtype=np.float64), + "ang_vel": spaces.Box(low=-max_flt, high=max_flt, dtype=np.float64), + } + ) self.constraints = ConstraintList.from_specs( self.state_space, self.action_space, constraints ) - # Set disturbance info. - self._disturbance_config = disturbances + self.disturbance_config = disturbances self.disturbances = self._setup_disturbances(disturbances) - # Default seed None means pure randomness/no seeding. - self.seed(seed) - # IROS 2022 - Save random seed for re-seeding. - self._seed = seed - self._reseed_on_reset = reseed + self.randomization = randomization + if self.settings.gui: + p.resetDebugVisualizerCamera( + cameraDistance=camera_view[0], + cameraYaw=camera_view[1], + cameraPitch=camera_view[2], + cameraTargetPosition=[camera_view[3], camera_view[4], camera_view[5]], + physicsClientId=self.pyb_client, + ) + + assert isinstance(track.drone, dict), "Expected drone state as dictionary." + for key, val in track.drone.items(): + assert key in self.drone.init_state, f"Unknown key '{key}' in drone state." + self.drone.init_state[key][:] = val + + self.pyb_objects = {} # Populated when objects are loaded into PyBullet. + + self.gates = {} + if gates := track.get("gates"): + self.gates = {i: g.toDict() for i, g in enumerate(gates)} + # Add nominal values to not loose the default when randomizing. + for i, gate in self.gates.items(): + self.gates[i].update({"nominal_" + k: v for k, v in gate.items()}) + self.n_gates = len(self.gates) - def seed(self, seed=None): - """Set up a random number generator for a given seed. + self.obstacles = {} + if obstacles := track.get("obstacles"): + self.obstacles = {i: o.toDict() for i, o in enumerate(obstacles)} + for i, obstacle in self.obstacles.items(): + self.obstacles[i].update({"nominal_" + k: v for k, v in obstacle.items()}) + self.n_obstacles = len(self.obstacles) - Remember to seed all random generators, currently in - - env - - action_space - - disturbances + # Helper variables + self._recording = None # PyBullet recording. + def step(self, desired_thrust: npt.NDArray[np.float_]): + """Advance the environment by one control step. + + Args: + desired_thrust: The desired thrust for the drone. """ + self.drone.desired_thrust[:] = desired_thrust + rpm = self._thrust_to_rpm(desired_thrust) # Pre-process/clip the action + disturb_force = np.zeros(3) + if "dynamics" in self.disturbances: # Add dynamics disturbance force. + disturb_force = self.disturbances["dynamics"].apply(disturb_force) + for _ in range(self.settings.sim_freq // self.settings.ctrl_freq): + self.drone.rpm[:] = rpm # Save the last applied action (e.g. to compute drag) + force_torques = self.physics.calculate_force_torques(self.drone, rpm, []) + self.physics.apply(self.drone, force_torques, disturb_force) + self.physics.step(self.drone) + self._sync_pyb_to_sim() + + def reset(self): + """Reset the simulation to its original state.""" + for mode in self.disturbances.keys(): + self.disturbances[mode].reset() + self.drone.reset() + self._reset_pybullet() + self._randomize_drone() + self._sync_pyb_to_sim() + + @property + def collisions(self) -> list[int]: + """Return the pybullet object IDs of the objects currently in collision with the drone.""" + collisions = [] + for o_id in self.pyb_objects.values(): + if p.getContactPoints(bodyA=o_id, bodyB=self.drone.id, physicsClientId=self.pyb_client): + collisions.append(o_id) + return collisions + + def in_range(self, bodies: dict, target_body, distance: float) -> npt.NDArray[np.bool_]: + """Return a mask array of objects within a certain distance of the drone.""" + in_range = np.zeros(len(bodies), dtype=bool) + for i, body in enumerate(bodies.values()): + assert "id" in body, "Body must have a PyBullet ID." + closest_points = p.getClosestPoints( + bodyA=body["id"], + bodyB=target_body.id, + distance=distance, + physicsClientId=self.pyb_client, + ) + in_range[i] = len(closest_points) > 0 + return in_range + + @property + def state(self): + # TODO: Remove in favor of the state property in the drone class. + pos = self.drone.state["pos"] + vel = self.drone.state["vel"] + rpy = self.drone.state["rpy"] + ang_vel = self.drone.state["ang_vel"] + ang_v_body_frame = R.from_euler("XYZ", rpy).as_matrix().T @ ang_vel + state = np.hstack( + [pos[0], vel[0], pos[1], vel[1], pos[2], vel[2], rpy, ang_v_body_frame] + ).reshape((12,)) + return state + + @property + def n_constraints(self): + return self.constraints.n_constraints + + def seed(self, seed: int | None = None): + """Set up a random number generator for a given seed.""" self.np_random, seed = seeding.np_random(seed) self.action_space.seed(seed) for d in self.disturbances.values(): - d.seed(self) - return [seed] + d.seed(seed) + return seed - def _randomize_values_by_info(self, original_values, randomization_info): - """Randomizes a list of values according to desired distributions. - - Args: - original_values (dict): a dict of orginal values. - randomization_info (dict): A dictionary containing information about the distributions - used to randomize original_values. + def render(self) -> npt.NDArray[np.uint8]: + """Retrieve a frame from PyBullet rendering. Returns: - dict: A dict of randomized values. + The RGB frame captured by PyBullet's camera as [h, w, 4] tensor. + """ + [w, h, rgb, _, _] = p.getCameraImage( + width=self.settings.render_resolution[0], + height=self.settings.render_resolution[1], + shadow=1, + viewMatrix=self.settings.camera_view, + projectionMatrix=self.settings.camera_projection, + renderer=p.ER_TINY_RENDERER, + flags=p.ER_SEGMENTATION_MASK_OBJECT_AND_LINKINDEX, + physicsClientId=self.pyb_client, + ) + return np.reshape(rgb, (h, w, 4)) + + def record(self, path: Path): + """Start the recording of a video output. + + The format of the video output is .mp4, if GUI is True, or .png, otherwise. + The video is saved under folder `files/videos`. """ - # Start from a copy of the original values. - randomized_values = copy.deepcopy(original_values) - # Copy the info dict to parse it with "pop". - rand_info_copy = copy.deepcopy(randomization_info) - # Randomized and replace values for which randomization info are given. - for key in original_values: - if key in rand_info_copy: - # Get distribution removing it from info dict. - distrib = getattr(self.np_random, rand_info_copy[key].pop("distrib")) - # Pop positional args. - d_args = rand_info_copy[key].pop("args", []) - # Keyword args are just anything left. - d_kwargs = rand_info_copy[key] - # Randomize (adding to the original values). - randomized_values[key] += distrib(*d_args, **d_kwargs) - return randomized_values - - def _setup_disturbances(self, disturbances: dict | None = None): - """Creates attributes and action spaces for the disturbances.""" - # Default: no passive disturbances. - if disturbances is None: + if not self.settings.gui: + logger.warning("Cannot record video without GUI.") return + self._recording = p.startStateLogging( + loggingType=p.STATE_LOGGING_VIDEO_MP4, + fileName=str(path.absolute()), + physicsClientId=self.pyb_client, + ) + + def close(self): + """Terminates the environment.""" + if self._recording is not None: + p.stopStateLogging(self._recording, physicsClientId=self.pyb_client) + if self.pyb_client >= 0: + p.disconnect(physicsClientId=self.pyb_client) + self.pyb_client = -1 + + def _setup_disturbances(self, disturbances: dict | None = None) -> dict[str, DisturbanceList]: + """Creates attributes and action spaces for the disturbances.""" dist = {} + if disturbances is None: # Default: no passive disturbances. + return dist modes = { "observation": {"dim": spaces.flatdim(self.observation_space)}, "action": {"dim": spaces.flatdim(self.action_space)}, @@ -146,44 +292,182 @@ def _setup_disturbances(self, disturbances: dict | None = None): } for mode, spec in disturbances.items(): assert mode in modes, "Disturbance mode not available." - dist[mode] = create_disturbance_list(spec, modes[mode], self) + spec["dim"] = modes[mode]["dim"] + dist[mode] = DisturbanceList.from_specs([spec]) return dist - @property - def n_constraints(self): - return 0 if self.constraints is None else self.constraints.n_constraints - - def before_reset(self): - """Pre-processing before calling `.reset()`.""" - if self._reseed_on_reset: # IROS 2022 - Re-seed on reset. - self.seed(self._seed) - - # Housekeeping variables. - self.pyb_step_counter = 0 - self.ctrl_step_counter = 0 - self.current_raw_input_action = None - self.current_preprocessed_action = None - # Reset the disturbances. - for mode in self.disturbances.keys(): - self.disturbances[mode].reset(self) - - def after_step(self, obs, rew, done, info): - """Post-processing after calling `.step()`.""" - # Increment counters - self.pyb_step_counter += self.sim_settings.sim_freq // self.sim_settings.ctrl_freq - self.ctrl_step_counter += 1 - - # Terminate when (any) constraint is violated. - # here we cache the constraint values `c_value`, so we only evaluate the constraints once, - # but use it in 1) info dict; 2) check constraint violation; 3) check near constraint violation/almost active. - # it also allows stateful constraint, where evaluation/update should only be done once per time step. - c_value = None - if self.constraints is not None: - c_value = self.constraints.value(self.state, self.current_raw_input_action) - info["constraint_values"] = c_value - info["constraint_violation"] = self.constraints.is_violated( - self.state, self.current_raw_input_action, c_value=c_value + def _reset_pybullet(self): + """Reset PyBullet's simulation environment.""" + p.resetSimulation(physicsClientId=self.pyb_client) + p.setGravity(0, 0, -Constants.GRAVITY, physicsClientId=self.pyb_client) + p.setRealTimeSimulation(0, physicsClientId=self.pyb_client) + p.setTimeStep(1 / self.settings.sim_freq, physicsClientId=self.pyb_client) + p.setAdditionalSearchPath(pybullet_data.getDataPath(), physicsClientId=self.pyb_client) + # Load ground plane, drone and obstacles models. + i = p.loadURDF("plane.urdf", [0, 0, 0], physicsClientId=self.pyb_client) + self.pyb_objects["plane"] = i + + self.drone.id = p.loadURDF( + str(self.URDF_DIR / "cf2x.urdf"), + self.drone.init_state["pos"], + p.getQuaternionFromEuler(self.drone.init_state["rpy"]), + flags=p.URDF_USE_INERTIA_FROM_FILE, # Use URDF inertia tensor. + physicsClientId=self.pyb_client, + ) + # Remove default damping. + p.changeDynamics(self.drone.id, -1, linearDamping=0, angularDamping=0) + # Load obstacles into the simulation and perturb their poses if configured. + self._reset_obstacles() + self._reset_gates() + self._sync_pyb_to_sim() + + def _reset_obstacles(self): + """Reset the obstacles in the simulation.""" + for i, obstacle in self.obstacles.items(): + pos_offset = np.zeros(3) + if obstacle_pos := self.randomization.get("obstacle_pos"): + distrib = getattr(self.np_random, obstacle_pos.get("type")) + kwargs = {k: v for k, v in obstacle_pos.items() if k != "type"} + pos_offset = distrib(**kwargs) + self.obstacles[i]["pos"] = np.array(obstacle["nominal_pos"]) + pos_offset + self.obstacles[i]["id"] = self._load_urdf_into_sim( + self.URDF_DIR / "obstacle.urdf", + self.obstacles[i]["pos"] + pos_offset, + marker=str(i), + ) + self.pyb_objects[f"obstacle_{i}"] = self.obstacles[i]["id"] + + def _reset_gates(self): + for i, gate in self.gates.items(): + pos_offset = np.zeros_like(gate["nominal_pos"]) + if gate_pos := self.randomization.get("gate_pos"): + distrib = getattr(self.np_random, gate_pos.get("type")) + pos_offset = distrib(**{k: v for k, v in gate_pos.items() if k != "type"}) + rpy_offset = np.zeros(3) + if gate_rpy := self.randomization.get("gate_rpy"): + distrib = getattr(self.np_random, gate_rpy.get("type")) + rpy_offset = distrib(**{k: v for k, v in gate_rpy.items() if k != "type"}) + gate["pos"] = np.array(gate["nominal_pos"]) + pos_offset + gate["rpy"] = map2pi(np.array(gate["nominal_rpy"]) + rpy_offset) # Ensure [-pi, pi] + gate["id"] = self._load_urdf_into_sim( + self.URDF_DIR / "gate.urdf", gate["pos"], gate["rpy"], marker=str(i) + ) + self.pyb_objects[f"gate_{i}"] = gate["id"] + + def _load_urdf_into_sim( + self, + urdf_path: Path, + pos: npt.NDArray[np.float_], + rpy: npt.NDArray[np.float_] | None = None, + marker: str | None = None, + ) -> int: + """Load a URDF file into the simulation. + + Args: + urdf_path: Path to the URDF file. + pos: Position of the object in the simulation. + rpy: Roll, pitch, yaw orientation of the object in the simulation. + marker: Optional text marker to display above the object + + Returns: + int: The ID of the object in the simulation. + """ + quat = p.getQuaternionFromEuler(rpy if rpy is not None else np.zeros(3)) + pyb_id = p.loadURDF(str(urdf_path), pos, quat, physicsClientId=self.pyb_client) + if marker is not None: + p.addUserDebugText( + str(marker), + textPosition=[0, 0, 0.5], + textColorRGB=[1, 0, 0], + textSize=1.5, + parentObjectUniqueId=pyb_id, + parentLinkIndex=-1, + physicsClientId=self.pyb_client, ) - # Terminate when reaching time limit, - # but distinguish between done due to true termination or time limit reached - return obs, rew, done, info + return pyb_id + + def _randomize_drone(self): + """Randomize the drone's position, orientation and physical properties.""" + inertia_diag = self.drone.nominal_params.J.diagonal() + if drone_inertia := self.randomization.get("drone_inertia"): + distrib = getattr(self.np_random, drone_inertia.type) + kwargs = {k: v for k, v in drone_inertia.items() if k != "type"} + inertia_diag = inertia_diag + distrib(**kwargs) + assert all(inertia_diag > 0), "Negative randomized inertial properties." + self.drone.params.J = np.diag(inertia_diag) + + mass = self.drone.nominal_params.mass + if drone_mass := self.randomization.get("drone_mass"): + distrib = getattr(self.np_random, drone_mass.type) + mass += distrib(**{k: v for k, v in drone_mass.items() if k != "type"}) + assert mass > 0, "Negative randomized mass." + self.drone.params.mass = mass + + p.changeDynamics( + self.drone.id, + linkIndex=-1, # Base link. + mass=mass, + localInertiaDiagonal=inertia_diag, + physicsClientId=self.pyb_client, + ) + + pos = self.drone.init_state["pos"] + if drone_pos := self.randomization.get("drone_pos"): + distrib = getattr(self.np_random, drone_pos.type) + pos += distrib(**{k: v for k, v in drone_pos.items() if k != "type"}) + + rpy = self.drone.init_state["rpy"] + if drone_rpy := self.randomization.get("drone_rpy"): + distrib = getattr(self.np_random, drone_rpy.type) + kwargs = {k: v for k, v in drone_rpy.items() if k != "type"} + rpy = np.clip(rpy + distrib(**kwargs), -np.pi, np.pi) + + p.resetBasePositionAndOrientation( + self.drone.id, + pos, + p.getQuaternionFromEuler(rpy), + physicsClientId=self.pyb_client, + ) + p.resetBaseVelocity(self.drone.id, [0, 0, 0], [0, 0, 0], physicsClientId=self.pyb_client) + + def _sync_pyb_to_sim(self): + """Read state values from PyBullet and write them to the simulator class. + + We cache the state values in the simulator class to avoid calling PyBullet too frequently. + """ + pos, quat = p.getBasePositionAndOrientation(self.drone.id, physicsClientId=self.pyb_client) + self.drone.state["pos"] = np.array(pos, float) + self.drone.state["rpy"] = np.array(p.getEulerFromQuaternion(quat), float) + vel, ang_vel = p.getBaseVelocity(self.drone.id, physicsClientId=self.pyb_client) + self.drone.state["vel"] = np.array(vel, float) + self.drone.state["ang_vel"] = np.array(ang_vel) + + def symbolic(self) -> SymbolicModel: + """Create a symbolic (CasADi) model for dynamics, observation, and cost. + + Returns: + CasADi symbolic model of the environment. + """ + return symbolic(self.drone, 1 / self.settings.sim_freq) + + def _thrust_to_rpm(self, thrust: npt.NDArray[np.float_]): + """Convert the desired_thrust into motors' RPMs. + + Args: + thrust: The desired thrust per motor. + + Returns: + The motors' RPMs to apply to the quadrotor. + """ + thrust = np.clip(thrust, self.drone.params.min_thrust, self.drone.params.max_thrust) + if "action" in self.disturbances: + thrust = self.disturbances["action"].apply(thrust) + pwm = thrust2pwm( + thrust, + self.drone.params.pwm2rpm_scale, + self.drone.params.pwm2rpm_const, + self.drone.params.kf, + self.drone.params.min_pwm, + self.drone.params.max_pwm, + ) + return self.drone.params.pwm2rpm_const + self.drone.params.pwm2rpm_scale * pwm diff --git a/safe_control_gym/envs/firmware_wrapper.py b/safe_control_gym/envs/firmware_wrapper.py deleted file mode 100644 index 1e147876b..000000000 --- a/safe_control_gym/envs/firmware_wrapper.py +++ /dev/null @@ -1,121 +0,0 @@ -from __future__ import annotations - -import numpy as np -import logging -import math -from typing import Callable - -import gymnasium -from safe_control_gym.envs.quadrotor import Quadrotor -from safe_control_gym.envs.drone import Drone - -import importlib.util - -spec = importlib.util.find_spec("pycffirmware") -firm = importlib.util.module_from_spec(spec) -spec.loader.exec_module(firm) -# import pycffirmware as firm - -logger = logging.getLogger(__name__) - - -class FirmwareWrapper(gymnasium.Env): - CONTROLLER = "mellinger" # specifies controller type - - def __init__(self, env_func: Callable[[], Quadrotor], firmware_freq: int, ctrl_freq: int): - """Initializes a FirmwareWrapper object. - - This allows users to simulate the on board controllers of CrazyFlie (CF), including access - to a portion of the CF command API. FirmwareWrapper.reset() must be called at the beginning - of every episode. - - Args: - env_func (function): initilizer for safe-control-gym environment - firmware_freq (int): frequency to run the firmware loop at (typically 500) - ctrl_freq (int): frequency that .step will be called (typically < 60) - - Attributes: - env: safe-control environment - - Todo: - * Add support for state estimation - """ - super().__init__() - self.action_space = gymnasium.spaces.Box(low=-1, high=1, shape=(4,)) - self.observation_space = gymnasium.spaces.Box(low=-1, high=1, shape=(10,)) - self.drone = Drone(self.CONTROLLER) - self.firmware_freq = firmware_freq - self.ctrl_freq = ctrl_freq - self.step_freq = ctrl_freq - self.env = env_func() - - def reset(self): - obs, info = self.env.reset() - pos, vel, rpy = obs["pos"], obs["vel"], obs["rpy"] - self.drone.reset(pos, rpy, vel) - obs = np.concatenate( - [np.array([pos[0], vel[0], pos[1], vel[1], pos[2], vel[2]]), rpy, obs["ang_vel"]] - ) - if self.env.n_drones > 1: - raise NotImplementedError("Firmware wrapper does not support multiple drones.") - return obs, info - - def close(self): - self.env.close() - - def step(self, sim_time: float, action: np.ndarray): - """Step the firmware_wrapper class and its environment. - - This function should be called once at the rate of ctrl_freq. Step processes and high level - commands, and runs the firmware loop and simulator according to the frequencies set. - - Args: - sim_time (float): Time in seconds since beginning the episode - action (np.ndarray(4)): Motor control forces to be applied. Order is: front left, back - left, back right, front right. - - Todo: - * Add support for state estimation - """ - while self.drone.tick / self.drone.firmware_freq < sim_time + 1 / self.step_freq: - obs, reward, done, info = self.env.step(action) - pos, vel, rpy = obs["pos"], obs["vel"], obs["rpy"] - obs = np.concatenate( - [np.array([pos[0], vel[0], pos[1], vel[1], pos[2], vel[2]]), rpy, obs["ang_vel"]] - ) - action = self.drone.step_controller(pos, rpy, vel)[::-1] - return obs, reward, done, info, action - - # region Controller functions - - def sendFullStateCmd(self, pos, vel, acc, yaw, rpy_rate, timestep): - self.drone.full_state_cmd(pos, vel, acc, yaw, rpy_rate, timestep) - - def sendTakeoffCmd(self, height, duration): - self.drone.takeoff_cmd(height, duration) - - def sendTakeoffYawCmd(self, height, duration, yaw): - self.drone.takeoff_cmd(height, duration, yaw) - - def sendTakeoffVelCmd(self, height, vel, relative): - self.drone.takeoff_vel_cmd(height, vel, relative) - - def sendLandCmd(self, height, duration): - self.drone.land_cmd(height, duration) - - def sendLandYawCmd(self, height, duration, yaw): - self.drone.land_cmd(height, duration, yaw) - - def sendLandVelCmd(self, height, vel, relative): - self.drone.land_vel_cmd(height, vel, relative) - - def sendStopCmd(self): - self.drone.stop_cmd() - - def sendGotoCmd(self, pos, yaw, duration_s, relative): - self.drone.go_to_cmd(pos, yaw, duration_s, relative) - - def notifySetpointStop(self): - self.drone.notify_setpoint_stop() - - # endregion diff --git a/safe_control_gym/envs/physics.py b/safe_control_gym/envs/physics.py new file mode 100644 index 000000000..f13e5bc12 --- /dev/null +++ b/safe_control_gym/envs/physics.py @@ -0,0 +1,262 @@ +from __future__ import annotations + +import numpy as np +from enum import Enum +import numpy.typing as npt + +from safe_control_gym.envs.drone import Drone + +import pybullet as p +from typing import NamedTuple +from scipy.spatial.transform import Rotation as R + + +class Constants: + GRAVITY: float = 9.81 + + +ForceTorque = NamedTuple("ForceTorque", f=npt.NDArray[np.float_], t=npt.NDArray[np.float_]) + + +class PhysicsMode(str, Enum): + """Physics implementations enumeration class.""" + + PYB = "pyb" # Base PyBullet physics update. + DYN = "dyn" # Update with an explicit model of the dynamics. + PYB_GND = "pyb_gnd" # PyBullet physics update with ground effect. + PYB_DRAG = "pyb_drag" # PyBullet physics update with drag. + PYB_DW = "pyb_dw" # PyBullet physics update with downwash. + # PyBullet physics update with ground effect, drag, and downwash. + PYB_GND_DRAG_DW = "pyb_gnd_drag_dw" + + +class Physics: + def __init__(self, pyb_client: int, dt: float, mode: PhysicsMode = PhysicsMode.PYB): + self.pyb_client = pyb_client + self.dt = dt + self.mode = PhysicsMode(mode) + assert isinstance(self.mode, PhysicsMode), "mode must be a PhysicsMode." + + def calculate_force_torques( + self, drone: Drone, rpms: npt.NDArray[np.float_], other_drones: list[Drone] + ) -> dict[int, list[str, npt.NDArray[np.float_]]]: + """Physics update function. + + Args: + drone: The target drone to calculate the physics for. + rpms: The rpms to apply to the drone rotors. + other_drones: List of other drones in the environment. + """ + if self.mode == PhysicsMode.PYB: + return self.motors(drone, rpms) + elif self.mode == PhysicsMode.DYN: + return self.dynamics(drone, rpms) + elif self.mode == PhysicsMode.PYB_GND: + return self.motors(drone, rpms) + self.ground_effect(drone, rpms) + elif self.mode == PhysicsMode.PYB_DRAG: + return self.motors(drone, rpms) + self.drag(drone, rpms) + elif self.mode == PhysicsMode.PYB_DW: + return self.motors(drone, rpms) + self.downwash(drone, other_drones) + elif self.mode == PhysicsMode.PYB_GND_DRAG_DW: + return ( + self.motors(drone, rpms) + + self.drag(drone, rpms) + + self.downwash(drone, other_drones) + ) + raise NotImplementedError(f"Physics mode {self.mode} not implemented.") + + def apply( + self, + drone: Drone, + force_torques: list[tuple[int, ForceTorque]], + external_force: npt.NDArray[np.float_] | None = None, + ): + """Apply the calculated forces and torques in simulation. + + Args: + drone: The target drone to apply the forces and torques to. + force_torques: A dictionary of forces and torques for each link of the drone body. + external_force: An optional, external force to apply to the drone body. + """ + for link_id, ft in force_torques: + p.applyExternalForce( + drone.id, + link_id, + forceObj=ft.f, + posObj=[0, 0, 0], + flags=p.LINK_FRAME, + physicsClientId=self.pyb_client, + ) + p.applyExternalTorque( + drone.id, + link_id, + torqueObj=ft.t, + flags=p.LINK_FRAME, + physicsClientId=self.pyb_client, + ) + if external_force is not None: + p.applyExternalForce( + drone.id, + linkIndex=4, # Link attached to the quadrotor's center of mass. + forceObj=external_force, + posObj=drone.state["pos"], + flags=p.WORLD_FRAME, + physicsClientId=self.pyb_client, + ) + + def step(self, drone: Drone): + """Advance the simulation by one step.""" + # PyBullet computes the new state, unless PhysicsMode.DYN. + if self.mode != PhysicsMode.DYN: + p.stepSimulation(physicsClientId=self.pyb_client) + elif self.mode == PhysicsMode.DYN: + p.resetBasePositionAndOrientation( + drone.id, + drone.state["pos"], + p.getQuaternionFromEuler(drone.state["rpy"]), + physicsClientId=self.pyb_client, + ) + p.resetBaseVelocity( + drone.id, + drone.state["vel"], + drone.state["ang_vel"], + physicsClientId=self.pyb_client, + ) + + @staticmethod + def motors(drone: Drone, rpms: npt.NDArray[np.float_]) -> list[tuple[int, ForceTorque]]: + """Base PyBullet physics implementation. + + Args: + drone: The target drone to calculate the physics for. + rpms: The rpms to apply to the drone rotors. + + Returns: + A list of tuples containing the link id and a force/torque tuple. + """ + ft = [] + forces = np.array(rpms**2) * drone.params.kf + torques = np.array(rpms**2) * drone.params.km + z_torque = torques[0] - torques[1] + torques[2] - torques[3] + for i in range(4): + ft.append((i, ForceTorque([0, 0, forces[i]], [0, 0, 0]))) + ft.append((4, ForceTorque([0, 0, 0], [0, 0, z_torque]))) + return ft + + def dynamics(self, drone: Drone, rpms: npt.NDArray[np.float_]) -> list[tuple[int, ForceTorque]]: + """Explicit dynamics implementation. + + Based on code written at the Dynamic Systems Lab by James Xu. + + Args: + drone: The target drone to calculate the physics for. + rpms: The rpm to apply to the drone rotors. + """ + pos = drone.state["pos"] + rpy = drone.state["rpy"] + vel = drone.state["vel"] + rpy_rates = drone.state["ang_vel"] + rotation = R.from_euler("XYZ", rpy).as_matrix() + # Compute forces and torques. + forces = np.array(rpms**2) * drone.params.kf + thrust = np.array([0, 0, np.sum(forces)]) + thrust_world_frame = np.dot(rotation, thrust) + force_world_frame = thrust_world_frame - np.array([0, 0, Constants.GRAVITY]) + z_torques = np.array(rpms**2) * drone.params.km + z_torque = z_torques[0] - z_torques[1] + z_torques[2] - z_torques[3] + L = drone.params.arm_len + x_torque = (forces[0] + forces[1] - forces[2] - forces[3]) * (L / np.sqrt(2)) + y_torque = (-forces[0] + forces[1] + forces[2] - forces[3]) * (L / np.sqrt(2)) + torques = np.array([x_torque, y_torque, z_torque]) + torques = torques - np.cross(rpy_rates, np.dot(drone.params.J, rpy_rates)) + rpy_rates_deriv = np.dot(drone.params.J_inv, torques) + no_pybullet_dyn_accs = force_world_frame / drone.params.mass + # Update state. + vel = vel + no_pybullet_dyn_accs * self.dt + rpy_rates = rpy_rates + rpy_rates_deriv * self.dt + drone.state["pos"] = pos + vel * self.dt + drone.state["rpy"] = rpy + rpy_rates * self.dt + drone.state["vel"] = vel + drone.state["ang_vel"] = rpy_rates + return [] # No forces/torques to apply. We set the drone state directly. + + @staticmethod + def downwash(drone: Drone, other_drones: list[Drone]) -> list[tuple[int, ForceTorque]]: + """PyBullet implementation of a ground effect model. + + Based on experiments conducted at the Dynamic Systems Lab by SiQi Zhou. + + Args: + drone: The target drone to calculate the downwash effect for. + other_drones: List of other drones in the environment. + + Returns: + A list of tuples containing the link id and a force/torque tuple. + """ + ft = [] + for other_drone in other_drones: + delta_z = drone.pos[2] - other_drone.pos[2] + delta_xy = np.linalg.norm(drone.state["pos"][:2] - other_drone.state["pos"][:2]) + if delta_z > 0 and delta_xy < 10: # Ignore drones more than 10 meters away + alpha = ( + other_drone.params.dw_coeff_1 + * (other_drone.params.prop_radius / (4 * delta_z)) ** 2 + ) + beta = other_drone.params.dw_coeff_2 * delta_z + other_drone.params.dw_coeff_3 + force = [0, 0, -alpha * np.exp(-0.5 * (delta_xy / beta) ** 2)] + ft.append((4, ForceTorque(force, [0, 0, 0]))) + return ft + + def ground_effect( + self, drone: Drone, rpms: npt.NDArray[np.float_] + ) -> list[tuple[int, ForceTorque]]: + """PyBullet implementation of a ground effect model. + + Inspired by the analytical model used for comparison in (Shi et al., 2019). + + Args: + drone: The target drone to calculate the ground effect for. + rpms: The rpms to apply to the drone rotors. + + Returns: + A list of tuples containing the link id and a force/torque tuple. + """ + s = p.getLinkStates( + drone.id, + linkIndices=[0, 1, 2, 3], + computeLinkVelocity=1, + computeForwardKinematics=1, + physicsClientId=self.pyb_client, + ) + prop_heights = np.array([s[0][0][2], s[1][0][2], s[2][0][2], s[3][0][2]]) + prop_heights = np.clip(prop_heights, drone.params.gnd_eff_min_height_clip, np.inf) + gnd_effects = ( + rpms**2 + * drone.params.kf + * drone.params.gnd_eff_coeff + * (drone.params.prop_radius / (4 * prop_heights)) ** 2 + ) + ft = [] + if np.abs(drone.state["rpy"][:2]).max() < np.pi / 2: # Ignore when not approximately level + for i in range(4): + ft.append((i, ForceTorque([0, 0, gnd_effects[i]], [0, 0, 0]))) + return ft + + @staticmethod + def drag(drone: Drone, rpms: npt.NDArray[np.float_]): + """PyBullet implementation of a drag model. + + Based on the the system identification in (Forster, 2015). + + Args: + drone: The target drone to calculate the ground effect for. + rpms: The rpms to apply to the drone rotors. + + Returns: + A list of tuples containing the link id and a force/torque tuple. + """ + rot = R.from_euler("XYZ", drone.state["rpy"]).as_matrix() + # Simple draft model applied to the base/center of mass + drag_factors = -1 * drone.params.drag_coeff * np.sum(np.array(2 * np.pi * rpms / 60)) + drag = np.dot(rot, drag_factors * np.array(drone.state["vel"])) + return [(4, ForceTorque(drag, [0, 0, 0]))] diff --git a/safe_control_gym/envs/quadrotor.py b/safe_control_gym/envs/quadrotor.py deleted file mode 100644 index ad2e795f0..000000000 --- a/safe_control_gym/envs/quadrotor.py +++ /dev/null @@ -1,691 +0,0 @@ -"""Quadrotor environment using PyBullet physics. - -Based on UTIAS Dynamic Systems Lab's gym-pybullet-drones: - * https://github.com/utiasDSL/gym-pybullet-drones - -""" - -import os -import logging -from copy import deepcopy -import casadi as cs -import numpy as np -import pybullet as p - -from safe_control_gym.math_and_models.symbolic_systems import SymbolicModel -from safe_control_gym.envs.base_aviary import BaseAviary -from safe_control_gym.envs.quadrotor_utils import QuadType, cmd2pwm, pwm2rpm -from safe_control_gym.math_and_models.transformations import csRotXYZ -from gymnasium import spaces - -logger = logging.getLogger(__name__) - - -class Quadrotor(BaseAviary): - """1D and 2D quadrotor environment task. - - Including symbolic model, constraints, randomization, adversarial disturbances, - multiple cost functions, stabilization and trajectory tracking references. - - """ - - GATE_Z_LOW = 0.525 - GATE_Z_HIGH = 1.0 - OBSTACLE_Z = 1.05 - - def __init__( - self, - init_state=None, - # custom args - quad_type: QuadType = QuadType.TWO_D, - norm_act_scale=0.1, - obs_goal_horizon=0, - rew_state_weight=1.0, - rew_act_weight=0.0001, - rew_exponential=True, - done_on_out_of_bound=True, - **kwargs, - ): - """Initialize a quadrotor environment. - - Args: - init_state (ndarray, optional): The initial state of the environment, (z, z_dot) or (x, x_dot, z, z_dot theta, theta_dot). - quad_type (QuadType, optional): The choice of motion type (1D along z or 2D in the x-z plane). - norm_act_scale (float): scaling the [-1,1] action space around hover thrust when `normalized_action_space` is True. - obs_goal_horizon (int): how many future goal states to append to obervation. - rew_state_weight (list/ndarray): quadratic weights for state in rl reward. - rew_act_weight (list/ndarray): quadratic weights for action in rl reward. - rew_exponential (bool): if to exponentiate negative quadratic cost to positive, bounded [0,1] reward. - done_on_out_of_bound (bool): if to termiante when state is out of bound. - - """ - self._init_state = init_state - self._init_state_randomization = kwargs["init_state_randomization_info"] - self._enable_init_state_randomization = kwargs["randomized_init"] - - self._inertial = None - self._inertial_randomization = kwargs["inertial_prop_randomization_info"] - self._enable_inertial_randomization = kwargs["randomized_inertial_prop"] - - # Select the 1D (moving along z) or 2D (moving in the xz plane) quadrotor. - self.QUAD_TYPE = QuadType(quad_type) - self.norm_act_scale = norm_act_scale - self.obs_goal_horizon = obs_goal_horizon - self.rew_state_weight = np.array(rew_state_weight, ndmin=1, dtype=float) - self.rew_act_weight = np.array(rew_act_weight, ndmin=1, dtype=float) - self.rew_exponential = rew_exponential - self.done_on_out_of_bound = done_on_out_of_bound - - # BaseAviary constructor, called after defining the custom args, - # since some BenchmarkEnv init setup can be task(custom args)-dependent. - super().__init__(init_state=init_state, **kwargs) - - # Store initial state info. - self.INIT_STATE_LABELS = [ - "init_x", - "init_x_dot", - "init_y", - "init_y_dot", - "init_z", - "init_z_dot", - "init_phi", - "init_theta", - "init_psi", - "init_p", - "init_q", - "init_r", - ] - - assert isinstance(init_state, dict), "Expected init_state as dictionary." - for init_name in self.INIT_STATE_LABELS: - self.__dict__[init_name.upper()] = init_state.get(init_name, 0.0) - - # Set prior/symbolic info. - self._setup_symbolic() - - # Equilibrium point at hover for linearization. - self.X_EQ = np.zeros(spaces.flatdim(self.state_space)) - - # IROS 2022 - Load maze. - self.OBSTACLES = [] - self.GATES = [] - if "obstacles" in kwargs: - self.OBSTACLES = kwargs["obstacles"] - self.OBSTACLES = np.array(self.OBSTACLES) - if "gates" in kwargs: - self.GATES = kwargs["gates"] - self.GATES = np.array(self.GATES) - self.NUM_GATES = len(self.GATES) - self.n_obstacles = len(self.OBSTACLES) - if kwargs.get("randomized_gates_and_obstacles", False): - self.RANDOMIZED_GATES_AND_OBS = True - if "gates_and_obstacles_randomization_info" not in kwargs: - raise ValueError( - "Missing 'gates_and_obstacles_randomization_info' in YAML configuration." - ) - self.GATES_AND_OBS_RAND_INFO = kwargs["gates_and_obstacles_randomization_info"] - else: - self.RANDOMIZED_GATES_AND_OBS = False - - def reset(self): - """(Re-)initializes the environment to start an episode. - - Mandatory to call at least once after __init__(). - - Returns: - ndarray: The initial state of the environment. - dict: A dictionary with information about the dynamics and constraints symbolic models. - - """ - super().before_reset() - # PyBullet simulation reset. - super()._reset_simulation() - - # IROS 2022 - Create maze. - self.OBSTACLES_IDS = [] - self.obstacle_poses = [] - if self.RANDOMIZED_GATES_AND_OBS: - rand_info_copy = deepcopy(self.GATES_AND_OBS_RAND_INFO) - distrib = getattr(self.np_random, rand_info_copy["obstacles"].pop("distrib")) - d_args = rand_info_copy["obstacles"].pop("args", []) - d_kwargs = rand_info_copy["obstacles"] - for obstacle in self.OBSTACLES: - obs_height = 0.525 # URDF dependent, places 'obstacle.urdf' at z == 0. - if self.RANDOMIZED_GATES_AND_OBS: - offset = np.array( - [distrib(*d_args, **d_kwargs), distrib(*d_args, **d_kwargs), obs_height] - ) - pose_disturbance = np.array([0, 0, distrib(*d_args, **d_kwargs)]) - else: - offset = np.array([0, 0, obs_height]) - pose_disturbance = np.array([0, 0, 0]) - self.obstacle_poses.append( - list(np.array(obstacle[0:3]) + offset) - + list(np.array(obstacle[3:6]) + pose_disturbance) - ) - TMP_ID = p.loadURDF( - os.path.join(self.URDF_DIR, "obstacle.urdf"), - np.array(obstacle[0:3]) + offset, - p.getQuaternionFromEuler(np.array(obstacle[3:6]) + pose_disturbance), - physicsClientId=self.PYB_CLIENT, - ) - p.addUserDebugText( - str(TMP_ID), - textPosition=[0, 0, 0.5], - textColorRGB=[1, 0, 0], - textSize=1.5, - parentObjectUniqueId=TMP_ID, - parentLinkIndex=-1, - physicsClientId=self.PYB_CLIENT, - ) - self.OBSTACLES_IDS.append(TMP_ID) - self.obstacle_poses = np.array(self.obstacle_poses) - self.obstacle_poses[:, 2] = self.OBSTACLE_Z - self.GATES_IDS = [] - self._gates_pose = [] - if self.RANDOMIZED_GATES_AND_OBS: - rand_info_copy = deepcopy(self.GATES_AND_OBS_RAND_INFO) - distrib = getattr(self.np_random, rand_info_copy["gates"].pop("distrib")) - d_args = rand_info_copy["gates"].pop("args", []) - d_kwargs = rand_info_copy["gates"] - for gate in self.GATES: - if gate[6] == 0: - urdf_file = "portal.urdf" - gate_height = self.GATE_Z_HIGH # URDF dependent, places 'portal.urdf' at z == 0. - elif gate[6] == 1: - urdf_file = "low_portal.urdf" - gate_height = self.GATE_Z_LOW # URDF dependent, places 'low_portal.urdf' at z == 0. - else: - raise ValueError("[ERROR] Unknown gate type.") - if self.RANDOMIZED_GATES_AND_OBS: - offset = np.array( - [distrib(*d_args, **d_kwargs), distrib(*d_args, **d_kwargs), gate_height] - ) - pose_disturbance = np.array([0, 0, distrib(*d_args, **d_kwargs)]) - else: - offset = np.array([0, 0, gate_height]) - pose_disturbance = np.array([0, 0, 0]) - self._gates_pose.append( - list(np.array(gate[0:3]) + offset) + list(np.array(gate[3:6]) + pose_disturbance) - ) - TMP_ID = p.loadURDF( - os.path.join(self.URDF_DIR, urdf_file), - np.array(gate[0:3]) + offset, - p.getQuaternionFromEuler(np.array(gate[3:6]) + pose_disturbance), - physicsClientId=self.PYB_CLIENT, - ) - p.addUserDebugText( - str(TMP_ID), - textPosition=[0, 0, 0.5], - textColorRGB=[1, 0, 0], - textSize=1.5, - parentObjectUniqueId=TMP_ID, - parentLinkIndex=-1, - physicsClientId=self.PYB_CLIENT, - ) - self.GATES_IDS.append(TMP_ID) - self._gates_pose = np.array(self._gates_pose) - self.current_gate = 0 - - # Initialize IROS-specific attributes. - self.stepped_through_gate = False - self.currently_collided = False - self.at_goal_pos = False - self.steps_at_goal_pos = 0 - self.task_completed = False - - # Choose randomized or deterministic inertial properties. - prop_values = { - "M": self.drone.params.mass, - "Ixx": self.J[0, 0], - "Iyy": self.J[1, 1], - "Izz": self.J[2, 2], - } - if self._enable_inertial_randomization: - prop_values = self._randomize_values_by_info(prop_values, self._inertial_randomization) - if any(phy_quantity < 0 for phy_quantity in prop_values.values()): - raise ValueError( - "[ERROR] in Quadrotor.reset(), negative randomized inertial properties." - ) - self.OVERRIDDEN_QUAD_MASS = prop_values["M"] - self.OVERRIDDEN_QUAD_INERTIA = [prop_values["Ixx"], prop_values["Iyy"], prop_values["Izz"]] - - # Override inertial properties. - p.changeDynamics( - self.DRONE_IDS[0], - linkIndex=-1, # Base link. - mass=self.OVERRIDDEN_QUAD_MASS, - localInertiaDiagonal=self.OVERRIDDEN_QUAD_INERTIA, - physicsClientId=self.PYB_CLIENT, - ) - - # Randomize initial state. - init_values = { - init_name: self.__dict__[init_name.upper()] for init_name in self.INIT_STATE_LABELS - } - if self._enable_init_state_randomization: - init_values = self._randomize_values_by_info( - init_values, self._init_state_randomization - ) - INIT_XYZ = [init_values.get("init_" + k, 0.0) for k in ["x", "y", "z"]] - INIT_VEL = [init_values.get("init_" + k + "_dot", 0.0) for k in ["x", "y", "z"]] - INIT_RPY = [init_values.get("init_" + k, 0.0) for k in ["phi", "theta", "psi"]] - INIT_ANG_VEL = [init_values.get("init_" + k, 0.0) for k in ["p", "q", "r"]] - p.resetBasePositionAndOrientation( - self.DRONE_IDS[0], - INIT_XYZ, - p.getQuaternionFromEuler(INIT_RPY), - physicsClientId=self.PYB_CLIENT, - ) - p.resetBaseVelocity( - self.DRONE_IDS[0], INIT_VEL, INIT_ANG_VEL, physicsClientId=self.PYB_CLIENT - ) - - # Update BaseAviary internal variables before calling self._get_observation(). - self._update_and_store_kinematic_information() - obs, info = self._get_observation(), self._get_reset_info() - - # Return either an observation and dictionary or just the observation. - return obs, info - - def step(self, action): - """Advances the environment by one control step. - - Pass the commanded RPMs and the adversarial force to the superclass .step(). - The PyBullet simulation is stepped PYB_FREQ/CTRL_FREQ times in BaseAviary. - - Args: - action (ndarray): the action applied to the environment for the step. - - Returns: - ndarray: The state of the environment after the step. - float: The scalar reward/cost of the step. - bool: Whether the conditions for the end of an episode are met in the step. - dict: A dictionary with information about the constraints evaluations and violations. - - """ - # Get the preprocessed rpm for each motor - self.current_raw_input_action = action - rpm = self._preprocess_control(action) # Pre-process/clip the action - - # Determine disturbance force. - disturb_force = None - passive_disturb = "dynamics" in self.disturbances - if passive_disturb: - disturb_force = np.zeros(3) - disturb_force = self.disturbances["dynamics"].apply(disturb_force, self) - # Construct full (3D) disturbance force. - if disturb_force is not None: - disturb_force = np.asarray(disturb_force).flatten() - - # Advance the simulation. - super()._advance_simulation(rpm, disturb_force) - # Standard Gym return. - obs = self._get_observation() - info = self._get_info() - # IROS 2022 - After _get_info() to use this step's 'self' attributes. - done = self._get_done() - rew = self._get_reward() - obs, rew, done, info = super().after_step(obs, rew, done, info) - return obs, rew, done, info - - def render(self, mode="human"): - """Retrieves a frame from PyBullet rendering. - - Args: - mode (str): Unused. - - Returns: - ndarray: A multidimensional array with the RGB frame captured by PyBullet's camera. - - """ - [w, h, rgb, dep, seg] = p.getCameraImage( - width=self.RENDER_WIDTH, - height=self.RENDER_HEIGHT, - shadow=1, - viewMatrix=self.CAM_VIEW, - projectionMatrix=self.CAM_PRO, - renderer=p.ER_TINY_RENDERER, - flags=p.ER_SEGMENTATION_MASK_OBJECT_AND_LINKINDEX, - physicsClientId=self.PYB_CLIENT, - ) - return np.reshape(rgb, (h, w, 4)) - - def _setup_symbolic(self): - """Creates symbolic (CasADi) models for dynamics, observation, and cost. - - Returns: - SymbolicModel: CasADi symbolic model of the environment. - - """ - m, g, l = self.drone.params.mass, self.GRAVITY_ACC, self.L - Iyy = self.J[1, 1] - dt = 1 / self.sim_settings.ctrl_freq - # Define states. - z = cs.MX.sym("z") - z_dot = cs.MX.sym("z_dot") - - # Set up the dynamics model for a 3D quadrotor. - nx, nu = 12, 4 - Ixx = self.J[0, 0] - Izz = self.J[2, 2] - J = cs.blockcat([[Ixx, 0.0, 0.0], [0.0, Iyy, 0.0], [0.0, 0.0, Izz]]) - Jinv = cs.blockcat([[1.0 / Ixx, 0.0, 0.0], [0.0, 1.0 / Iyy, 0.0], [0.0, 0.0, 1.0 / Izz]]) - gamma = self.KM / self.KF - x = cs.MX.sym("x") - y = cs.MX.sym("y") - phi = cs.MX.sym("phi") # Roll - theta = cs.MX.sym("theta") # Pitch - psi = cs.MX.sym("psi") # Yaw - x_dot = cs.MX.sym("x_dot") - y_dot = cs.MX.sym("y_dot") - p = cs.MX.sym("p") # Body frame roll rate - q = cs.MX.sym("q") # body frame pith rate - r = cs.MX.sym("r") # body frame yaw rate - # PyBullet Euler angles use the SDFormat for rotation matrices. - Rob = csRotXYZ( - phi, theta, psi - ) # rotation matrix transforming a vector in the body frame to the world frame. - - # Define state variables. - X = cs.vertcat(x, x_dot, y, y_dot, z, z_dot, phi, theta, psi, p, q, r) - - # Define inputs. - f1 = cs.MX.sym("f1") - f2 = cs.MX.sym("f2") - f3 = cs.MX.sym("f3") - f4 = cs.MX.sym("f4") - U = cs.vertcat(f1, f2, f3, f4) - - # From Ch. 2 of Luis, Carlos, and Jérôme Le Ny. "Design of a trajectory tracking - # controller for a nanoquadcopter." arXiv preprint arXiv:1608.05786 (2016). - - # Defining the dynamics function. - # We are using the velocity of the base wrt to the world frame expressed in the world - # frame. Note that the reference expresses this in the body frame. - oVdot_cg_o = Rob @ cs.vertcat(0, 0, f1 + f2 + f3 + f4) / m - cs.vertcat(0, 0, g) - pos_ddot = oVdot_cg_o - pos_dot = cs.vertcat(x_dot, y_dot, z_dot) - Mb = cs.vertcat( - l / cs.sqrt(2.0) * (f1 + f2 - f3 - f4), - l / cs.sqrt(2.0) * (-f1 + f2 + f3 - f4), - gamma * (f1 - f2 + f3 - f4), - ) - rate_dot = Jinv @ (Mb - (cs.skew(cs.vertcat(p, q, r)) @ J @ cs.vertcat(p, q, r))) - ang_dot = cs.blockcat( - [ - [1, cs.sin(phi) * cs.tan(theta), cs.cos(phi) * cs.tan(theta)], - [0, cs.cos(phi), -cs.sin(phi)], - [0, cs.sin(phi) / cs.cos(theta), cs.cos(phi) / cs.cos(theta)], - ] - ) @ cs.vertcat(p, q, r) - X_dot = cs.vertcat( - pos_dot[0], - pos_ddot[0], - pos_dot[1], - pos_ddot[1], - pos_dot[2], - pos_ddot[2], - ang_dot, - rate_dot, - ) - - Y = cs.vertcat(x, x_dot, y, y_dot, z, z_dot, phi, theta, psi, p, q, r) - - # Define cost (quadratic form). - Q = cs.MX.sym("Q", nx, nx) - R = cs.MX.sym("R", nu, nu) - Xr = cs.MX.sym("Xr", nx, 1) - Ur = cs.MX.sym("Ur", nu, 1) - cost_func = 0.5 * (X - Xr).T @ Q @ (X - Xr) + 0.5 * (U - Ur).T @ R @ (U - Ur) - # Define dynamics and cost dictionaries. - dynamics = {"dyn_eqn": X_dot, "obs_eqn": Y, "vars": {"X": X, "U": U}} - cost = { - "cost_func": cost_func, - "vars": {"X": X, "U": U, "Xr": Xr, "Ur": Ur, "Q": Q, "R": R}, - } - # Setup symbolic model. - self.symbolic = SymbolicModel(dynamics=dynamics, cost=cost, dt=dt) - - def _preprocess_control(self, action): - """Converts the action passed to .step() into motors' RPMs (ndarray of shape (4,)). - - Args: - action (ndarray): The raw action input, of size 1 or 2 depending on QUAD_TYPE. - - Returns: - ndarray: The motors RPMs to apply to the quadrotor. - - """ - thrust = np.clip(action, self.action_space.low, self.action_space.high) - self.current_preprocessed_action = thrust - # Apply disturbances. - if "action" in self.disturbances: - thrust = self.disturbances["action"].apply(thrust, self) - # convert to quad motor rpm commands - pwm = cmd2pwm( - thrust, self.PWM2RPM_SCALE, self.PWM2RPM_CONST, self.KF, self.MIN_PWM, self.MAX_PWM - ) - rpm = pwm2rpm(pwm, self.PWM2RPM_SCALE, self.PWM2RPM_CONST) - return rpm - - def _get_observation(self): - """Returns the current observation (state) of the environment. - - Returns: - ndarray: The state of the quadrotor, of size 2 or 6 depending on QUAD_TYPE. - - """ - full_state = self._get_drone_state_vector(0) - pos, _, rpy, vel, ang_v, _ = np.split(full_state, [3, 7, 10, 13, 16]) - - Rob = np.array(p.getMatrixFromQuaternion(self.quat[0])).reshape((3, 3)) - Rbo = Rob.T - ang_v_body_frame = Rbo @ ang_v - # {x, x_dot, y, y_dot, z, z_dot, phi, theta, psi, p, q, r}. - self.state = np.hstack( - [pos[0], vel[0], pos[1], vel[1], pos[2], vel[2], rpy, ang_v_body_frame] - ).reshape((12,)) - # Apply observation disturbance. - obs = deepcopy(self.state) - if "observation" in self.disturbances: - obs = self.disturbances["observation"].apply(obs, self) - return {"pos": pos, "rpy": rpy, "vel": vel, "ang_vel": ang_v_body_frame} - return obs - - def _get_reward(self): - """Computes the current step's reward value. - - Returns: - float: The evaluated reward/cost. - - """ - # IROS 2022 - Competition sparse reward signal. - reward = 0 - # Reward for stepping through the (correct) next gate. - if self.stepped_through_gate: - reward += 100 - # Reward for reaching goal position (after navigating the gates in the correct order). - if self.at_goal_pos: - reward += 100 - # Penalize by collision. - if self.currently_collided: - reward -= 1000 - return reward - - def _get_done(self): - """Computes the conditions for termination of an episode. - - Returns: - bool: Whether an episode is over. - - """ - # Done if state is out-of-bounds. - if self.done_on_out_of_bound: - assert False - mask = np.array([1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0]) - # Element-wise or to check out-of-bound conditions. - out_of_bound = np.logical_or( - self.state < self.state_space.low, self.state > self.state_space.high - ) - # Mask out un-included dimensions (i.e. velocities) - out_of_bound = np.any(out_of_bound * mask) - # Early terminate if needed. - if out_of_bound: - return True - if self.currently_collided: - return True - - return False - - def _get_info(self): - """Generates the info dictionary returned by every call to .step(). - - Returns: - dict: A dictionary with information about the constraints evaluations and violations. - - """ - info = {} - # IROS 2022 - Per-step info. - # Collisions - for GATE_OBS_ID in self.GATES_IDS + self.OBSTACLES_IDS + [self.PLANE_ID]: - ret = p.getContactPoints( - bodyA=GATE_OBS_ID, - bodyB=self.DRONE_IDS[0], - physicsClientId=self.PYB_CLIENT, - ) - if ret: - info["collision"] = (GATE_OBS_ID, True) - self.currently_collided = True - break # Note: only returning the first collision per step. - else: - info["collision"] = (None, False) - self.currently_collided = False - # - # Gates progress (note: allow 0.5 seconds for initial drop if objects are not on the gound). - if ( - self.pyb_step_counter > 0.5 * self.sim_settings.sim_freq - and self.NUM_GATES > 0 - and self.current_gate < self.NUM_GATES - ): - x, y, _, _, _, rot = self._gates_pose[self.current_gate] - if self.GATES[self.current_gate][6] == 0: - height = self.GATE_Z_HIGH # URDF dependent. - elif self.GATES[self.current_gate][6] == 1: - height = self.GATE_Z_LOW # URDF dependent. - else: - raise ValueError("Unknown gate type.") - half_length = 0.1875 # Obstacle URDF dependent. - delta_x = 0.05 * np.cos(rot) - delta_y = 0.05 * np.sin(rot) - fr = [[x, y, height - half_length]] - to = [[x, y, height + half_length]] - for i in [1, 2, 3]: - fr.append([x + i * delta_x, y + i * delta_y, height - half_length]) - fr.append([x - i * delta_x, y - i * delta_y, height - half_length]) - to.append([x + i * delta_x, y + i * delta_y, height + half_length]) - to.append([x - i * delta_x, y - i * delta_y, height + half_length]) - rays = p.rayTestBatch( - rayFromPositions=fr, rayToPositions=to, physicsClientId=self.PYB_CLIENT - ) - self.stepped_through_gate = False - for r in rays: - if r[2] < 0.9999: - self.current_gate += 1 - self.stepped_through_gate = True - break - # LSY Drone Racing - # Always add the nominal gate positions. If any gates are in range, update the position. If - # any obstacles are in range, also update the obstacle positions. - VISIBILITY_RANGE = 0.45 - info["gates_pose"] = self.GATES[:, :6].copy() - info["gates_pose"][:, 2] = np.where(self.GATES[:, 6], self.GATE_Z_LOW, self.GATE_Z_HIGH) - info["obstacles_pose"] = self.OBSTACLES.copy() - info["obstacles_pose"][:, 2] = self.OBSTACLE_Z - info["gates_in_range"] = np.zeros(self.NUM_GATES, dtype=bool) - info["obstacles_in_range"] = np.zeros(self.n_obstacles, dtype=bool) - for i in range(self.NUM_GATES): - closest_points = p.getClosestPoints( - bodyA=self.GATES_IDS[i], - bodyB=self.DRONE_IDS[0], - distance=VISIBILITY_RANGE, - physicsClientId=self.PYB_CLIENT, - ) - if len(closest_points) > 0: - info["gates_pose"][i] = self._gates_pose[i] - info["gates_in_range"][i] = True - for i in range(self.n_obstacles): - closest_points = p.getClosestPoints( - bodyA=self.OBSTACLES_IDS[i], - bodyB=self.DRONE_IDS[0], - distance=VISIBILITY_RANGE, - physicsClientId=self.PYB_CLIENT, - ) - if len(closest_points) > 0: - info["obstacles_pose"][i] = self.obstacle_poses[i] - info["obstacles_in_range"][i] = True - - info["gates_type"] = self.GATES[:, 6] - info["current_gate_id"] = self.current_gate if self.current_gate < self.NUM_GATES else -1 - - # Final goal position reached - info["at_goal_position"] = False - info["task_completed"] = False - return info - - def _get_reset_info(self): - """Generates the info dictionary returned by every call to .reset(). - - Returns: - dict: A dictionary with information about the dynamics and constraints symbolic models. - - """ - info = {} - info["symbolic_model"] = self.symbolic - info["nominal_physical_parameters"] = { - "quadrotor_mass": self.drone.params.mass, - "quadrotor_ixx_inertia": self.J[0, 0], - "quadrotor_iyy_inertia": self.J[1, 1], - "quadrotor_izz_inertia": self.J[2, 2], - } - if self.constraints is not None: - info["symbolic_constraints"] = self.constraints.symbolic_model() - else: - info["symbolic_constraints"] = {} - - # IROS 2022 - Reset info. - info["ctrl_timestep"] = 1 / self.sim_settings.ctrl_freq - info["ctrl_freq"] = self.sim_settings.ctrl_freq - info["quadrotor_kf"] = self.KF - info["quadrotor_km"] = self.KM - info["gate_dimensions"] = { - "tall": {"shape": "square", "height": self.GATE_Z_HIGH, "edge": 0.45}, - "low": {"shape": "square", "height": self.GATE_Z_LOW, "edge": 0.45}, - } - info["obstacle_dimensions"] = { - "shape": "cylinder", - "height": self.OBSTACLE_Z, - "radius": 0.05, - } - info["nominal_gates_pos_and_type"] = self.GATES - info["nominal_obstacles_pos"] = self.OBSTACLES - - if self._enable_init_state_randomization: - info["initial_state_randomization"] = self._init_state_randomization - else: - info["initial_state_randomization"] = {} - if self._enable_inertial_randomization: - info["inertial_prop_randomization"] = self._inertial_randomization - else: - info["inertial_prop_randomization"] = {} - if self.RANDOMIZED_GATES_AND_OBS: - info["gates_and_obs_randomization"] = self.GATES_AND_OBS_RAND_INFO - else: - info["gates_and_obs_randomization"] = {} - info["disturbances"] = self._disturbance_config - info["pyb_client"] = self.PYB_CLIENT - info["urdf_dir"] = self.URDF_DIR - info["constraint_values"] = self.constraints.value(self.state, only_state=True) - - info.update(self._get_info()) - return info diff --git a/safe_control_gym/envs/quadrotor.yaml b/safe_control_gym/envs/quadrotor.yaml deleted file mode 100644 index b77c7443f..000000000 --- a/safe_control_gym/envs/quadrotor.yaml +++ /dev/null @@ -1,40 +0,0 @@ -# Simulation options -info_in_reset: False -ctrl_freq: 60 -pyb_freq: 240 -physics: pyb -gui: False -quad_type: 2 -normalized_rl_action_space: False -episode_len_sec: 5 -# State initialization -init_state: null -randomized_init: False -init_state_randomization_info: null -# Randomization -inertial_prop: null -randomized_inertial_prop: False -inertial_prop_randomization_info: null -# Task -task: stabilization -task_info: null -cost: rl_reward -# Disturbances -disturbances: null -adversary_disturbance: null -adversary_disturbance_offset: 0.0 -adversary_disturbance_scale: 0.01 -# Constraints -constraints: null -done_on_violation: False -use_constraint_penalty: False -constraint_penalty: -1 -# Misc -verbose: False -# RL Hyper-parameters -norm_act_scale: 0.1 -obs_goal_horizon: 0 -rew_state_weight: 1.0 -rew_act_weight: 0.0001 -rew_exponential: True -done_on_out_of_bound: True diff --git a/safe_control_gym/envs/quadrotor_utils.py b/safe_control_gym/envs/quadrotor_utils.py index 337de736a..96e6de5f7 100644 --- a/safe_control_gym/envs/quadrotor_utils.py +++ b/safe_control_gym/envs/quadrotor_utils.py @@ -1,93 +1,58 @@ -"""Helper functions for the quadrotor environment. +"""Helper functions for the quadrotor environment.""" -""" import math -from enum import IntEnum import numpy as np import pybullet as p from scipy.spatial.transform import Rotation +import numpy.typing as npt -class QuadType(IntEnum): - """Quadrotor types numeration class. - - """ - - ONE_D = 1 # One-dimensional (along z) movement. - TWO_D = 2 # Two-dimensional (in the x-z plane) movement. - THREE_D = 3 # Three-dimensional movement. - - -def cmd2pwm(thrust, pwm2rpm_scale, pwm2rpm_const, ct, pwm_min, pwm_max): - """Generic cmd to pwm function. - - For 1D, thrust is the total of all 4 motors; for 2D, 1st thrust is total of motor - 1 & 4, 2nd thrust is total of motor 2 & 3; for 4D, thrust is thrust of each motor. +def thrust2pwm( + thrust: npt.NDArray[np.float_], + pwm2rpm_scale: float, + pwm2rpm_const: float, + ct: float, + pwm_min: float, + pwm_max: float, +) -> npt.NDArray[np.float_]: + """Compute motor PWMs from thrust. Args: - thrust (ndarray): array of length 1, 2 containing target thrusts. - pwm2rpm_scale (float): scaling factor between PWM and RPMs. - pwm2rpm_const (float): constant factor between PWM and RPMs. - ct (float): torque coefficient. - pwm_min (float): pwm lower bound. - pwm_max (float): pwm upper bound. + thrust: The desired thrusts for each motor. + pwm2rpm_scale: Scaling factor between PWM and RPMs. + pwm2rpm_const: Constant factor between PWM and RPMs. + ct: Torque coefficient. + pwm_min: Minimum PWM. + pwm_max: Maximum PWM. Returns: - ndarray: array of length 4 containing PWM. - + The PWMs for each motor. """ - n_motor = 4 // int(thrust.size) thrust = np.clip(thrust, np.zeros_like(thrust), None) # Make sure thrust is not negative. - motor_pwm = (np.sqrt(thrust / n_motor / ct) - pwm2rpm_const) / pwm2rpm_scale - if thrust.size == 1: # 1D case. - motor_pwm = np.repeat(motor_pwm, 4) - elif thrust.size == 2: # 2D case. - motor_pwm = np.concatenate([motor_pwm, motor_pwm[::-1]], 0) - elif thrust.size == 4: # 3D case. - motor_pwm = np.array(motor_pwm) - else: - raise ValueError("Input action shape not supported.") - motor_pwm = np.clip(motor_pwm, pwm_min, pwm_max) - return motor_pwm - - -def pwm2rpm(pwm, pwm2rpm_scale, pwm2rpm_const): - """Computes motor squared rpm from pwm. - - Args: - pwm (ndarray): Array of length 4 containing PWM. - pwm2rpm_scale (float): Scaling factor between PWM and RPMs. - pwm2rpm_const (float): Constant factor between PWM and RPMs. - - Returns: - ndarray: Array of length 4 containing RPMs. - - """ - rpm = pwm2rpm_scale * pwm + pwm2rpm_const - return rpm - - -class PIDController(): - """PID control class for Crazyflies. - - """ - - def __init__(self, - g: float = 9.8, - m: float = 0.027, - kf: float = 3.16e-10, - km: float = 7.94e-12, - pwm2rpm_scale: float = 0.2685, - pwm2rpm_const: float = 4070.3, - min_pwm: float = 20000, - max_pwm: float = 65535, - p_coeff_for=np.array([.4, .4, 1.25]), - i_coeff_for=np.array([.05, .05, .05]), - d_coeff_for=np.array([.2, .2, .5]), - p_coeff_tor=np.array([70000., 70000., 60000.]), - i_coeff_tor=np.array([.0, .0, 500.]), - d_coeff_tor=np.array([20000., 20000., 12000.]), - ): + motor_pwm = (np.sqrt(thrust / ct) - pwm2rpm_const) / pwm2rpm_scale + return np.clip(motor_pwm, pwm_min, pwm_max) + + +class PIDController: + """PID control class for Crazyflies.""" + + def __init__( + self, + g: float = 9.81, + m: float = 0.027, + kf: float = 3.16e-10, + km: float = 7.94e-12, + pwm2rpm_scale: float = 0.2685, + pwm2rpm_const: float = 4070.3, + min_pwm: float = 20000, + max_pwm: float = 65535, + p_coeff_for=np.array([0.4, 0.4, 1.25]), + i_coeff_for=np.array([0.05, 0.05, 0.05]), + d_coeff_for=np.array([0.2, 0.2, 0.5]), + p_coeff_tor=np.array([70000.0, 70000.0, 60000.0]), + i_coeff_tor=np.array([0.0, 0.0, 500.0]), + d_coeff_tor=np.array([20000.0, 20000.0, 12000.0]), + ): """Common control classes __init__ method. Args: @@ -107,7 +72,7 @@ def __init__(self, d_coeff_tor (ndarray, optional): attitude derivative coefficients. """ - self.GRAVITY = g * m # The gravitational force (M*g) acting on each drone. + self.GRAVITY = g * m # The gravitational force (M*g) acting on each drone. self.KF = kf self.KM = km self.P_COEFF_FOR = p_coeff_for @@ -120,7 +85,9 @@ def __init__(self, self.PWM2RPM_CONST = pwm2rpm_const self.MIN_PWM = min_pwm self.MAX_PWM = max_pwm - self.MIXER_MATRIX = np.array([[.5, -.5, 1], [.5, .5, -1], [-.5, .5, 1], [-.5, -.5, -1]]) + self.MIXER_MATRIX = np.array( + [[0.5, -0.5, 1], [0.5, 0.5, -1], [-0.5, 0.5, 1], [-0.5, -0.5, -1]] + ) self.reset() def reset(self): @@ -136,17 +103,18 @@ def reset(self): self.last_rpy_e = np.zeros(3) self.integral_rpy_e = np.zeros(3) - def compute_control(self, - control_timestep, - cur_pos, - cur_quat, - cur_vel, - cur_ang_vel, - target_pos, - target_rpy=np.zeros(3), - target_vel=np.zeros(3), - target_rpy_rates=np.zeros(3) - ): + def compute_control( + self, + control_timestep, + cur_pos, + cur_quat, + cur_vel, + cur_ang_vel, + target_pos, + target_rpy=np.zeros(3), + target_vel=np.zeros(3), + target_rpy_rates=np.zeros(3), + ): """Computes the PID control action (as RPMs) for a single drone. This methods sequentially calls `_compute_force_and_euler()` and `_compute_rpms()`. @@ -170,32 +138,18 @@ def compute_control(self, """ self.control_counter += 1 - thrust, computed_target_rpy, pos_e = self._compute_force_and_euler(control_timestep, - cur_pos, - cur_quat, - cur_vel, - target_pos, - target_rpy, - target_vel - ) - rpm = self._compute_rpms(control_timestep, - thrust, - cur_quat, - computed_target_rpy, - target_rpy_rates - ) + thrust, computed_target_rpy, pos_e = self._compute_force_and_euler( + control_timestep, cur_pos, cur_quat, cur_vel, target_pos, target_rpy, target_vel + ) + rpm = self._compute_rpms( + control_timestep, thrust, cur_quat, computed_target_rpy, target_rpy_rates + ) cur_rpy = p.getEulerFromQuaternion(cur_quat) return rpm, pos_e, computed_target_rpy[2] - cur_rpy[2] - def _compute_force_and_euler(self, - control_timestep, - cur_pos, - cur_quat, - cur_vel, - target_pos, - target_rpy, - target_vel - ): + def _compute_force_and_euler( + self, control_timestep, cur_pos, cur_quat, cur_vel, target_pos, target_rpy, target_vel + ): """DSL's CF2.x PID position control. Args: @@ -216,33 +170,38 @@ def _compute_force_and_euler(self, cur_rotation = np.array(p.getMatrixFromQuaternion(cur_quat)).reshape(3, 3) pos_e = target_pos - cur_pos vel_e = target_vel - cur_vel - self.integral_pos_e = self.integral_pos_e + pos_e*control_timestep - self.integral_pos_e = np.clip(self.integral_pos_e, -2., 2.) - self.integral_pos_e[2] = np.clip(self.integral_pos_e[2], -0.15, .15) + self.integral_pos_e = self.integral_pos_e + pos_e * control_timestep + self.integral_pos_e = np.clip(self.integral_pos_e, -2.0, 2.0) + self.integral_pos_e[2] = np.clip(self.integral_pos_e[2], -0.15, 0.15) # PID target thrust. - target_thrust = np.multiply(self.P_COEFF_FOR, pos_e) \ - + np.multiply(self.I_COEFF_FOR, self.integral_pos_e) \ - + np.multiply(self.D_COEFF_FOR, vel_e) + np.array([0, 0, self.GRAVITY]) - scalar_thrust = max(0., np.dot(target_thrust, cur_rotation[:, 2])) - thrust = (math.sqrt(scalar_thrust / (4*self.KF)) - self.PWM2RPM_CONST) / self.PWM2RPM_SCALE + target_thrust = ( + np.multiply(self.P_COEFF_FOR, pos_e) + + np.multiply(self.I_COEFF_FOR, self.integral_pos_e) + + np.multiply(self.D_COEFF_FOR, vel_e) + + np.array([0, 0, self.GRAVITY]) + ) + scalar_thrust = max(0.0, np.dot(target_thrust, cur_rotation[:, 2])) + thrust = ( + math.sqrt(scalar_thrust / (4 * self.KF)) - self.PWM2RPM_CONST + ) / self.PWM2RPM_SCALE target_z_ax = target_thrust / np.linalg.norm(target_thrust) target_x_c = np.array([math.cos(target_rpy[2]), math.sin(target_rpy[2]), 0]) - target_y_ax = np.cross(target_z_ax, target_x_c) / np.linalg.norm(np.cross(target_z_ax, target_x_c)) + target_y_ax = np.cross(target_z_ax, target_x_c) / np.linalg.norm( + np.cross(target_z_ax, target_x_c) + ) target_x_ax = np.cross(target_y_ax, target_z_ax) target_rotation = (np.vstack([target_x_ax, target_y_ax, target_z_ax])).transpose() # Target rotation. - target_euler = (Rotation.from_matrix(target_rotation)).as_euler('XYZ', degrees=False) + target_euler = (Rotation.from_matrix(target_rotation)).as_euler("XYZ", degrees=False) if np.any(np.abs(target_euler) > math.pi): - raise ValueError("\n[ERROR] ctrl it", self.control_counter, "in Control._compute_force_and_euler(), values outside range [-pi,pi]") + raise ValueError( + "\n[ERROR] ctrl it", + self.control_counter, + "in Control._compute_force_and_euler(), values outside range [-pi,pi]", + ) return thrust, target_euler, pos_e - def _compute_rpms(self, - control_timestep, - thrust, - cur_quat, - target_euler, - target_rpy_rates - ): + def _compute_rpms(self, control_timestep, thrust, cur_quat, target_euler, target_rpy_rates): """DSL's CF2.x PID attitude control. Args: @@ -258,20 +217,24 @@ def _compute_rpms(self, """ cur_rotation = np.array(p.getMatrixFromQuaternion(cur_quat)).reshape(3, 3) cur_rpy = np.array(p.getEulerFromQuaternion(cur_quat)) - target_quat = (Rotation.from_euler('XYZ', target_euler, degrees=False)).as_quat() + target_quat = (Rotation.from_euler("XYZ", target_euler, degrees=False)).as_quat() w, x, y, z = target_quat target_rotation = (Rotation.from_quat([w, x, y, z])).as_matrix() - rot_matrix_e = np.dot((target_rotation.transpose()), cur_rotation) - np.dot(cur_rotation.transpose(), target_rotation) + rot_matrix_e = np.dot((target_rotation.transpose()), cur_rotation) - np.dot( + cur_rotation.transpose(), target_rotation + ) rot_e = np.array([rot_matrix_e[2, 1], rot_matrix_e[0, 2], rot_matrix_e[1, 0]]) - rpy_rates_e = target_rpy_rates - (cur_rpy - self.last_rpy)/control_timestep + rpy_rates_e = target_rpy_rates - (cur_rpy - self.last_rpy) / control_timestep self.last_rpy = cur_rpy - self.integral_rpy_e = self.integral_rpy_e - rot_e*control_timestep - self.integral_rpy_e = np.clip(self.integral_rpy_e, -1500., 1500.) - self.integral_rpy_e[0:2] = np.clip(self.integral_rpy_e[0:2], -1., 1.) + self.integral_rpy_e = self.integral_rpy_e - rot_e * control_timestep + self.integral_rpy_e = np.clip(self.integral_rpy_e, -1500.0, 1500.0) + self.integral_rpy_e[0:2] = np.clip(self.integral_rpy_e[0:2], -1.0, 1.0) # PID target torques. - target_torques = - np.multiply(self.P_COEFF_TOR, rot_e) \ - + np.multiply(self.D_COEFF_TOR, rpy_rates_e) \ - + np.multiply(self.I_COEFF_TOR, self.integral_rpy_e) + target_torques = ( + -np.multiply(self.P_COEFF_TOR, rot_e) + + np.multiply(self.D_COEFF_TOR, rpy_rates_e) + + np.multiply(self.I_COEFF_TOR, self.integral_rpy_e) + ) target_torques = np.clip(target_torques, -3200, 3200) pwm = thrust + np.dot(self.MIXER_MATRIX, target_torques) pwm = np.clip(pwm, self.MIN_PWM, self.MAX_PWM) diff --git a/safe_control_gym/envs/symbolic.py b/safe_control_gym/envs/symbolic.py new file mode 100644 index 000000000..31e470619 --- /dev/null +++ b/safe_control_gym/envs/symbolic.py @@ -0,0 +1,94 @@ +import casadi as cs +from safe_control_gym.math_and_models.symbolic_systems import SymbolicModel +from safe_control_gym.envs.physics import Constants +from safe_control_gym.envs.drone import Drone +from safe_control_gym.math_and_models.transformations import csRotXYZ + + +def symbolic(drone: Drone, dt: float) -> SymbolicModel: + """Create symbolic (CasADi) models for dynamics, observation, and cost of a quadcopter. + + Returns: + The CasADi symbolic model of the environment. + """ + m, g = drone.nominal_params.mass, Constants.GRAVITY + # Define states. + z = cs.MX.sym("z") + z_dot = cs.MX.sym("z_dot") + + # Set up the dynamics model for a 3D quadrotor. + nx, nu = 12, 4 + Ixx, Iyy, Izz = drone.nominal_params.J.diagonal() + J = cs.blockcat([[Ixx, 0.0, 0.0], [0.0, Iyy, 0.0], [0.0, 0.0, Izz]]) + Jinv = cs.blockcat([[1.0 / Ixx, 0.0, 0.0], [0.0, 1.0 / Iyy, 0.0], [0.0, 0.0, 1.0 / Izz]]) + gamma = drone.nominal_params.km / drone.nominal_params.kf + x = cs.MX.sym("x") + y = cs.MX.sym("y") + phi = cs.MX.sym("phi") # Roll + theta = cs.MX.sym("theta") # Pitch + psi = cs.MX.sym("psi") # Yaw + x_dot = cs.MX.sym("x_dot") + y_dot = cs.MX.sym("y_dot") + p = cs.MX.sym("p") # Body frame roll rate + q = cs.MX.sym("q") # body frame pith rate + r = cs.MX.sym("r") # body frame yaw rate + # Rotation matrix transforming a vector in the body frame to the world frame. PyBullet Euler + # angles use the SDFormat for rotation matrices. + Rob = csRotXYZ(phi, theta, psi) + # Define state variables. + X = cs.vertcat(x, x_dot, y, y_dot, z, z_dot, phi, theta, psi, p, q, r) + # Define inputs. + f1 = cs.MX.sym("f1") + f2 = cs.MX.sym("f2") + f3 = cs.MX.sym("f3") + f4 = cs.MX.sym("f4") + U = cs.vertcat(f1, f2, f3, f4) + + # From Ch. 2 of Luis, Carlos, and Jérôme Le Ny. "Design of a trajectory tracking + # controller for a nanoquadcopter." arXiv preprint arXiv:1608.05786 (2016). + + # Defining the dynamics function. + # We are using the velocity of the base wrt to the world frame expressed in the world frame. + # Note that the reference expresses this in the body frame. + oVdot_cg_o = Rob @ cs.vertcat(0, 0, f1 + f2 + f3 + f4) / m - cs.vertcat(0, 0, g) + pos_ddot = oVdot_cg_o + pos_dot = cs.vertcat(x_dot, y_dot, z_dot) + Mb = cs.vertcat( + drone.nominal_params.arm_len / cs.sqrt(2.0) * (f1 + f2 - f3 - f4), + drone.nominal_params.arm_len / cs.sqrt(2.0) * (-f1 + f2 + f3 - f4), + gamma * (f1 - f2 + f3 - f4), + ) + rate_dot = Jinv @ (Mb - (cs.skew(cs.vertcat(p, q, r)) @ J @ cs.vertcat(p, q, r))) + ang_dot = cs.blockcat( + [ + [1, cs.sin(phi) * cs.tan(theta), cs.cos(phi) * cs.tan(theta)], + [0, cs.cos(phi), -cs.sin(phi)], + [0, cs.sin(phi) / cs.cos(theta), cs.cos(phi) / cs.cos(theta)], + ] + ) @ cs.vertcat(p, q, r) + X_dot = cs.vertcat( + pos_dot[0], + pos_ddot[0], + pos_dot[1], + pos_ddot[1], + pos_dot[2], + pos_ddot[2], + ang_dot, + rate_dot, + ) + + Y = cs.vertcat(x, x_dot, y, y_dot, z, z_dot, phi, theta, psi, p, q, r) + + # Define cost (quadratic form). + Q = cs.MX.sym("Q", nx, nx) + R = cs.MX.sym("R", nu, nu) + Xr = cs.MX.sym("Xr", nx, 1) + Ur = cs.MX.sym("Ur", nu, 1) + cost_func = 0.5 * (X - Xr).T @ Q @ (X - Xr) + 0.5 * (U - Ur).T @ R @ (U - Ur) + # Define dynamics and cost dictionaries. + dynamics = {"dyn_eqn": X_dot, "obs_eqn": Y, "vars": {"X": X, "U": U}} + cost = { + "cost_func": cost_func, + "vars": {"X": X, "U": U, "Xr": Xr, "Ur": Ur, "Q": Q, "R": R}, + } + return SymbolicModel(dynamics=dynamics, cost=cost, dt=dt) diff --git a/safe_control_gym/envs/utils.py b/safe_control_gym/envs/utils.py index ee05762e1..4ac6b1182 100644 --- a/safe_control_gym/envs/utils.py +++ b/safe_control_gym/envs/utils.py @@ -1,3 +1,6 @@ -def update_properties(obj, **kwargs): - for k, v in kwargs.items(): - setattr(obj, k, v) +import numpy as np +import numpy.typing as npt + + +def map2pi(x: npt.NDArray[np.float_]) -> npt.NDArray[np.float_]: + return (x + np.pi) % (2 * np.pi) - np.pi diff --git a/safe_control_gym/math_and_models/symbolic_systems.py b/safe_control_gym/math_and_models/symbolic_systems.py index 9c033f7d1..dab79d046 100644 --- a/safe_control_gym/math_and_models/symbolic_systems.py +++ b/safe_control_gym/math_and_models/symbolic_systems.py @@ -1,20 +1,18 @@ """Symbolic Models.""" -import numpy as np import casadi as cs class SymbolicModel: """Implements the dynamics model with symbolic variables. - x_dot = f(x,u), y = g(x,u), with other pre-defined, symbolic functions - (e.g. cost, constraints), serve as priors for the controllers. + x_dot = f(x,u), y = g(x,u), with other pre-defined, symbolic functions (e.g. cost, constraints), + serve as priors for the controllers. Notes: * naming convention on symbolic variable and functions. * for single-letter symbol, use {}_sym, otherwise use underscore for delimiter. * for symbolic functions to be exposed, use {}_func. - """ def __init__(self, dynamics, cost, dt=1e-3, integration_algo="cvodes", funcs=None): diff --git a/safe_control_gym/math_and_models/transformations.py b/safe_control_gym/math_and_models/transformations.py index 971802a94..d3ae3ece6 100644 --- a/safe_control_gym/math_and_models/transformations.py +++ b/safe_control_gym/math_and_models/transformations.py @@ -1,199 +1,82 @@ """Transformations with NumPy Based on github.com/bulletphysics/bullet3/blob/master/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/transformation.py + +For SDFormat specification, see http://sdformat.org/tutorials?tut=specify_pose&cat=specification& """ -import math -import numpy as np import casadi as cs +import numpy as np +import numpy.typing as npt -def unit_vector(data, axis=None, out=None): - """Return ndarray normalized by length, i.e. Euclidean norm, along axis. - >>> v0 = np.random.random(3) - >>> v1 = unit_vector(v0) - >>> np.allclose(v1, v0 / np.linalg.norm(v0)) - True - >>> v0 = np.random.rand(5, 4, 3) - >>> v1 = unit_vector(v0, axis=-1) - >>> v2 = v0 / np.expand_dims(np.sqrt(np.sum(v0*v0, axis=2)), 2) - >>> np.allclose(v1, v2) - True - >>> v1 = unit_vector(v0, axis=1) - >>> v2 = v0 / np.expand_dims(np.sqrt(np.sum(v0*v0, axis=1)), 1) - >>> np.allclose(v1, v2) - True - >>> v1 = np.empty((5, 4, 3)) - >>> unit_vector(v0, axis=1, out=v1) - >>> np.allclose(v1, v2) - True - >>> list(unit_vector([])) - [] - >>> list(unit_vector([1])) - [1.0] - """ - if out is None: - data = np.array(data, dtype=np.float64, copy=True) - if data.ndim == 1: - data /= math.sqrt(np.dot(data, data)) - return data - else: - if out is not data: - out[:] = np.array(data, copy=False) - data = out - length = np.atleast_1d(np.sum(data * data, axis)) - np.sqrt(length, length) - if axis is not None: - length = np.expand_dims(length, axis) - data /= length - if out is None: - return data - - -def projection_matrix(point, normal, direction=None, perspective=None, pseudo=False): - """Return matrix to project onto plane defined by point and normal. - Using either perspective point, projection direction, or none of both. - If pseudo is True, perspective projections will preserve relative depth - such that Perspective = dot(Orthogonal, PseudoPerspective). - >>> P = projection_matrix([0, 0, 0], [1, 0, 0]) - >>> np.allclose(P[1:, 1:], np.identity(4)[1:, 1:]) - True - >>> point = np.random.random(3) - 0.5 - >>> normal = np.random.random(3) - 0.5 - >>> direct = np.random.random(3) - 0.5 - >>> persp = np.random.random(3) - 0.5 - >>> P0 = projection_matrix(point, normal) - >>> P1 = projection_matrix(point, normal, direction=direct) - >>> P2 = projection_matrix(point, normal, perspective=persp) - >>> P3 = projection_matrix(point, normal, perspective=persp, pseudo=True) - >>> is_same_transform(P2, np.dot(P0, P3)) - True - >>> P = projection_matrix([3, 0, 0], [1, 1, 0], [1, 0, 0]) - >>> v0 = (np.random.rand(4, 5) - 0.5) * 20 - >>> v0[3] = 1 - >>> v1 = np.dot(P, v0) - >>> np.allclose(v1[1], v0[1]) - True - >>> np.allclose(v1[0], 3-v1[1]) - True - """ - M = np.identity(4) - point = np.array(point[:3], dtype=np.float64, copy=False) - normal = unit_vector(normal[:3]) - if perspective is not None: - # perspective projection - perspective = np.array(perspective[:3], dtype=np.float64, copy=False) - M[0, 0] = M[1, 1] = M[2, 2] = np.dot(perspective - point, normal) - M[:3, :3] -= np.outer(perspective, normal) - if pseudo: - # preserve relative depth - M[:3, :3] -= np.outer(normal, normal) - M[:3, 3] = np.dot(point, normal) * (perspective + normal) - else: - M[:3, 3] = np.dot(point, normal) * perspective - M[3, :3] = -normal - M[3, 3] = np.dot(perspective, normal) - elif direction is not None: - # parallel projection - direction = np.array(direction[:3], dtype=np.float64, copy=False) - scale = np.dot(direction, normal) - M[:3, :3] -= np.outer(direction, normal) / scale - M[:3, 3] = direction * (np.dot(point, normal) / scale) - else: - # orthogonal projection - M[:3, :3] -= np.outer(normal, normal) - M[:3, 3] = np.dot(point, normal) * normal - return M - - -def transform_trajectory(pos, vel, trans_info={}): - """Makes 2D reference trajectory into a 3D one. - - Args: - pos: position in the reference trajectory, with shape (T,3). - vel: velocity in the reference trajectory, with shape (T,3). - - """ - # Shape (4,4) with augmented last dim (always 1). - M = projection_matrix(trans_info["point"], trans_info["normal"]) - # Position. - aug_pos = np.concatenate([pos, np.ones((pos.shape[0], 1))], -1) # (T,4) - trans_pos = np.matmul(aug_pos, M.transpose())[:, :3] # (T,3) - # Velocity (transfomration is linear, direclty multiply for derivatives). - aug_vel = np.concatenate([vel, np.ones((vel.shape[0], 1))], -1) # (T,4) - trans_vel = np.matmul(aug_vel, M.transpose())[:, :3] # (T,3) - return trans_pos, trans_vel - - -def csRotZ(psi): - """Rotation matrix about Z axis following SDFormat http://sdformat.org/tutorials?tut=specify_pose&cat=specification&. +def csRotZ(psi: float) -> cs.MX: + """Rotation matrix about Z axis following SDFormat. Args: - psi: Scalar rotation + psi: Scalar rotation. Returns: - R: casadi Rotation matrix + R: casadi Rotation matrix """ - R = cs.blockcat([[cs.cos(psi), -cs.sin(psi), 0], [cs.sin(psi), cs.cos(psi), 0], [0, 0, 1]]) - return R + return cs.blockcat([[cs.cos(psi), -cs.sin(psi), 0], [cs.sin(psi), cs.cos(psi), 0], [0, 0, 1]]) -def csRotY(theta): - """Rotation matrix about Y axis following SDFormat http://sdformat.org/tutorials?tut=specify_pose&cat=specification&. +def csRotY(theta: float) -> cs.MX: + """Rotation matrix about Y axis following SDFormat. . Args: - theta: Scalar rotation + theta: Scalar rotation Returns: - R: casadi Rotation matrix + R: casadi Rotation matrix """ - R = cs.blockcat( + return cs.blockcat( [[cs.cos(theta), 0, cs.sin(theta)], [0, 1, 0], [-cs.sin(theta), 0, cs.cos(theta)]] ) - return R -def csRotX(phi): - """Rotation matrix about X axis following SDFormat http://sdformat.org/tutorials?tut=specify_pose&cat=specification&. +def csRotX(phi: float) -> cs.MX: + """Rotation matrix about X axis. Args: - phi: Scalar rotation + phi: Scalar rotation Returns: - R: casadi Rotation matrix + R: casadi Rotation matrix """ - R = cs.blockcat([[1, 0, 0], [0, cs.cos(phi), -cs.sin(phi)], [0, cs.sin(phi), cs.cos(phi)]]) - return R + return cs.blockcat([[1, 0, 0], [0, cs.cos(phi), -cs.sin(phi)], [0, cs.sin(phi), cs.cos(phi)]]) + +def csRotXYZ(phi: float, theta: float, psi: float) -> cs.MX: + """Rotation matrix from euler angles. -def csRotXYZ(phi, theta, psi): - """Rotation matrix from euller angles following SDFormat http://sdformat.org/tutorials?tut=specify_pose&cat=specification&. - This represents the extrinsic X-Y-Z (or quivalently the intrinsic Z-Y-X (3-2-1)) euler angle rotation. + This represents the extrinsic X-Y-Z (or quivalently the intrinsic Z-Y-X (3-2-1)) euler angle + rotation. Args: - phi: roll (or rotation about X). - theta: pitch (or rotation about Y). - psi: yaw (or rotation about Z). + phi: roll (or rotation about X). + theta: pitch (or rotation about Y). + psi: yaw (or rotation about Z). Returns: - R: casadi Rotation matrix + R: casadi Rotation matrix """ - R = csRotZ(psi) @ csRotY(theta) @ csRotX(phi) + return csRotZ(psi) @ csRotY(theta) @ csRotX(phi) - return R +def RotXYZ(phi: float, theta: float, psi: float) -> npt.NDArray[np.float_]: + """Rotation matrix from euler angles as numpy array. -def RotXYZ(phi, theta, psi): - """Rotation matrix from euller angles following SDFormat http://sdformat.org/tutorials?tut=specify_pose&cat=specification&. - This represents the extrinsic X-Y-Z (or quivalently the intrinsic Z-Y-X (3-2-1)) euler angle rotation. + This represents the extrinsic X-Y-Z (or quivalently the intrinsic Z-Y-X (3-2-1)) euler angle + rotation. Args: - phi: roll (or rotation about X). - theta: pitch (or rotation about Y). - psi: yaw (or rotation about Z). + phi: roll (or rotation about X). + theta: pitch (or rotation about Y). + psi: yaw (or rotation about Z). Returns: - R: casadi Rotation matrix + R: casadi Rotation matrix """ - R = csRotXYZ(phi, theta, psi).toarray() - return R + return csRotXYZ(phi, theta, psi).toarray() diff --git a/safe_control_gym/utils/registration.py b/safe_control_gym/utils/registration.py deleted file mode 100644 index ede0b8802..000000000 --- a/safe_control_gym/utils/registration.py +++ /dev/null @@ -1,145 +0,0 @@ -"""Environments and controllers registration. - -Based on https://github.com/openai/gym/blob/master/gym/envs/registration.py - -""" - -import copy -import yaml -import importlib - -try: - import importlib.resources as pkg_resources -except ImportError: - import importlib_resources as pkg_resources # Try backported to PY<37 `importlib_resources`. - - -def load(name): - """Loads a callable from a string with format `module_path:callable_name`.""" - mod_name, attr_name = name.split(":") - mod = importlib.import_module(mod_name) - fn = getattr(mod, attr_name) - return fn - - -class Spec: - """A specification for a particular instance of the environment.""" - - def __init__(self, id, entry_point=None, config_entry_point=None): - """Used in function "register". - - Args: - id (str): The official environment ID - entry_point (cls, str): The Python entrypoint of the environment class. - config_entry_point (str): The config to pass to the environment class. - - """ - self.id = id - self.entry_point = entry_point - self.config_entry_point = config_entry_point - - def __repr__(self): - """Defines a class instance's string representation.""" - return "Spec({})".format(self.id) - - def get_config(self): - """Fetches config (as dict) for the registered class.""" - if isinstance(self.config_entry_point, str): - if self.config_entry_point.endswith(".yaml"): - # Specified as file path. - mod_name, config_name = self.config_entry_point.split(":") - with pkg_resources.open_text(mod_name, config_name) as f: - config = yaml.load(f, Loader=yaml.FullLoader) - else: - # Specified as "module_path:config_dict_name". - config = load(self.config_entry_point) - elif self.config_entry_point is None: - # No default config. - config = {} - else: - raise Exception("Config type {} is not supported.".format(self.config_entry_point)) - return config - - def make(self, *args, **kwargs): - """Instantiates an instance of the registered class with appropriate kwargs.""" - if self.entry_point is None: - raise Exception("Attempting to make deprecated env {}.".format(self.id)) - if callable(self.entry_point): - obj = self.entry_point(*args, **kwargs) - else: - cls = load(self.entry_point) - obj = cls(*args, **kwargs) - # Make the instance aware of which spec it came from. - spec = copy.deepcopy(self) - if hasattr(obj, "spec"): - obj.instance_spec = spec - else: - obj.spec = spec - return obj - - -class Registry: - """Register a callable by ID.""" - - def __init__(self): - self.specs = {} - - def make(self, path, *args, **kwargs): - """Create an instance of the registered callable by id `path`.""" - spec = self.specs[path] - obj = spec.make(*args, **kwargs) - return obj - - def all(self): - """Returns all registered callables.""" - return self.specs.values() - - def spec(self, path): - """Returns spec of the registered callable by id.""" - if ":" in path: - mod_name, id = path.split(":") - try: - importlib.import_module(mod_name) - except: - raise Exception( - """A module ({}) was specified for the environment but was not found,make sure the - package is installed with `pip install` before calling `gym.make()`""".format( - mod_name - ) - ) - else: - id = path - try: - return self.specs[id] - except: - raise Exception("Key not found in registry.") - - def register(self, id, **kwargs): - """Saves a reference to the callable with a id.""" - if id in self.specs: - raise Exception("Cannot re-register id: {}".format(id)) - self.specs[id] = Spec(id, **kwargs) - - -def register(id, **kwargs): - """Saves the callable to the global registry.""" - return registry.register(id, **kwargs) - - -def make(id, *args, **kwargs): - """Creates an instance of the callable from global registry.""" - return registry.make(id, *args, **kwargs) - - -def spec(id): - """Gets the spec of the callable from global registry.""" - return registry.spec(id) - - -def get_config(id): - """Gets the config of the callable from global registry.""" - return registry.spec(id).get_config() - - -# Registry: global registry (singleton) used by functions `register`, `make`, `spec` and `get_config`. -registry = Registry() diff --git a/safe_control_gym/utils/__init__.py b/tests/__init__.py similarity index 100% rename from safe_control_gym/utils/__init__.py rename to tests/__init__.py diff --git a/tests/integration_tests/config/test_sim.toml b/tests/integration_tests/config/test_sim.toml new file mode 100644 index 000000000..040d78a22 --- /dev/null +++ b/tests/integration_tests/config/test_sim.toml @@ -0,0 +1,99 @@ +[sim] +physics = "pyb" # pyb: PyBullet, dyn: Mathematical dynamics model, TODO: Complete the list +camera_view = [5.0, -40.0, -40.0, 0.5, -1.0, 0.5] +sim_freq = 500 # Simulation frequency, in Hz +ctrl_freq = 500 # Controller frequency, in Hz. This frequency is used to simulate the onboard controller, NOT for the environment's step function +gui = false # Enable/disable PyBullet's GUI + +[env] +reseed = false # Whether to re-seed the random number generator between episodes +seed = 1337 # Random seed +freq = 30 # Frequency of the environment's step function, in Hz +symbolic = false # Whether to include symbolic expressions in the info dict. Note: This can interfere with multiprocessing! If you want to parallelize your training, set this to false. + +[env.track] +# Tall gates: 1.0m height. Short gates: 0.525m height. Height is measured from the ground to the +# center of the gate. +[[env.track.gates]] +pos = [0.45, -1.0, 0.525] +rpy = [0.0, 0.0, 2.35] +[[env.track.gates]] +pos = [1.0, -1.55, 1.0] +rpy = [0.0, 0.0, -0.78] +[[env.track.gates]] +pos = [0.0, 0.5, 0.525] +rpy = [0.0, 0.0, 0.0] +[[env.track.gates]] +pos = [-0.5, -0.5, 1.0] +rpy = [0.0, 0.0, 3.14] + +# Obstacle height: 1.05m. Height is measured from the ground to the top of the obstacle. +[[env.track.obstacles]] +pos = [1.0, -0.5, 1.05] +[[env.track.obstacles]] +pos = [0.5, -1.5, 1.05] +[[env.track.obstacles]] +pos = [-0.5, 0.0, 1.05] +[[env.track.obstacles]] +pos = [0.0, 1.0, 1.05] + +[env.track.drone] +pos = [1.0, 1.0, 0.05] +rpy = [0, 0, 0] +vel = [0, 0, 0] +ang_vel = [0, 0, 0] + +[env.randomization.drone_pos] +type = "uniform" # Everything that can be used as a distribution in numpy.random +# Kwargs that are permissable in the np random function +low = [-0.1, -0.1, 0.0] +high = [0.1, 0.1, 0.02] + +[env.randomization.drone_rpy] +type = "uniform" +low = [-0.1, -0.1, -0.1] +high = [0.1, 0.1, 0.1] + +[env.randomization.drone_mass] +type = "uniform" +low = -0.01 +high = 0.01 + +[env.randomization.drone_inertia] +type = "uniform" +low = [-0.000001, -0.000001, -0.000001] +high = [0.000001, 0.000001, 0.000001] + +[env.randomization.gate_pos] +type = "uniform" +low = [-0.1, -0.1, 0.0] +high = [0.1, 0.1, 0.0] + +[env.randomization.gate_rpy] +type = "uniform" +low = [0.0, 0.0, -0.1] +high = [0.0, 0.0, 0.1] + +[env.randomization.obstacle_pos] +type = "uniform" +low = [-0.1, -0.1, 0.0] +high = [0.1, 0.1, 0.0] + +[[env.constraints]] +type = "DefaultConstraint" +ctype = "input" +[[env.constraints]] +type = "BoundedConstraint" +ctype = "state" +active_dims = [0, 2, 4] +upper_bounds = [3, 3, 2] +lower_bounds = [-3.0, -3.0, -0.1] + +[env.disturbances.action] +type = "WhiteNoise" +std = 0.001 + +[env.disturbances.dynamics] +type = "UniformNoise" +low = [-0.1, -0.1, -0.1] +high = [0.1, 0.1, 0.1] diff --git a/tests/integration_tests/test_sim.py b/tests/integration_tests/test_sim.py new file mode 100644 index 000000000..32729ff70 --- /dev/null +++ b/tests/integration_tests/test_sim.py @@ -0,0 +1,21 @@ +from safe_control_gym.envs.physics import PhysicsMode +import gymnasium +import pytest +from pathlib import Path +import toml +from functools import partial +from munch import munchify +from lsy_drone_racing.constants import FIRMWARE_FREQ + + +@pytest.mark.parametrize("physics", PhysicsMode) +def test_sim(physics: PhysicsMode): + with open(Path(__file__).parent / "config/test_sim.toml") as f: + config = munchify(toml.load(f)) + config.sim.physics = physics # override physics mode + env = gymnasium.make("drone_racing-v0", config=config) + env.reset() + while True: + _, _, terminated, truncated, _ = env.step(env.action_space.sample()) + if terminated or truncated: + break diff --git a/tests/scripts/tracking.py b/tests/scripts/tracking.py deleted file mode 100644 index 9b7904a4d..000000000 --- a/tests/scripts/tracking.py +++ /dev/null @@ -1,84 +0,0 @@ -"""A quadrotor trajectory tracking example. - -Notes: - Includes and uses PID control. - -Run as: - - $ python3 tracking.py --overrides ./tracking.yaml - -""" -import time -import pybullet as p -from functools import partial - -from safe_control_gym.utils.configuration import ConfigFactory -from safe_control_gym.utils.registration import make - -def run(gui=None, max_steps=None): - """The main function creating, running, and closing an environment. - - """ - - # Create an environment - CONFIG_FACTORY = ConfigFactory() - config = CONFIG_FACTORY.merge() - - # Set iterations and episode counter. - ITERATIONS = int(config.quadrotor_config['episode_len_sec']*config.quadrotor_config['ctrl_freq']) - - # Use function arguments for workflow testing - if gui is not None: - config.quadrotor_config['gui'] = gui - if max_steps is not None: - ITERATIONS = min(ITERATIONS, max_steps) - - for i in range(3): - # Start a timer. - START = time.time() - - if i == 1: - config.quadrotor_config['task_info']['trajectory_type'] = 'circle' - elif i == 2: - config.quadrotor_config['task_info']['trajectory_type'] = 'square' - - # Create controller. - env_func = partial(make, - 'quadrotor', - **config.quadrotor_config - ) - ctrl = make('pid', - env_func, - ) - - reference_traj = ctrl.reference - - # Plot trajectory. - if config.quadrotor_config['gui']: - for i in range(0, reference_traj.shape[0], 10): - p.addUserDebugLine(lineFromXYZ=[reference_traj[i-10,0], 0, reference_traj[i-10,2]], - lineToXYZ=[reference_traj[i,0], 0, reference_traj[i,2]], - lineColorRGB=[1, 0, 0], - physicsClientId=ctrl.env.PYB_CLIENT) - - # Run the experiment. - results = ctrl.run(iterations=ITERATIONS) - - # Plot the experiment. - for i in range(ITERATIONS): - # Step the environment and print all returned information. - obs, reward, done, info, action = results['obs'][i], results['reward'][i], results['done'][i], results['info'][i], results['action'][i] - - # Print the last action and the information returned at each step. - print(i, '-th step.') - print(action, '\n', obs, '\n', reward, '\n', done, '\n', info, '\n') - - ctrl.close() - - elapsed_sec = time.time() - START - print("\n{:d} iterations (@{:d}Hz) and {:d} episodes in {:.2f} seconds, i.e. {:.2f} steps/sec for a {:.2f}x speedup.\n" - .format(ITERATIONS, config.quadrotor_config.ctrl_freq, 1, elapsed_sec, ITERATIONS/elapsed_sec, (ITERATIONS*(1. / config.quadrotor_config.ctrl_freq))/elapsed_sec)) - - -if __name__ == "__main__": - run() \ No newline at end of file diff --git a/tests/scripts/tracking.yaml b/tests/scripts/tracking.yaml deleted file mode 100644 index 86eb0c0ab..000000000 --- a/tests/scripts/tracking.yaml +++ /dev/null @@ -1,29 +0,0 @@ -quadrotor_config: - seed: 1337 - info_in_reset: True - ctrl_freq: 60 - pyb_freq: 240 - gui: True - physics: pyb - quad_type: 2 - init_state: - init_x: 0 - init_x_dot: 0 - init_z: 1 - init_z_dot: 0 - init_theta: 0 - init_theta_dot: 0 - randomized_init: False - randomized_inertial_prop: False - task: traj_tracking - task_info: - stabilization_goal: [0, 1] - stabilization_goal_tolerance: 0.01 - trajectory_type: figure8 - num_cycles: 1 - trajectory_plane: "xz" - trajectory_position_offset: [0, 1] - trajectory_scale: 1 - episode_len_sec: 6 - cost: quadratic - done_on_out_of_bound: False diff --git a/tests/scripts/verbose_api.py b/tests/scripts/verbose_api.py deleted file mode 100644 index 09472a9c1..000000000 --- a/tests/scripts/verbose_api.py +++ /dev/null @@ -1,156 +0,0 @@ -"""A simple script to demonstrate safe-control-gym API. - -Example: - - $ python3 verbose_api.py --task quadrotor --overrides verbose_api.yaml - -""" -import time -import inspect -import pybullet as p - -from safe_control_gym.utils.configuration import ConfigFactory -from safe_control_gym.utils.registration import make - - -def run(): - """The main function creating, running, and closing an environment. - - """ - # Set iterations and episode counter. - num_episodes = 1 - ITERATIONS = int(2) - # Start a timer. - START = time.time() - # Create an environment - CONFIG_FACTORY = ConfigFactory() - config = CONFIG_FACTORY.merge() - if config.task == 'cartpole': - env = make(config.task, **config.cartpole_config) - elif config.task == 'quadrotor': - env = make(config.task, **config.quadrotor_config) - # Reset the environment, obtain and print the initial observations. - initial_obs, initial_info = env.reset() - print('\n\n') - # Dynamics info - print_str_with_style('PyBullet dynamics info:', 7) - if config.task == 'cartpole': - print('\t' + str(p.getDynamicsInfo(bodyUniqueId=env.CARTPOLE_ID, linkIndex=-1, physicsClientId=env.PYB_CLIENT))) - elif config.task == 'quadrotor': - print('\t' + str(p.getDynamicsInfo(bodyUniqueId=env.DRONE_IDS[0], linkIndex=-1, physicsClientId=env.PYB_CLIENT))) - print('\n\n') - print_str_with_style('Initial reset.\n', 7) - print_str_with_style('Open AI gym API:', 2) - print_str_with_style('\tInitial observation: ' + str(initial_obs), 2) - # - print_str_with_style('safe-control-gym API:', 0) - print_str_with_style('\tA priori symbolic model:', 0) - out = '\t\tState: ' + str(initial_info['symbolic_model'].x_sym).strip('vertcat') - print_str_with_style(out, 0) - out = '\t\tInput: ' + str(initial_info['symbolic_model'].u_sym).strip('vertcat') - print_str_with_style(out, 0) - out = '\t\tDynamics: ' + str(initial_info['symbolic_model'].x_dot).strip('vertcat') - print_str_with_style(out, 0) - out = '\t\tCost: ' + str(initial_info['symbolic_model'].cost_func).replace('vertcat', '').replace(', (',',\n\t\t\t(').replace(', @',',\n\t\t\t@') - print_str_with_style(out, 0) - print_str_with_style('\tConstraints:', 0) - for fun in initial_info['symbolic_constraints']: - out = '\t' + str(inspect.getsource(fun)).strip('\n') - print_str_with_style(out, 0) - print_str_with_style('\tA priori parameters:', 0) - out = '\t\t' + str(initial_info['nominal_physical_parameters']) - print_str_with_style(out, 0) - print_str_with_style('\tX reference:', 0) - out = '\t\t' + str(initial_info['x_reference']) - print_str_with_style(out, 0) - print_str_with_style('\tU reference:', 0) - out = '\t\t' + str(initial_info['u_reference']) - print_str_with_style(out, 0) - print('\n\n') - # Run an experiment. - for i in range(ITERATIONS): - # Step by keyboard input - # _ = input('Press any key to continue.') - # Sample a random action. - action = env.action_space.sample() - # Step the environment and print all returned information. - obs, reward, done, info = env.step(action) - # - print_str_with_style(str(i)+'-th step.', 7) - out = '\tApplied action: ' + str(action) + '\n' - print(out) - - print_str_with_style('Open AI gym API:', 2) - out = '\tObservation: ' + str(obs) - print_str_with_style(out, 2) - out = '\tReward: ' + str(reward) - print_str_with_style(out, 2) - out = '\tDone: ' + str(done) - print_str_with_style(out, 2) - - print_str_with_style('safe-control-gym API:', 0) - out = '\tConstraints evaluations: ' + str(info['constraint_values']) - print_str_with_style(out, 0) - out = '\tConstraints violation: ' + str(bool(info['constraint_violation'])) - print_str_with_style(out, 0) - - print('\n\n') - # If an episode is complete, reset the environment. - if done: - num_episodes += 1 - new_initial_obs, new_initial_info = env.reset() - print_str_with_style(str(num_episodes)+'-th reset.', 7) - print_str_with_style('Reset obs' + str(new_initial_obs), 2) - print_str_with_style('Reset info' + str(new_initial_info), 0) - print('\n\n------------------------------------------------------------------------------') - print('------------------------------------------------------------------------------\n\n') - - # Close the environment and print timing statistics. - env.close() - elapsed_sec = time.time() - START - out = str("\n{:d} iterations (@{:d}Hz) and {:d} episodes in {:.2f} seconds, i.e. {:.2f} steps/sec for a {:.2f}x speedup.\n\n" - .format(ITERATIONS, env.CTRL_FREQ, num_episodes, elapsed_sec, ITERATIONS/elapsed_sec, (ITERATIONS*env.CTRL_TIMESTEP)/elapsed_sec)) - print_str_with_style(out,7) - -class bcolors: - """Support for color output. - - """ - HEADER = '\033[95m' - OKBLUE = '\033[94m' - OKCYAN = '\033[96m' - OKGREEN = '\033[92m' - WARNING = '\033[93m' - FAIL = '\033[91m' - ENDC = '\033[0m' - BOLD = '\033[1m' - UNDERLINE = '\033[4m' - - -def print_str_with_style(string: str='', style: int=0): - """Function to convert to string and print in color. - - """ - string = str(string) - if style == 0: - print(bcolors.HEADER + string + bcolors.ENDC) - elif style == 1: - print(bcolors.OKBLUE + string + bcolors.ENDC) - elif style == 2: - print(bcolors.OKCYAN + string + bcolors.ENDC) - elif style == 3: - print(bcolors.OKGREEN + string + bcolors.ENDC) - elif style == 4: - print(bcolors.WARNING + string + bcolors.ENDC) - elif style == 5: - print(bcolors.FAIL + string + bcolors.ENDC) - elif style == 6: - print(bcolors.BOLD + string + bcolors.ENDC) - elif style == 7: - print(bcolors.UNDERLINE + string + bcolors.ENDC) - else: - raise ValueError('[ERROR] unknown style!') - - -if __name__ == "__main__": - run() diff --git a/tests/scripts/verbose_api.yaml b/tests/scripts/verbose_api.yaml deleted file mode 100644 index 09c5e4984..000000000 --- a/tests/scripts/verbose_api.yaml +++ /dev/null @@ -1,186 +0,0 @@ -cartpole_config: - seed: 1337 - info_in_reset: True - ctrl_freq: 50 - pyb_freq: 50 - gui: False - normalized_rl_action_space: False -# - init_state: - init_x: 0 - init_x_dot: 0 - init_theta: 0 - init_theta_dot: 0 - randomized_init: True - init_state_randomization_info: - init_x: - distrib: "uniform" - low: -0.05 - high: 0.05 - init_x_dot: - distrib: "uniform" - low: -0.05 - high: 0.05 - init_theta: - distrib: "uniform" - low: -0.05 - high: 0.05 - init_theta_dot: - distrib: "uniform" - low: -0.05 - high: 0.05 - prior_prop: - pole_length: 0.5 - pole_mass: 0.1 - cart_mass: 1 - inertial_prop: - pole_length: 1 - pole_mass: 0.1 - cart_mass: 1 - randomized_inertial_prop: True - inertial_prop_randomization_info: - pole_length: - distrib: "uniform" - low: -0.25 - high: 0.0 - cart_mass: - distrib: "uniform" - low: 0.5 - high: 1.5 - pole_mass: - distrib: "uniform" - low: 0.05 - high: 0.15 -# - constraints: - - constraint_form: default_constraint - constrained_variable: input - strict: False - - constraint_form: default_constraint - constrained_variable: state -# - task: traj_tracking - task_info: - stabilization_goal: [0] - stabilization_goal_tolerance: 0.01 - trajectory_type: "circle" - num_cycles: 1 - trajectory_plane: "zx" - trajectory_position_offset: [0, 0] - trajectory_scale: 0.2 - episode_len_sec: 10 - cost: quadratic -# - disturbances: - observation: - - disturbance_func: white_noise - std: 0.01 - action: - - disturbance_func: impulse - magnitude: 10 - step_offset: 2 - duration: 1 - decary_rate: 1 - dynamics: - - disturbance_func: white_noise - std: 0.1 -# - done_on_violation: True - use_constraint_penalty: True - constraint_penalty: -1 - verbose: False - - - - -quadrotor_config: - seed: 1337 - info_in_reset: True - ctrl_freq: 60 - pyb_freq: 240 - gui: False - physics: pyb - quad_type: 2 - normalized_rl_action_space: False -# - init_state: - init_x: 0 - init_x_dot: 0 - init_z: 0 - init_z_dot: 0 - init_theta: 0 - init_theta_dot: 0 - randomized_init: True - init_state_randomization_info: - init_x: - distrib: "uniform" - low: -0.5 - high: 0.5 - init_x_dot: - distrib: "uniform" - low: -0.01 - high: 0.01 - init_z: - distrib: "uniform" - low: 0.1 - high: 1.5 - init_z_dot: - distrib: "uniform" - low: -0.01 - high: 0.01 - init_theta: - distrib: "uniform" - low: -0.3 - high: 0.3 - init_theta_dot: - distrib: "uniform" - low: -0.01 - high: 0.01 - inertial_prop: - mass: 0.027 - iyy: 1.4e-5 - randomized_inertial_prop: True - inertial_prop_randomization_info: - M: - distrib: "uniform" - low: 0.022 - high: 0.032 - Iyy: - distrib: "uniform" - low: 1.3e-5 - high: 1.5e-5 -# - constraints: - - constraint_form: default_constraint - constrained_variable: input - - constraint_form: default_constraint - constrained_variable: state -# - task: traj_tracking - task_info: - stabilization_goal: [0, 1] - stabilization_goal_tolerance: 0.01 - trajectory_type: "circle" - num_cycles: 1 - trajectory_plane: "zx" - trajectory_position_offset: [0.5, 0] - trajectory_scale: -0.5 - episode_len_sec: 10 - cost: quadratic -# - disturbances: - observation: - - disturbance_func: white_noise - std: 0.01 - action: - - disturbance_func: impulse - magnitude: 10 - step_offset: 2 - duration: 1 - decary_rate: 1 - dynamics: - - disturbance_func: white_noise - std: 0.1 -# - done_on_violation: True - verbose: False diff --git a/tests/test_build.py b/tests/test_build.py deleted file mode 100644 index 301e70551..000000000 --- a/tests/test_build.py +++ /dev/null @@ -1,6 +0,0 @@ -def test_imports(): - import safe_control_gym - import safe_control_gym.controllers - import safe_control_gym.envs - import safe_control_gym.envs.env_wrappers - import safe_control_gym.utils \ No newline at end of file diff --git a/tests/test_competition/test_competition.py b/tests/test_competition/test_competition.py deleted file mode 100644 index 8e4d2ae21..000000000 --- a/tests/test_competition/test_competition.py +++ /dev/null @@ -1,7 +0,0 @@ -import sys - -from competition.getting_started import run as competition_run - -def test_competition(): - sys.argv[1:] = ['--overrides', './competition/getting_started.yaml'] - competition_run(test=True) diff --git a/tests/test_examples/test_examples.py b/tests/test_examples/test_examples.py deleted file mode 100644 index e1fb424ee..000000000 --- a/tests/test_examples/test_examples.py +++ /dev/null @@ -1,12 +0,0 @@ -import sys - -from tests.scripts.tracking import run as tracking_run -from tests.scripts.verbose_api import run as verbose_run - -def test_tracking(): - sys.argv[1:] = ['--overrides', './tests/scripts/tracking.yaml'] - tracking_run(gui=False, max_steps=10) - -def test_verbose_api_quadrotor(): - sys.argv[1:] = ['--overrides', './tests/scripts/verbose_api.yaml', '--task', 'quadrotor'] - verbose_run()