diff --git a/CHANGELOG.md b/CHANGELOG.md index 2b7a9c4..d11d701 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,24 +1,66 @@ -## [1.16.1](https://github.com/act3-ace/run-time-assurance/compare/v1.16.0...v1.16.1) (2024-04-05) +## [1.18.3]() (2024-7-19) ### Bug Fixes -* bump jax verstion to 0.4.26 ([7e9688a](https://github.com/act3-ace/run-time-assurance/commit/7e9688a671cfdd5d445dffbbd41610f2fabced26)) -* **dependencies:** updated safe-autonomy-dynamics to 1.2.3 with jax extra ([b4c077d](https://github.com/act3-ace/run-time-assurance/commit/b4c077d04417a65d29af6cb7efdc0df0956410c3)) +* **safe-autonomy-simulation:** upgrade to safe-autonomy-simulation v2 dynamics ([02c0237]()), closes [#49]() -# [1.16.0](https://github.com/act3-ace/run-time-assurance/compare/v1.15.3...v1.16.0) (2024-03-26) +## [1.18.3]() (2024-7-11) + + +### Bug Fixes + +* **safe-autonomy-simulation:** upgrade to safe-autonomy-simulation v2 dynamics ([02c0237]()), closes [#49]() + +## [1.18.2]() (2024-6-27) + + +### Bug Fixes + +* integrator tutorial updates ([4885287]()) + +## [1.18.1]() (2024-6-26) + + +### Bug Fixes + +* add params to integrator constraints ([38b484c]()) + +# [1.18.0]() (2024-6-24) + + +### Features + +* Track constraints that cause intervention ([5926b23]()) + +# [1.17.0]() (2024-4-15) + + +### Features + +* **sim:** change sim backend to safe-autonomy-simulation ([562829c]()), closes [#48]() + +## [1.16.1]() (2024-4-5) + + +### Bug Fixes + +* bump jax verstion to 0.4.26 ([80908f5]()) +* **dependencies:** updated safe-autonomy-dynamics to 1.2.3 with jax extra ([8a1e0d4]()) + +# [1.16.0]() (2024-3-4) ### Features -* Dynamically changing parameters ([2d4aa95](https://github.com/act3-ace/run-time-assurance/commit/2d4aa95e818b0d901632882aec40bff017526c6d)) +* Dynamically changing parameters ([5791dad]()) -## [1.15.3](https://github.com/act3-ace/run-time-assurance/compare/v1.15.2...v1.15.3) (2024-01-10) +## 1.15.3 (2023-09-21) ### Bug Fixes -* **requirements:** update required sa-dynamics version to 0.13.2 ([2842994](https://github.com/act3-ace/run-time-assurance/commit/284299422aac33c222291b25242e281fa701b3be)) +* **requirements:** update required sa-dynamics version to 0.13.2 96254f6 ## 1.15.2 (2023-09-20) @@ -119,132 +161,132 @@ * removed semantic release commit message repo links c37d7b6 -## 1.10.1 (2023-03-28) +## [1.10.1](https://github.com/act3-ace/run-time-assurance/compare/v1.10.0...v1.10.1) (2023-03-28) ### Bug Fixes -* use jax 0.4.3 +* use jax 0.4.3 ([305f017](https://github.com/act3-ace/run-time-assurance/commit/305f0171fc020bab523f22036031451a743e9d34)) -# 1.10.0 (2023-03-16) +# [1.10.0](https://github.com/act3-ace/run-time-assurance/compare/v1.9.2...v1.10.0) (2023-03-16) ### Features -* Created SA-Dynamics ASIF ode solver with support for jax ode integration +* Created SA-Dynamics ASIF ode solver with support for jax ode integration ([1b45175](https://github.com/act3-ace/run-time-assurance/commit/1b45175f5cf3cf4277e08bda8e713d03f75ce432)) -## 1.9.2 (2023-03-15) +## [1.9.2](https://github.com/act3-ace/run-time-assurance/compare/v1.9.1...v1.9.2) (2023-03-15) ### Bug Fixes -* Refactor tests to use DataTrackingSampleTestingModule +* Refactor tests to use DataTrackingSampleTestingModule ([4f848bc](https://github.com/act3-ace/run-time-assurance/commit/4f848bcc2374f1db6c033836a4ba0e113fb256c0)) -## 1.9.1 (2023-03-01) +## [1.9.1](https://github.com/act3-ace/run-time-assurance/compare/v1.9.0...v1.9.1) (2023-03-01) ### Bug Fixes -* Use jnp array instead of list for PSM +* Use jnp array instead of list for PSM ([f60d869](https://github.com/act3-ace/run-time-assurance/commit/f60d869a0c52ab1f0e2b6ede91457fed0429556c)) -# 1.9.0 (2023-01-25) +# [1.9.0](https://github.com/act3-ace/run-time-assurance/compare/v1.8.0...v1.9.0) (2023-01-25) ### Features -* **constraint:** Created inequality constraint class that can be added to QP +* **constraint:** Created inequality constraint class that can be added to QP ([e60fb7f](https://github.com/act3-ace/run-time-assurance/commit/e60fb7fefe97cb00499f1720aa47fe48dc83c38e)) -# 1.8.0 (2023-01-24) +# [1.8.0](https://github.com/act3-ace/run-time-assurance/compare/v1.7.0...v1.8.0) (2023-01-24) ### Features -* **constraint:** Resolve "Incorporate exponential/high order CBFs" -* **jit:** Disable jit and vmap for debugging +* **constraint:** Resolve "Incorporate exponential/high order CBFs" ([66e53aa](https://github.com/act3-ace/run-time-assurance/commit/66e53aa78c7b7671fe017ae56b8e026e09a58b11)) +* **jit:** Disable jit and vmap for debugging ([45359db](https://github.com/act3-ace/run-time-assurance/commit/45359dbe97010a5eca24d30e58219a8050bfa882)) -# 1.7.0 (2023-01-24) +# [1.7.0](https://github.com/act3-ace/run-time-assurance/compare/v1.6.0...v1.7.0) (2023-01-24) ### Features -* **zoo:** 1d Integrator RTA +* **zoo:** 1d Integrator RTA ([b6448c9](https://github.com/act3-ace/run-time-assurance/commit/b6448c987e31c6b5d33c4c41d6077a346896a3b5)) -# 1.6.0 (2023-01-18) +# [1.6.0](https://github.com/act3-ace/run-time-assurance/compare/v1.5.0...v1.6.0) (2023-01-18) ### Features -* **logging:** constraint values are logged by rta modules +* **logging:** constraint values are logged by rta modules ([0a253b3](https://github.com/act3-ace/run-time-assurance/commit/0a253b360bb6f107c58c20a099e523329afc5e29)) -# 1.5.0 (2023-01-16) +# [1.5.0](https://github.com/act3-ace/run-time-assurance/compare/v1.4.0...v1.5.0) (2023-01-16) ### Features -* **implicit asif:** Fix subsample_constraints for implicit ASIF +* **implicit asif:** Fix subsample_constraints for implicit ASIF ([4f6a7f4](https://github.com/act3-ace/run-time-assurance/commit/4f6a7f4cc2ddee25c6c9c81c90efd014a47010b3)) -# 1.4.0 (2023-01-16) +# [1.4.0](https://github.com/act3-ace/run-time-assurance/compare/v1.3.0...v1.4.0) (2023-01-16) ### Features -* **dynamics:** Default ASIF predicted state method +* **dynamics:** Default ASIF predicted state method ([e052c7c](https://github.com/act3-ace/run-time-assurance/commit/e052c7c403f8dcbd22a9ffc7d0008b0af2c35339)) -# 1.3.0 (2022-11-14) +# [1.3.0](https://github.com/act3-ace/run-time-assurance/compare/v1.2.0...v1.3.0) (2022-11-14) ### Features -* **zoo:** Inspection RTA +* **zoo:** Inspection RTA ([d5c489f](https://github.com/act3-ace/run-time-assurance/commit/d5c489f809b99e619537469f57253c0a75359003)) -# 1.2.0 (2022-11-01) +# [1.2.0](https://github.com/act3-ace/run-time-assurance/compare/v1.1.1...v1.2.0) (2022-11-01) ### Features -* **constraint:** constraint bias and monte carlo compat +* **constraint:** constraint bias and monte carlo compat ([941f7e5](https://github.com/act3-ace/run-time-assurance/commit/941f7e56e8feb8b1ea17f52e2ef5ed29e5b3ad03)) -## 1.1.1 (2022-08-18) +## [1.1.1](https://github.com/act3-ace/run-time-assurance/compare/v1.1.0...v1.1.1) (2022-08-18) ### Bug Fixes -* **setup.py:** fix versioning +* **setup.py:** fix versioning ([89ead8e](https://github.com/act3-ace/run-time-assurance/commit/89ead8e99ea28f0c1b8932e6d64f457185553cee)) -# 1.1.0 (2022-08-18) +# [1.1.0](https://github.com/act3-ace/run-time-assurance/compare/v1.0.0...v1.1.0) (2022-08-18) ### Bug Fixes -* add version stuff for package -* **Dockerfiel:** typo -* **Dockerfile:** add temp version for build to pass -* **Dockerfile:** explicitly name version -* **Dockerfile:** minor typo fixes -* **Dockerfile:** replace content -* **Dockerfile:** stupid computer -* **Dockerfile:** try again with arg -* **Dockerfile:** typo -* **dockerfile:** updat version -* **Dockerfile:** update for package dependencies, remove git clone -* **Dockerfile:** update for sa-dynamics changes -* **docker:** update sa dynamics to 0.3.0 -* **gitlab-ci:** image dep for mkkdocs -* **gitlab-ci:** no allow fail on mkdocs -* **gitlab-ci:** update cicd -* **gitlab-ci:** update mkdocs -* **gitlab-ci:** update mkdocs image -* image path -* more mkdocds fix -* pin mkdocsstrings -* **pylint:** fix pylint errors -* remove version file and lint disable -* semantic release files -* try old mkdocs -* updat mkdocs to allow failure -* update semantic release items +* add version stuff for package ([a74ffcf](https://github.com/act3-ace/run-time-assurance/commit/a74ffcf565518644499c55930ec2fa5b47e0a5c5)) +* **Dockerfiel:** typo ([5616740](https://github.com/act3-ace/run-time-assurance/commit/5616740fed9cd888c1ff2778597fc8dc8cace092)) +* **Dockerfile:** add temp version for build to pass ([8ffe626](https://github.com/act3-ace/run-time-assurance/commit/8ffe626369643a92bd1394d57ab66cae0e1c5a3b)) +* **Dockerfile:** explicitly name version ([ed580c7](https://github.com/act3-ace/run-time-assurance/commit/ed580c708d575bbcd6c32a1ff829c9133f1fb4d8)) +* **Dockerfile:** minor typo fixes ([821983a](https://github.com/act3-ace/run-time-assurance/commit/821983a2919312292deb28be0ecfd66531628548)) +* **Dockerfile:** replace content ([6abedd2](https://github.com/act3-ace/run-time-assurance/commit/6abedd2ec65ecd8a2698fa95269b1743a8ba6dab)) +* **Dockerfile:** stupid computer ([9736db0](https://github.com/act3-ace/run-time-assurance/commit/9736db0167ac8cf5aadfbe8bc9fc7f9b92ce7615)) +* **Dockerfile:** try again with arg ([4caa7be](https://github.com/act3-ace/run-time-assurance/commit/4caa7be727b6080e523a1ab20fd2313be4523946)) +* **Dockerfile:** typo ([b03bbb3](https://github.com/act3-ace/run-time-assurance/commit/b03bbb3f7391b7cb442f068363730c5691688715)) +* **dockerfile:** updat version ([5267338](https://github.com/act3-ace/run-time-assurance/commit/5267338eb3d471db5bc66c75d387798eac1c8cd4)) +* **Dockerfile:** update for package dependencies, remove git clone ([334c3f3](https://github.com/act3-ace/run-time-assurance/commit/334c3f39ec02b9dd2b120b826d480b93cabc6ded)) +* **Dockerfile:** update for sa-dynamics changes ([7b27c2d](https://github.com/act3-ace/run-time-assurance/commit/7b27c2d9c6ba484669150e976d9672be0f8fd165)) +* **docker:** update sa dynamics to 0.3.0 ([c01ee32](https://github.com/act3-ace/run-time-assurance/commit/c01ee3294ded7be264162d5a03f0926e19bfb76f)) +* **gitlab-ci:** image dep for mkkdocs ([7c77d83](https://github.com/act3-ace/run-time-assurance/commit/7c77d8335060ec8f315afa7c0328839c3b27ed9a)) +* **gitlab-ci:** no allow fail on mkdocs ([159f925](https://github.com/act3-ace/run-time-assurance/commit/159f92567020650a46423edd3685e4a99597d4ae)) +* **gitlab-ci:** update cicd ([f1fc16b](https://github.com/act3-ace/run-time-assurance/commit/f1fc16b374100e7446c8e3fa6a628294da72958c)) +* **gitlab-ci:** update mkdocs ([cc3a506](https://github.com/act3-ace/run-time-assurance/commit/cc3a506084ed35fc15a72789a02b9b497d575496)) +* **gitlab-ci:** update mkdocs image ([52e9bc0](https://github.com/act3-ace/run-time-assurance/commit/52e9bc0f337ff94bb6c0abb0103f8df6cbd9c676)) +* image path ([0934e18](https://github.com/act3-ace/run-time-assurance/commit/0934e18c520111ea3d8a25778b10c90c8a69eb9a)) +* more mkdocds fix ([a119ad0](https://github.com/act3-ace/run-time-assurance/commit/a119ad01bad7028a580dd746836edc451dd6bd53)) +* pin mkdocsstrings ([14cdd73](https://github.com/act3-ace/run-time-assurance/commit/14cdd73eed46fa1631c3f2c233dc0c59328aa3eb)) +* **pylint:** fix pylint errors ([e16d75d](https://github.com/act3-ace/run-time-assurance/commit/e16d75d77c93e1f364da1659fdb75bbcfd0f8ba9)) +* remove version file and lint disable ([46d10e8](https://github.com/act3-ace/run-time-assurance/commit/46d10e85660bdc909a00d23b64a17812a82d0299)) +* semantic release files ([37cc3cf](https://github.com/act3-ace/run-time-assurance/commit/37cc3cf37c988cfb25b5b075e4e02bfe2c8530bf)) +* try old mkdocs ([4bde0ab](https://github.com/act3-ace/run-time-assurance/commit/4bde0ab9fd4e6dac613212c039cbd349f9746c10)) +* updat mkdocs to allow failure ([b4c48fb](https://github.com/act3-ace/run-time-assurance/commit/b4c48fb6cf1104b684d2f583cc2237a044b9cfdd)) +* update semantic release items ([bd02af1](https://github.com/act3-ace/run-time-assurance/commit/bd02af1237cc2c2c59add29e919aa0d6823ae513)) ### Features -* **Dockerfile:** update Dockerfile verrsion +* **Dockerfile:** update Dockerfile verrsion ([cb5cc93](https://github.com/act3-ace/run-time-assurance/commit/cb5cc93374a43c79ea5dcf054217b6faf0231b7b)) diff --git a/VERSION b/VERSION index 41c11ff..a67b05e 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -1.16.1 +1.18.4 diff --git a/poetry.lock b/poetry.lock index 2dd7314..b9456c0 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,91 +1,103 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. + +[[package]] +name = "aiohappyeyeballs" +version = "2.3.2" +description = "Happy Eyeballs" +optional = false 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version = "0.4.26", extras = ["cpu"] } numpy = "^1.23.5" matplotlib = "^3.8.0" quadprog = "^0.1.11" +pydantic = "^2.8.0" +safe-autonomy-simulation = "^2.0.12" +pyqt6 = "^6.7.1" [tool.poetry.group.lint.dependencies] pylint = "2.15.4" @@ -47,7 +49,7 @@ optional = true pytest = "^7.1.2" pytest-cov = "2.11.1" pyinstrument = "^4.3.0" -twine = "^4.0.2" +twine = "^5.1.1" coverage-badge = "^1.1.0" [tool.poetry.group.docs.dependencies] @@ -69,6 +71,7 @@ pymdown-extensions = "^9.5" pytkdocs = { version = ">=0.5.0", extras = ["numpy-style"] } mkdocstrings-python-legacy = "^0.2.1" + [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" diff --git a/run_time_assurance/rta/asif.py b/run_time_assurance/rta/asif.py index 28c3e66..d5b6ee6 100644 --- a/run_time_assurance/rta/asif.py +++ b/run_time_assurance/rta/asif.py @@ -15,7 +15,7 @@ import numpy as np import quadprog from jax import jacfwd, jit, lax, vmap -from safe_autonomy_dynamics.base_models import BaseControlAffineODESolverDynamics, BaseODESolverDynamics +from safe_autonomy_simulation.dynamics import ControlAffineODEDynamics, ODEDynamics from scipy.optimize import minimize from run_time_assurance.constraint import ConstraintModule, DirectInequalityConstraint, DiscreteCBFConstraint @@ -86,6 +86,7 @@ class ASIFModule(BaseOptimizationModule): def __init__(self, *args: Any, **kwargs: Any): super().__init__(*args, **kwargs) + self.constraint_names = [] self.ineq_weight_actuation, self.ineq_constant_actuation = self._generate_actuation_constraint_mats() self.direct_inequality_constraints = self._setup_direct_constraints() @@ -93,6 +94,8 @@ def __init__(self, *args: Any, **kwargs: Any): self.direct_inequality_params = dict.fromkeys(self.direct_inequality_constraints.keys()) self.direct_inequality_enabled_dict = dict.fromkeys(self.direct_inequality_constraints.keys()) self._update_direct_inequality_params() + self.constraints_causing_intervention = None + self.constraint_names += list(self.constraints.keys()) + list(self.direct_inequality_constraints.keys()) def _update_direct_inequality_params(self): """Update the parameters for each constraint""" @@ -151,6 +154,8 @@ def _generate_actuation_constraint_mats(self) -> tuple[jnp.ndarray, jnp.ndarray] c = jnp.hstack((c, jnp.zeros((self.control_dim, self.num_slack)))) ineq_weight = jnp.vstack((ineq_weight, c)) ineq_constant = jnp.concatenate((ineq_constant, b)) + for i in range(self.control_dim): + self.constraint_names.append('u' + str(i + 1) + '_min') if self.control_bounds_high is not None: c, b = get_lower_bound_ineq_constraint_mats(self.control_bounds_high, self.control_dim) @@ -159,6 +164,8 @@ def _generate_actuation_constraint_mats(self) -> tuple[jnp.ndarray, jnp.ndarray] c = jnp.hstack((c, jnp.zeros((self.control_dim, self.num_slack)))) ineq_weight = jnp.vstack((ineq_weight, c)) ineq_constant = jnp.concatenate((ineq_constant, b)) + for i in range(self.control_dim): + self.constraint_names.append('u' + str(i + 1) + '_max') return ineq_weight, ineq_constant @@ -239,17 +246,26 @@ def _optimize( try: opt = quadprog.solve_qp( obj_weight, obj_constant, np.array(ineq_weight, dtype=np.float64), np.array(ineq_constant, dtype=np.float64), 0 - )[0] + ) + u = opt[0] + + # Get constraints causing intervention + self.constraints_causing_intervention = [] + for i in opt[5]: + key = self.constraint_names[i - 1] + if key not in self.constraints_causing_intervention: + self.constraints_causing_intervention.append(key) + except ValueError as e: if e.args[0] == "constraints are inconsistent, no solution": if not self.solver_exception: warnings.warn(SolverWarning()) - opt = obj_constant + u = obj_constant else: raise SolverError() from e else: raise e - return opt[0:self.control_dim] + return u[0:self.control_dim] @abc.abstractmethod def _generate_barrier_constraint_mats(self, state: jnp.ndarray, step_size: float, params: dict, @@ -451,7 +467,7 @@ class ImplicitASIFModule(ASIFModule, BackupControlBasedRTA): backup_controller : RTABackupController backup controller object utilized by rta module to generate backup control integration_method: str, optional - Integration method to use, either 'RK45_JAX', 'RK45', or 'Euler' + Integration method to use, either 'RK45', or 'Euler' """ def __init__( @@ -459,7 +475,7 @@ def __init__( *args: Any, backup_window: float, backup_controller: RTABackupController, - integration_method: str = 'RK45_JAX', + integration_method: str = 'RK45', **kwargs: Any, ): self.backup_window = backup_window @@ -501,12 +517,7 @@ def compose(self): else: self._generate_ineq_constraint_mats_fn = self._generate_ineq_constraint_mats - if self.integration_method in ('Euler', 'RK45_JAX'): - default_int = True - elif self.integration_method == 'RK45': - default_int = False - else: - raise ValueError('integration_method must be either RK45_JAX, RK45, or Euler') + default_int = True if self.jit_enable and self.jit_compile_dict.get('pred_state', default_int): self._pred_state_fn = jit(self._pred_state, static_argnames=['step_size']) @@ -584,6 +595,17 @@ def _generate_barrier_constraint_mats(self, state: jnp.ndarray, step_size: float state, num_steps, traj_states, traj_sensitivities, params, constraint_enabled_dict ) + # Fix constraint names: + idx = 0 + if self.control_bounds_low is not None: + idx += self.control_dim + if self.control_bounds_high is not None: + idx += self.control_dim + self.constraint_names = self.constraint_names[0:idx] + for k in self.constraints.keys(): + self.constraint_names += [k] * num_steps + self.constraint_names += list(self.direct_inequality_constraints.keys()) + return ineq_weight, ineq_constant def _generate_ineq_constraint_mats( @@ -933,13 +955,13 @@ def get_lower_bound_ineq_constraint_mats(bound: Union[int, float, np.ndarray, jn return c, b -class ASIFODESolver(BaseControlAffineODESolverDynamics): +class ASIFODESolver(ControlAffineODEDynamics): """Control Affine ODE solver for ASIF""" def __init__(self, integration_method, state_transition_system, state_transition_input, **kwargs): self.asif_state_transition_system = state_transition_system self.asif_state_transition_input = state_transition_input - super().__init__(integration_method=integration_method, use_jax=True, **kwargs) + super().__init__(integration_method=integration_method, **kwargs) def state_transition_system(self, state): return self.asif_state_transition_system(state) @@ -1085,12 +1107,12 @@ def compute_state_dot(self, t: float, state: jnp.ndarray, control: jnp.ndarray) raise NotImplementedError -class DifferentiableODESolver(BaseODESolverDynamics): +class DifferentiableODESolver(ODEDynamics): """Differentiable ODE solver for Discrete ASIF Module""" def __init__(self, state_dot_fn, **kwargs): self.state_dot_fn = state_dot_fn - super().__init__(integration_method='RK45_JAX', use_jax=True, **kwargs) + super().__init__(integration_method='RK45', **kwargs) def _compute_state_dot(self, t, state, control): return self.state_dot_fn(t, state, control) diff --git a/run_time_assurance/zoo/cwh/docking_2d.py b/run_time_assurance/zoo/cwh/docking_2d.py index 3161378..091a818 100644 --- a/run_time_assurance/zoo/cwh/docking_2d.py +++ b/run_time_assurance/zoo/cwh/docking_2d.py @@ -1,13 +1,13 @@ -"""This module implements RTA methods for the docking problem with 2D CWH dynamics models -""" +"""This module implements RTA methods for the docking problem with 2D CWH dynamics models""" + from collections import OrderedDict from typing import Dict, Tuple, Union import jax.numpy as jnp import numpy as np import scipy -from safe_autonomy_dynamics.base_models import BaseLinearODESolverDynamics -from safe_autonomy_dynamics.cwh import M_DEFAULT, N_DEFAULT, generate_cwh_matrices +import safe_autonomy_simulation.dynamics as dynamics +import safe_autonomy_simulation.sims.spacecraft.defaults as defaults from run_time_assurance.constraint import ( ConstraintMagnitudeStateLimit, @@ -16,15 +16,22 @@ PolynomialConstraintStrengthener, ) from run_time_assurance.controller import RTABackupController -from run_time_assurance.rta import ExplicitASIFModule, ExplicitSimplexModule, ImplicitASIFModule, ImplicitSimplexModule +from run_time_assurance.rta import ( + ExplicitASIFModule, + ExplicitSimplexModule, + ImplicitASIFModule, + ImplicitSimplexModule, +) from run_time_assurance.state import RTAStateWrapper from run_time_assurance.utils import norm_with_delta +from run_time_assurance.zoo.cwh.utils import generate_cwh_matrices + X_VEL_LIMIT_DEFAULT = 10 Y_VEL_LIMIT_DEFAULT = 10 V0_DEFAULT = 0.2 V1_COEF_DEFAULT = 2 -V1_DEFAULT = V1_COEF_DEFAULT * N_DEFAULT +V1_DEFAULT = V1_COEF_DEFAULT * defaults.N_DEFAULT V0_DISTANCE_DEFAULT = 0 @@ -77,39 +84,65 @@ class Docking2dRTAMixin: Must call mixin methods using the RTA interface methods """ - def _setup_docking_properties(self, m: float, n: float, v1_coef: float, jit_compile_dict: Dict[str, bool], integration_method: str): + def _setup_docking_properties( + self, + m: float, + n: float, + v1_coef: float, + jit_compile_dict: Dict[str, bool], + integration_method: str, + ): """Initializes docking specific properties from other class members""" self.v1 = v1_coef * n A, B = generate_cwh_matrices(m, n, mode="2d") self.A = jnp.array(A) self.B = jnp.array(B) - self.dynamics = BaseLinearODESolverDynamics(A=A, B=B, integration_method=integration_method, use_jax=True) - - if integration_method == 'RK45': - jit_compile_dict.setdefault('pred_state', False) - jit_compile_dict.setdefault('integrate', False) - if jit_compile_dict.get('pred_state'): - raise ValueError('pred_state uses RK45 integration and can not be compiled using jit') - if jit_compile_dict.get('integrate'): - raise ValueError('integrate uses RK45 integration and can not be compiled using jit') - elif integration_method in ('Euler', 'RK45_JAX'): - jit_compile_dict.setdefault('pred_state', True) - jit_compile_dict.setdefault('integrate', True) - else: - raise ValueError('integration_method must be either RK45_JAX, RK45, or Euler') - - def _setup_docking_constraints(self, v0: float, v1: float, v0_distance: float, x_vel_limit: float, y_vel_limit: float) -> OrderedDict: + self.dynamics = dynamics.LinearODEDynamics( + A=A, + B=B, + integration_method=integration_method, + ) + + assert integration_method in ( + "RK45", + "Euler", + ), f"Invalid integration method {integration_method}, must be 'RK45' or 'Euler'" + + jit_compile_dict.setdefault("pred_state", True) + jit_compile_dict.setdefault("integrate", True) + + def _setup_docking_constraints( + self, + v0: float, + v1: float, + v0_distance: float, + x_vel_limit: float, + y_vel_limit: float, + ) -> OrderedDict: """generates constraints used in the docking problem""" return OrderedDict( [ - ('rel_vel', ConstraintDocking2dRelativeVelocity(v0=v0, v1=v1, v0_distance=v0_distance, bias=-1e-4)), - ('x_vel', ConstraintMagnitudeStateLimit(limit_val=x_vel_limit, state_index=2)), - ('y_vel', ConstraintMagnitudeStateLimit(limit_val=y_vel_limit, state_index=3)), + ( + "rel_vel", + ConstraintDocking2dRelativeVelocity( + v0=v0, v1=v1, v0_distance=v0_distance, bias=-1e-4 + ), + ), + ( + "x_vel", + ConstraintMagnitudeStateLimit(limit_val=x_vel_limit, state_index=2), + ), + ( + "y_vel", + ConstraintMagnitudeStateLimit(limit_val=y_vel_limit, state_index=3), + ), ] ) - def _docking_pred_state(self, state: jnp.ndarray, step_size: float, control: jnp.ndarray) -> jnp.ndarray: + def _docking_pred_state( + self, state: jnp.ndarray, step_size: float, control: jnp.ndarray + ) -> jnp.ndarray: """Predicts the next state given the current state and control action""" out, _ = self.dynamics.step(step_size, state, control) return out @@ -154,14 +187,14 @@ class Docking2dExplicitSwitchingRTA(ExplicitSimplexModule, Docking2dRTAMixin): jit_compile_dict: Dict[str, bool], optional Dictionary specifying which subroutines will be jax jit compiled. Behavior defined in self.compose() integration_method: str, optional - Integration method to use, either 'RK45_JAX', 'RK45', or 'Euler' + Integration method to use, either 'RK45' or 'Euler' """ def __init__( self, *args, - m: float = M_DEFAULT, - n: float = N_DEFAULT, + m: float = defaults.M_DEFAULT, + n: float = defaults.N_DEFAULT, v0: float = V0_DEFAULT, v1_coef: float = V1_COEF_DEFAULT, v0_distance: float = V0_DISTANCE_DEFAULT, @@ -171,8 +204,8 @@ def __init__( control_bounds_low: Union[float, np.ndarray] = -1, backup_controller: RTABackupController = None, jit_compile_dict: Dict[str, bool] = None, - integration_method: str = 'RK45_JAX', - **kwargs + integration_method: str = "RK45", + **kwargs, ): self.m = m self.n = n @@ -188,7 +221,7 @@ def __init__( backup_controller = Docking2dStopLQRBackupController(m=self.m, n=self.n) if jit_compile_dict is None: - jit_compile_dict = {'constraint_violation': True} + jit_compile_dict = {"constraint_violation": True} super().__init__( *args, @@ -196,16 +229,22 @@ def __init__( control_bounds_low=control_bounds_low, backup_controller=backup_controller, jit_compile_dict=jit_compile_dict, - **kwargs + **kwargs, ) def _setup_properties(self): - self._setup_docking_properties(self.m, self.n, self.v1_coef, self.jit_compile_dict, self.integration_method) + self._setup_docking_properties( + self.m, self.n, self.v1_coef, self.jit_compile_dict, self.integration_method + ) def _setup_constraints(self) -> OrderedDict: - return self._setup_docking_constraints(self.v0, self.v1, self.v0_distance, self.x_vel_limit, self.y_vel_limit) + return self._setup_docking_constraints( + self.v0, self.v1, self.v0_distance, self.x_vel_limit, self.y_vel_limit + ) - def _pred_state(self, state: jnp.ndarray, step_size: float, control: jnp.ndarray) -> jnp.ndarray: + def _pred_state( + self, state: jnp.ndarray, step_size: float, control: jnp.ndarray + ) -> jnp.ndarray: return self._docking_pred_state(state, step_size, control) @@ -242,15 +281,15 @@ class Docking2dImplicitSwitchingRTA(ImplicitSimplexModule, Docking2dRTAMixin): jit_compile_dict: Dict[str, bool], optional Dictionary specifying which subroutines will be jax jit compiled. Behavior defined in self.compose() integration_method: str, optional - Integration method to use, either 'RK45_JAX', 'RK45', or 'Euler' + Integration method to use, either 'RK45' or 'Euler' """ def __init__( self, *args, backup_window: float = 5, - m: float = M_DEFAULT, - n: float = N_DEFAULT, + m: float = defaults.M_DEFAULT, + n: float = defaults.N_DEFAULT, v0: float = V0_DEFAULT, v1_coef: float = V1_COEF_DEFAULT, v0_distance: float = V0_DISTANCE_DEFAULT, @@ -260,10 +299,9 @@ def __init__( control_bounds_low: Union[float, np.ndarray] = -1, backup_controller: RTABackupController = None, jit_compile_dict: Dict[str, bool] = None, - integration_method: str = 'RK45_JAX', - **kwargs + integration_method: str = "RK45", + **kwargs, ): - self.m = m self.n = n self.v0 = v0 @@ -278,7 +316,7 @@ def __init__( backup_controller = Docking2dStopLQRBackupController(m=self.m, n=self.n) if jit_compile_dict is None: - jit_compile_dict = {'constraint_violation': True} + jit_compile_dict = {"constraint_violation": True} super().__init__( *args, @@ -287,16 +325,22 @@ def __init__( control_bounds_high=control_bounds_high, control_bounds_low=control_bounds_low, jit_compile_dict=jit_compile_dict, - **kwargs + **kwargs, ) def _setup_properties(self): - self._setup_docking_properties(self.m, self.n, self.v1_coef, self.jit_compile_dict, self.integration_method) + self._setup_docking_properties( + self.m, self.n, self.v1_coef, self.jit_compile_dict, self.integration_method + ) def _setup_constraints(self) -> OrderedDict: - return self._setup_docking_constraints(self.v0, self.v1, self.v0_distance, self.x_vel_limit, self.y_vel_limit) + return self._setup_docking_constraints( + self.v0, self.v1, self.v0_distance, self.x_vel_limit, self.y_vel_limit + ) - def _pred_state(self, state: jnp.ndarray, step_size: float, control: jnp.ndarray) -> jnp.ndarray: + def _pred_state( + self, state: jnp.ndarray, step_size: float, control: jnp.ndarray + ) -> jnp.ndarray: return self._docking_pred_state(state, step_size, control) @@ -335,8 +379,8 @@ class Docking2dExplicitOptimizationRTA(ExplicitASIFModule, Docking2dRTAMixin): def __init__( self, *args, - m: float = M_DEFAULT, - n: float = N_DEFAULT, + m: float = defaults.M_DEFAULT, + n: float = defaults.N_DEFAULT, v0: float = V0_DEFAULT, v1_coef: float = V1_COEF_DEFAULT, v0_distance: float = V0_DISTANCE_DEFAULT, @@ -345,9 +389,8 @@ def __init__( control_bounds_high: Union[float, np.ndarray] = 1, control_bounds_low: Union[float, np.ndarray] = -1, jit_compile_dict: Dict[str, bool] = None, - **kwargs + **kwargs, ): - self.m = m self.n = n self.v0 = v0 @@ -358,7 +401,7 @@ def __init__( self.y_vel_limit = y_vel_limit if jit_compile_dict is None: - jit_compile_dict = {'generate_barrier_constraint_mats': True} + jit_compile_dict = {"generate_barrier_constraint_mats": True} super().__init__( *args, @@ -366,14 +409,18 @@ def __init__( control_bounds_high=control_bounds_high, control_bounds_low=control_bounds_low, jit_compile_dict=jit_compile_dict, - **kwargs + **kwargs, ) def _setup_properties(self): - self._setup_docking_properties(self.m, self.n, self.v1_coef, self.jit_compile_dict, 'RK45_JAX') + self._setup_docking_properties( + self.m, self.n, self.v1_coef, self.jit_compile_dict, "RK45" + ) def _setup_constraints(self) -> OrderedDict: - return self._setup_docking_constraints(self.v0, self.v1, self.v0_distance, self.x_vel_limit, self.y_vel_limit) + return self._setup_docking_constraints( + self.v0, self.v1, self.v0_distance, self.x_vel_limit, self.y_vel_limit + ) def state_transition_system(self, state: jnp.ndarray) -> jnp.ndarray: return self._docking_f_x(state) @@ -418,15 +465,15 @@ class Docking2dImplicitOptimizationRTA(ImplicitASIFModule, Docking2dRTAMixin): jit_compile_dict: Dict[str, bool], optional Dictionary specifying which subroutines will be jax jit compiled. Behavior defined in self.compose() integration_method: str, optional - Integration method to use, either 'RK45_JAX', 'RK45', or 'Euler' + Integration method to use, either 'RK45' or 'Euler' """ def __init__( self, *args, backup_window: float = 5, - m: float = M_DEFAULT, - n: float = N_DEFAULT, + m: float = defaults.M_DEFAULT, + n: float = defaults.N_DEFAULT, v0: float = V0_DEFAULT, v1_coef: float = V1_COEF_DEFAULT, v0_distance: float = V0_DISTANCE_DEFAULT, @@ -436,8 +483,8 @@ def __init__( control_bounds_low: Union[float, np.ndarray] = -1, backup_controller: RTABackupController = None, jit_compile_dict: Dict[str, bool] = None, - integration_method: str = 'RK45_JAX', - **kwargs + integration_method: str = "RK45", + **kwargs, ): self.m = m self.n = n @@ -452,7 +499,7 @@ def __init__( backup_controller = Docking2dStopLQRBackupController(m=self.m, n=self.n) if jit_compile_dict is None: - jit_compile_dict = {'generate_ineq_constraint_mats': True} + jit_compile_dict = {"generate_ineq_constraint_mats": True} super().__init__( *args, @@ -463,14 +510,18 @@ def __init__( control_bounds_low=control_bounds_low, jit_compile_dict=jit_compile_dict, integration_method=integration_method, - **kwargs + **kwargs, ) def _setup_properties(self): - self._setup_docking_properties(self.m, self.n, self.v1_coef, self.jit_compile_dict, self.integration_method) + self._setup_docking_properties( + self.m, self.n, self.v1_coef, self.jit_compile_dict, self.integration_method + ) def _setup_constraints(self) -> OrderedDict: - return self._setup_docking_constraints(self.v0, self.v1, self.v0_distance, self.x_vel_limit, self.y_vel_limit) + return self._setup_docking_constraints( + self.v0, self.v1, self.v0_distance, self.x_vel_limit, self.y_vel_limit + ) def state_transition_system(self, state: jnp.ndarray) -> jnp.ndarray: return self._docking_f_x(state) @@ -490,9 +541,9 @@ class Docking2dStopLQRBackupController(RTABackupController): orbital mean motion in rad/s of current Hill's reference frame, by default N_DEFAULT """ - def __init__(self, m: float = M_DEFAULT, n: float = N_DEFAULT): + def __init__(self, m: float = defaults.M_DEFAULT, n: float = defaults.N_DEFAULT): # LQR Gain Matrices - self.Q = jnp.multiply(.050, jnp.eye(4)) + self.Q = jnp.multiply(0.050, jnp.eye(4)) self.R = jnp.multiply(1000, jnp.eye(2)) self.A, self.B = generate_cwh_matrices(m, n, mode="2d") @@ -508,9 +559,8 @@ def _generate_control( self, state: jnp.ndarray, step_size: float, - controller_state: Union[jnp.ndarray, Dict[str, jnp.ndarray], None] = None + controller_state: Union[jnp.ndarray, Dict[str, jnp.ndarray], None] = None, ) -> Tuple[jnp.ndarray, None]: - state_des = jnp.copy(state) state_des = state_des.at[2:].set(0) @@ -539,13 +589,29 @@ class ConstraintDocking2dRelativeVelocity(ConstraintModule): Defaults to PolynomialConstraintStrengthener([0, 0.01, 0, 0.1]) """ - def __init__(self, v0: float, v1: float, v0_distance: float = 0, delta: float = 1e-5, alpha: ConstraintStrengthener = None, **kwargs): + def __init__( + self, + v0: float, + v1: float, + v0_distance: float = 0, + delta: float = 1e-5, + alpha: ConstraintStrengthener = None, + **kwargs, + ): self.delta = delta if alpha is None: alpha = PolynomialConstraintStrengthener([0, 0.01, 0, 0.1]) - super().__init__(alpha=alpha, params={'v0': v0, 'v1': v1, 'v0_distance': v0_distance}, **kwargs) + super().__init__( + alpha=alpha, + params={"v0": v0, "v1": v1, "v0_distance": v0_distance}, + **kwargs, + ) def _compute(self, state: jnp.ndarray, params: dict) -> float: - return params['v0'] + params['v1'] * (norm_with_delta(state[0:2], self.delta) - - params['v0_distance']) - norm_with_delta(state[2:4], self.delta) + return ( + params["v0"] + + params["v1"] + * (norm_with_delta(state[0:2], self.delta) - params["v0_distance"]) + - norm_with_delta(state[2:4], self.delta) + ) diff --git a/run_time_assurance/zoo/cwh/docking_3d.py b/run_time_assurance/zoo/cwh/docking_3d.py index d63b358..6ceacc9 100644 --- a/run_time_assurance/zoo/cwh/docking_3d.py +++ b/run_time_assurance/zoo/cwh/docking_3d.py @@ -1,12 +1,12 @@ -"""This module implements RTA methods for the docking problem with 3D CWH dynamics models -""" +"""This module implements RTA methods for the docking problem with 3D CWH dynamics models""" + from collections import OrderedDict from typing import Dict, Tuple, Union import jax.numpy as jnp import scipy -from safe_autonomy_dynamics.base_models import BaseLinearODESolverDynamics -from safe_autonomy_dynamics.cwh import M_DEFAULT, N_DEFAULT, generate_cwh_matrices +import safe_autonomy_simulation.dynamics as dynamics +import safe_autonomy_simulation.sims.spacecraft.defaults as defaults from run_time_assurance.constraint import ( ConstraintMagnitudeStateLimit, @@ -15,14 +15,24 @@ PolynomialConstraintStrengthener, ) from run_time_assurance.controller import RTABackupController -from run_time_assurance.rta import ExplicitASIFModule, ExplicitSimplexModule, ImplicitASIFModule, ImplicitSimplexModule +from run_time_assurance.rta import ( + ExplicitASIFModule, + ExplicitSimplexModule, + ImplicitASIFModule, + ImplicitSimplexModule, +) from run_time_assurance.state import RTAStateWrapper from run_time_assurance.utils import norm_with_delta -from run_time_assurance.zoo.cwh.docking_2d import V0_DEFAULT, X_VEL_LIMIT_DEFAULT, Y_VEL_LIMIT_DEFAULT +from run_time_assurance.zoo.cwh.docking_2d import ( + V0_DEFAULT, + X_VEL_LIMIT_DEFAULT, + Y_VEL_LIMIT_DEFAULT, +) +from run_time_assurance.zoo.cwh.utils import generate_cwh_matrices Z_VEL_LIMIT_DEFAULT = 10 V1_COEF_DEFAULT = 4 -V1_DEFAULT = V1_COEF_DEFAULT * N_DEFAULT +V1_DEFAULT = V1_COEF_DEFAULT * defaults.N_DEFAULT V0_DISTANCE_DEFAULT = 0 @@ -95,7 +105,14 @@ class Docking3dRTAMixin: Must call mixin methods using the RTA interface methods """ - def _setup_docking_properties(self, m: float, n: float, v1_coef: float, jit_compile_dict: Dict[str, bool], integration_method: str): + def _setup_docking_properties( + self, + m: float, + n: float, + v1_coef: float, + jit_compile_dict: Dict[str, bool], + integration_method: str, + ): """Initializes docking specific properties from other class members""" self.v1 = v1_coef * n @@ -103,35 +120,56 @@ def _setup_docking_properties(self, m: float, n: float, v1_coef: float, jit_comp self.A = jnp.array(A) self.B = jnp.array(B) - self.dynamics = BaseLinearODESolverDynamics(A=A, B=B, integration_method=integration_method, use_jax=True) - - if integration_method == 'RK45': - jit_compile_dict.setdefault('pred_state', False) - jit_compile_dict.setdefault('integrate', False) - if jit_compile_dict.get('pred_state'): - raise ValueError('pred_state uses RK45 integration and can not be compiled using jit') - if jit_compile_dict.get('integrate'): - raise ValueError('integrate uses RK45 integration and can not be compiled using jit') - elif integration_method in ('Euler', 'RK45_JAX'): - jit_compile_dict.setdefault('pred_state', True) - jit_compile_dict.setdefault('integrate', True) - else: - raise ValueError('integration_method must be either RK45_JAX, RK45, or Euler') + self.dynamics = dynamics.LinearODEDynamics( + A=A, + B=B, + integration_method=integration_method, + ) + + assert integration_method in ( + "Euler", + "RK45", + ), f"Invalid integration method {integration_method}, must be 'Euler' or 'RK45'" + + jit_compile_dict.setdefault("pred_state", True) + jit_compile_dict.setdefault("integrate", True) def _setup_docking_constraints( - self, v0: float, v1: float, v0_distance: float, x_vel_limit: float, y_vel_limit: float, z_vel_limit: float + self, + v0: float, + v1: float, + v0_distance: float, + x_vel_limit: float, + y_vel_limit: float, + z_vel_limit: float, ) -> OrderedDict: """generates constraints used in the docking problem""" return OrderedDict( [ - ('rel_vel', ConstraintCWH3dRelativeVelocity(v0=v0, v1=v1, v0_distance=v0_distance, bias=-1e-4)), - ('x_vel', ConstraintMagnitudeStateLimit(limit_val=x_vel_limit, state_index=3)), - ('y_vel', ConstraintMagnitudeStateLimit(limit_val=y_vel_limit, state_index=4)), - ('z_vel', ConstraintMagnitudeStateLimit(limit_val=z_vel_limit, state_index=5)), + ( + "rel_vel", + ConstraintCWH3dRelativeVelocity( + v0=v0, v1=v1, v0_distance=v0_distance, bias=-1e-4 + ), + ), + ( + "x_vel", + ConstraintMagnitudeStateLimit(limit_val=x_vel_limit, state_index=3), + ), + ( + "y_vel", + ConstraintMagnitudeStateLimit(limit_val=y_vel_limit, state_index=4), + ), + ( + "z_vel", + ConstraintMagnitudeStateLimit(limit_val=z_vel_limit, state_index=5), + ), ] ) - def _docking_pred_state(self, state: jnp.ndarray, step_size: float, control: jnp.ndarray) -> jnp.ndarray: + def _docking_pred_state( + self, state: jnp.ndarray, step_size: float, control: jnp.ndarray + ) -> jnp.ndarray: """Predicts the next state given the current state and control action""" out, _ = self.dynamics.step(step_size, state, control) return out @@ -178,14 +216,14 @@ class Docking3dExplicitSwitchingRTA(ExplicitSimplexModule, Docking3dRTAMixin): jit_compile_dict: Dict[str, bool], optional Dictionary specifying which subroutines will be jax jit compiled. Behavior defined in self.compose() integration_method: str, optional - Integration method to use, either 'RK45_JAX', 'RK45', or 'Euler' + Integration method to use, either 'RK45' or 'Euler' """ def __init__( self, *args, - m: float = M_DEFAULT, - n: float = N_DEFAULT, + m: float = defaults.M_DEFAULT, + n: float = defaults.N_DEFAULT, v0: float = V0_DEFAULT, v1_coef: float = V1_COEF_DEFAULT, v0_distance: float = V0_DISTANCE_DEFAULT, @@ -196,8 +234,8 @@ def __init__( control_bounds_low: float = -1, backup_controller: RTABackupController = None, jit_compile_dict: Dict[str, bool] = None, - integration_method: str = 'RK45_JAX', - **kwargs + integration_method: str = "RK45", + **kwargs, ): self.m = m self.n = n @@ -214,7 +252,7 @@ def __init__( backup_controller = Docking3dStopLQRBackupController(m=self.m, n=self.n) if jit_compile_dict is None: - jit_compile_dict = {'constraint_violation': True} + jit_compile_dict = {"constraint_violation": True} super().__init__( *args, @@ -222,16 +260,27 @@ def __init__( control_bounds_low=control_bounds_low, backup_controller=backup_controller, jit_compile_dict=jit_compile_dict, - **kwargs + **kwargs, ) def _setup_properties(self): - self._setup_docking_properties(self.m, self.n, self.v1_coef, self.jit_compile_dict, self.integration_method) + self._setup_docking_properties( + self.m, self.n, self.v1_coef, self.jit_compile_dict, self.integration_method + ) def _setup_constraints(self) -> OrderedDict: - return self._setup_docking_constraints(self.v0, self.v1, self.v0_distance, self.x_vel_limit, self.y_vel_limit, self.z_vel_limit) + return self._setup_docking_constraints( + self.v0, + self.v1, + self.v0_distance, + self.x_vel_limit, + self.y_vel_limit, + self.z_vel_limit, + ) - def _pred_state(self, state: jnp.ndarray, step_size: float, control: jnp.ndarray) -> jnp.ndarray: + def _pred_state( + self, state: jnp.ndarray, step_size: float, control: jnp.ndarray + ) -> jnp.ndarray: return self._docking_pred_state(state, step_size, control) @@ -270,15 +319,15 @@ class Docking3dImplicitSwitchingRTA(ImplicitSimplexModule, Docking3dRTAMixin): jit_compile_dict: Dict[str, bool], optional Dictionary specifying which subroutines will be jax jit compiled. Behavior defined in self.compose() integration_method: str, optional - Integration method to use, either 'RK45_JAX', 'RK45', or 'Euler' + Integration method to use, either 'RK45' or 'Euler' """ def __init__( self, *args, backup_window: float = 5, - m: float = M_DEFAULT, - n: float = N_DEFAULT, + m: float = defaults.M_DEFAULT, + n: float = defaults.N_DEFAULT, v0: float = V0_DEFAULT, v1_coef: float = V1_COEF_DEFAULT, v0_distance: float = V0_DISTANCE_DEFAULT, @@ -289,8 +338,8 @@ def __init__( control_bounds_low: float = -1, backup_controller: RTABackupController = None, jit_compile_dict: Dict[str, bool] = None, - integration_method: str = 'RK45_JAX', - **kwargs + integration_method: str = "RK45", + **kwargs, ): self.m = m self.n = n @@ -307,7 +356,7 @@ def __init__( backup_controller = Docking3dStopLQRBackupController(m=self.m, n=self.n) if jit_compile_dict is None: - jit_compile_dict = {'constraint_violation': True} + jit_compile_dict = {"constraint_violation": True} super().__init__( *args, @@ -316,16 +365,27 @@ def __init__( control_bounds_high=control_bounds_high, control_bounds_low=control_bounds_low, jit_compile_dict=jit_compile_dict, - **kwargs + **kwargs, ) def _setup_properties(self): - self._setup_docking_properties(self.m, self.n, self.v1_coef, self.jit_compile_dict, self.integration_method) + self._setup_docking_properties( + self.m, self.n, self.v1_coef, self.jit_compile_dict, self.integration_method + ) def _setup_constraints(self) -> OrderedDict: - return self._setup_docking_constraints(self.v0, self.v1, self.v0_distance, self.x_vel_limit, self.y_vel_limit, self.z_vel_limit) + return self._setup_docking_constraints( + self.v0, + self.v1, + self.v0_distance, + self.x_vel_limit, + self.y_vel_limit, + self.z_vel_limit, + ) - def _pred_state(self, state: jnp.ndarray, step_size: float, control: jnp.ndarray) -> jnp.ndarray: + def _pred_state( + self, state: jnp.ndarray, step_size: float, control: jnp.ndarray + ) -> jnp.ndarray: return self._docking_pred_state(state, step_size, control) @@ -366,8 +426,8 @@ class Docking3dExplicitOptimizationRTA(ExplicitASIFModule, Docking3dRTAMixin): def __init__( self, *args, - m: float = M_DEFAULT, - n: float = N_DEFAULT, + m: float = defaults.M_DEFAULT, + n: float = defaults.N_DEFAULT, v0: float = V0_DEFAULT, v1_coef: float = V1_COEF_DEFAULT, v0_distance: float = V0_DISTANCE_DEFAULT, @@ -377,7 +437,7 @@ def __init__( control_bounds_high: float = 1, control_bounds_low: float = -1, jit_compile_dict: Dict[str, bool] = None, - **kwargs + **kwargs, ): self.m = m self.n = n @@ -390,7 +450,7 @@ def __init__( self.z_vel_limit = z_vel_limit if jit_compile_dict is None: - jit_compile_dict = {'generate_barrier_constraint_mats': True} + jit_compile_dict = {"generate_barrier_constraint_mats": True} super().__init__( *args, @@ -398,14 +458,23 @@ def __init__( control_bounds_high=control_bounds_high, control_bounds_low=control_bounds_low, jit_compile_dict=jit_compile_dict, - **kwargs + **kwargs, ) def _setup_properties(self): - self._setup_docking_properties(self.m, self.n, self.v1_coef, self.jit_compile_dict, 'RK45_JAX') + self._setup_docking_properties( + self.m, self.n, self.v1_coef, self.jit_compile_dict, "RK45" + ) def _setup_constraints(self) -> OrderedDict: - return self._setup_docking_constraints(self.v0, self.v1, self.v0_distance, self.x_vel_limit, self.y_vel_limit, self.z_vel_limit) + return self._setup_docking_constraints( + self.v0, + self.v1, + self.v0_distance, + self.x_vel_limit, + self.y_vel_limit, + self.z_vel_limit, + ) def state_transition_system(self, state: jnp.ndarray) -> jnp.ndarray: return self._docking_f_x(state) @@ -452,15 +521,15 @@ class Docking3dImplicitOptimizationRTA(ImplicitASIFModule, Docking3dRTAMixin): jit_compile_dict: Dict[str, bool], optional Dictionary specifying which subroutines will be jax jit compiled. Behavior defined in self.compose() integration_method: str, optional - Integration method to use, either 'RK45_JAX', 'RK45', or 'Euler' + Integration method to use, either 'RK45' or 'Euler' """ def __init__( self, *args, backup_window: float = 5, - m: float = M_DEFAULT, - n: float = N_DEFAULT, + m: float = defaults.M_DEFAULT, + n: float = defaults.N_DEFAULT, v0: float = V0_DEFAULT, v1_coef: float = V1_COEF_DEFAULT, v0_distance: float = V0_DISTANCE_DEFAULT, @@ -471,8 +540,8 @@ def __init__( control_bounds_low: float = -1, backup_controller: RTABackupController = None, jit_compile_dict: Dict[str, bool] = None, - integration_method: str = 'RK45_JAX', - **kwargs + integration_method: str = "RK45", + **kwargs, ): self.m = m self.n = n @@ -488,7 +557,7 @@ def __init__( backup_controller = Docking3dStopLQRBackupController(m=self.m, n=self.n) if jit_compile_dict is None: - jit_compile_dict = {'generate_ineq_constraint_mats': True} + jit_compile_dict = {"generate_ineq_constraint_mats": True} super().__init__( *args, @@ -499,14 +568,23 @@ def __init__( control_bounds_low=control_bounds_low, jit_compile_dict=jit_compile_dict, integration_method=integration_method, - **kwargs + **kwargs, ) def _setup_properties(self): - self._setup_docking_properties(self.m, self.n, self.v1_coef, self.jit_compile_dict, self.integration_method) + self._setup_docking_properties( + self.m, self.n, self.v1_coef, self.jit_compile_dict, self.integration_method + ) def _setup_constraints(self) -> OrderedDict: - return self._setup_docking_constraints(self.v0, self.v1, self.v0_distance, self.x_vel_limit, self.y_vel_limit, self.z_vel_limit) + return self._setup_docking_constraints( + self.v0, + self.v1, + self.v0_distance, + self.x_vel_limit, + self.y_vel_limit, + self.z_vel_limit, + ) def state_transition_system(self, state: jnp.ndarray) -> jnp.ndarray: return self._docking_f_x(state) @@ -526,9 +604,9 @@ class Docking3dStopLQRBackupController(RTABackupController): orbital mean motion in rad/s of current Hill's reference frame, by default N_DEFAULT """ - def __init__(self, m: float = M_DEFAULT, n: float = N_DEFAULT): + def __init__(self, m: float = defaults.M_DEFAULT, n: float = defaults.N_DEFAULT): # LQR Gain Matrices - self.Q = jnp.multiply(.050, jnp.eye(6)) + self.Q = jnp.multiply(0.050, jnp.eye(6)) self.R = jnp.multiply(1000, jnp.eye(3)) self.A, self.B = generate_cwh_matrices(m, n, mode="3d") @@ -544,7 +622,7 @@ def _generate_control( self, state: jnp.ndarray, step_size: float, - controller_state: Union[jnp.ndarray, Dict[str, jnp.ndarray], None] = None + controller_state: Union[jnp.ndarray, Dict[str, jnp.ndarray], None] = None, ) -> Tuple[jnp.ndarray, None]: state_des = jnp.copy(state) state_des = state_des.at[3:].set(0) @@ -574,13 +652,28 @@ class ConstraintCWH3dRelativeVelocity(ConstraintModule): Defaults to PolynomialConstraintStrengthener([0, 0.05, 0, 0.5]) """ - def __init__(self, v0: float, v1: float, v0_distance: float = 0, delta: float = 1e-5, alpha: ConstraintStrengthener = None, **kwargs): + def __init__( + self, + v0: float, + v1: float, + v0_distance: float = 0, + delta: float = 1e-5, + alpha: ConstraintStrengthener = None, + **kwargs, + ): self.delta = delta if alpha is None: alpha = PolynomialConstraintStrengthener([0, 0.05, 0, 0.005]) - super().__init__(alpha=alpha, params={'v0': v0, 'v1': v1, 'v0_distance': v0_distance}, **kwargs) + super().__init__( + alpha=alpha, + params={"v0": v0, "v1": v1, "v0_distance": v0_distance}, + **kwargs, + ) def _compute(self, state: jnp.ndarray, params: dict) -> float: - return (params['v0'] + params['v1'] * - (norm_with_delta(state[0:3], self.delta) - params['v0_distance'])) - norm_with_delta(state[3:6], self.delta) + return ( + params["v0"] + + params["v1"] + * (norm_with_delta(state[0:3], self.delta) - params["v0_distance"]) + ) - norm_with_delta(state[3:6], self.delta) diff --git a/run_time_assurance/zoo/cwh/inspection_1v1.py b/run_time_assurance/zoo/cwh/inspection_1v1.py index 4c42c0c..eed26f3 100644 --- a/run_time_assurance/zoo/cwh/inspection_1v1.py +++ b/run_time_assurance/zoo/cwh/inspection_1v1.py @@ -1,5 +1,5 @@ -"""This module implements RTA methods for the single-agent inspection problem with 3D CWH dynamics models -""" +"""This module implements RTA methods for the single-agent inspection problem with 3D CWH dynamics models""" + from collections import OrderedDict from typing import Union @@ -8,7 +8,7 @@ import scipy from jax import jit, lax, vmap from jax.experimental.ode import odeint -from safe_autonomy_dynamics.cwh.point_model import M_DEFAULT, N_DEFAULT, generate_cwh_matrices +import safe_autonomy_simulation.sims.spacecraft.defaults as defaults from run_time_assurance.constraint import ( ConstraintMagnitudeStateLimit, @@ -17,10 +17,16 @@ HOCBFConstraint, PolynomialConstraintStrengthener, ) -from run_time_assurance.rta import CascadedRTA, DiscreteASIFModule, ExplicitASIFModule, RTAModule +from run_time_assurance.rta import ( + CascadedRTA, + DiscreteASIFModule, + ExplicitASIFModule, + RTAModule, +) from run_time_assurance.state import RTAStateWrapper from run_time_assurance.utils import to_jnp_array_jit from run_time_assurance.zoo.cwh.docking_3d import ConstraintCWH3dRelativeVelocity +from run_time_assurance.zoo.cwh.utils import generate_cwh_matrices CHIEF_RADIUS_DEFAULT = 5 # chief radius of collision [m] (collision freedom) DEPUTY_RADIUS_DEFAULT = 5 # deputy radius of collision [m] (collision freedom) @@ -28,11 +34,13 @@ V1_COEF_DEFAULT = 2 # velocity constraint slope [-] (dynamic velocity constraint) V0_DISTANCE_DEFAULT = 0 # distance where v0 is applied R_MAX_DEFAULT = 1000 # max distance from chief [m] (translational keep out zone) -FOV_DEFAULT = 60 * jnp.pi / 180 # sun avoidance angle [rad] (translational keep out zone) +FOV_DEFAULT = ( + 60 * jnp.pi / 180 +) # sun avoidance angle [rad] (translational keep out zone) U_MAX_DEFAULT = 1 # Max thrust [N] (avoid actuation saturation) VEL_LIMIT_DEFAULT = 1 # Maximum velocity limit [m/s] (Avoid aggressive maneuvering) DELTA_V_LIMIT_DEFAULT = 20 # Delta v limit [m/s] -SUN_VEL_DEFAULT = -N_DEFAULT # Speed of sun rotation in x-y plane +SUN_VEL_DEFAULT = -defaults.N_DEFAULT # Speed of sun rotation in x-y plane class Inspection3dState(RTAStateWrapper): @@ -164,8 +172,8 @@ class Inspection1v1RTA(ExplicitASIFModule): def __init__( # pylint:disable=too-many-arguments self, *args, - m: float = M_DEFAULT, - n: float = N_DEFAULT, + m: float = defaults.M_DEFAULT, + n: float = defaults.N_DEFAULT, chief_radius: float = CHIEF_RADIUS_DEFAULT, deputy_radius: float = DEPUTY_RADIUS_DEFAULT, v0: float = V0_DEFAULT, @@ -177,9 +185,13 @@ def __init__( # pylint:disable=too-many-arguments delta_v_limit: float = DELTA_V_LIMIT_DEFAULT, sun_vel: float = SUN_VEL_DEFAULT, use_hocbf: bool = False, - control_bounds_high: Union[float, list, np.ndarray, jnp.ndarray] = U_MAX_DEFAULT, - control_bounds_low: Union[float, list, np.ndarray, jnp.ndarray] = -U_MAX_DEFAULT, - **kwargs + control_bounds_high: Union[ + float, list, np.ndarray, jnp.ndarray + ] = U_MAX_DEFAULT, + control_bounds_low: Union[ + float, list, np.ndarray, jnp.ndarray + ] = -U_MAX_DEFAULT, + **kwargs, ): self.m = m self.n = n @@ -198,26 +210,39 @@ def __init__( # pylint:disable=too-many-arguments self.u_max = U_MAX_DEFAULT vmax = min(self.vel_limit, self.v0 + self.v1 * self.r_max) - self.a_max = self.u_max / self.m - 3 * self.n**2 * self.r_max - 2 * self.n * vmax + self.a_max = ( + self.u_max / self.m - 3 * self.n**2 * self.r_max - 2 * self.n * vmax + ) A, B = generate_cwh_matrices(self.m, self.n, mode="3d") self.A = jnp.array(A) self.B = jnp.array(B) self.control_dim = self.B.shape[1] - self._pred_state_fn = jit(self._pred_state, static_argnames=['step_size']) - self._pred_state_cwh_fn = jit(self._pred_state_cwh, static_argnames=['step_size']) + self._pred_state_fn = jit(self._pred_state, static_argnames=["step_size"]) + self._pred_state_cwh_fn = jit( + self._pred_state_cwh, static_argnames=["step_size"] + ) super().__init__( - *args, control_dim=self.control_dim, control_bounds_high=control_bounds_high, control_bounds_low=control_bounds_low, **kwargs + *args, + control_dim=self.control_dim, + control_bounds_high=control_bounds_high, + control_bounds_low=control_bounds_low, + **kwargs, ) def _setup_constraints(self) -> OrderedDict: constraint_dict = OrderedDict( [ - ('rel_vel', ConstraintCWH3dRelativeVelocity(v0=self.v0, v1=self.v1, v0_distance=self.v0_distance, bias=-1e-3)), ( - 'sun', + "rel_vel", + ConstraintCWH3dRelativeVelocity( + v0=self.v0, v1=self.v1, v0_distance=self.v0_distance, bias=-1e-3 + ), + ), + ( + "sun", ConstraintCWHConicKeepOutZone( a_max=self.a_max, fov=self.fov, @@ -225,71 +250,97 @@ def _setup_constraints(self) -> OrderedDict: get_vel=self.get_vel_vector, get_cone_vec=self.get_sun_vector, cone_ang_vel=jnp.array([0, 0, self.sun_vel]), - bias=-1e-3 - ) + bias=-1e-3, + ), + ), + ( + "r_max", + ConstraintCWHMaxDistance( + r_max=self.r_max, a_max=self.a_max, bias=-1e-3 + ), ), - ('r_max', ConstraintCWHMaxDistance(r_max=self.r_max, a_max=self.a_max, bias=-1e-3)), ( - 'PSM', + "PSM", ConstraintPassivelySafeManeuver( - collision_radius=self.chief_radius + self.deputy_radius, m=self.m, n=self.n, dt=1, steps=100 - ) + collision_radius=self.chief_radius + self.deputy_radius, + m=self.m, + n=self.n, + dt=1, + steps=100, + ), ), ( - 'x_vel', + "x_vel", ConstraintMagnitudeStateLimit( - limit_val=self.vel_limit, state_index=3, alpha=PolynomialConstraintStrengthener([0, 0.1, 0, 0.01]), bias=-0.001 - ) + limit_val=self.vel_limit, + state_index=3, + alpha=PolynomialConstraintStrengthener([0, 0.1, 0, 0.01]), + bias=-0.001, + ), ), ( - 'y_vel', + "y_vel", ConstraintMagnitudeStateLimit( - limit_val=self.vel_limit, state_index=4, alpha=PolynomialConstraintStrengthener([0, 0.1, 0, 0.01]), bias=-0.001 - ) + limit_val=self.vel_limit, + state_index=4, + alpha=PolynomialConstraintStrengthener([0, 0.1, 0, 0.01]), + bias=-0.001, + ), ), ( - 'z_vel', + "z_vel", ConstraintMagnitudeStateLimit( - limit_val=self.vel_limit, state_index=5, alpha=PolynomialConstraintStrengthener([0, 0.1, 0, 0.01]), bias=-0.001 - ) + limit_val=self.vel_limit, + state_index=5, + alpha=PolynomialConstraintStrengthener([0, 0.1, 0, 0.01]), + bias=-0.001, + ), ), ] ) if self.use_hocbf: - constraint_dict['chief_collision_hocbf'] = HOCBFConstraint( - HOCBFExampleChiefCollision(collision_radius=self.chief_radius + self.deputy_radius), + constraint_dict["chief_collision_hocbf"] = HOCBFConstraint( + HOCBFExampleChiefCollision( + collision_radius=self.chief_radius + self.deputy_radius + ), relative_degree=2, state_transition_system=self.state_transition_system, - alpha=PolynomialConstraintStrengthener([0, 0.1, 0, 0.1]) + alpha=PolynomialConstraintStrengthener([0, 0.1, 0, 0.1]), ) else: - constraint_dict['chief_collision'] = ConstraintCWHChiefCollision( - collision_radius=self.chief_radius + self.deputy_radius, a_max=self.a_max + constraint_dict["chief_collision"] = ConstraintCWHChiefCollision( + collision_radius=self.chief_radius + self.deputy_radius, + a_max=self.a_max, ) return constraint_dict - def _pred_state(self, state: jnp.ndarray, step_size: float, control: jnp.ndarray) -> jnp.ndarray: - sol = odeint(self.compute_state_dot, state, jnp.linspace(0., step_size, 11), control) + def _pred_state( + self, state: jnp.ndarray, step_size: float, control: jnp.ndarray + ) -> jnp.ndarray: + sol = odeint( + self.compute_state_dot, state, jnp.linspace(0.0, step_size, 11), control + ) return sol[-1, :] def compute_state_dot(self, x, t, u): - """Computes state dot for ODE integration - """ + """Computes state dot for ODE integration""" xd = self.A @ x[0:6] + self.B @ u + 0 * t delta_v = jnp.sum(jnp.abs(u)) / (self.m) return jnp.concatenate((xd, jnp.array([self.sun_vel, delta_v]))) - def _pred_state_cwh(self, state: jnp.ndarray, step_size: float, control: jnp.ndarray) -> jnp.ndarray: - """Predicted state for only CWH equations - """ - sol = odeint(self.compute_state_dot_cwh, state, jnp.linspace(0., step_size, 11), control) + def _pred_state_cwh( + self, state: jnp.ndarray, step_size: float, control: jnp.ndarray + ) -> jnp.ndarray: + """Predicted state for only CWH equations""" + sol = odeint( + self.compute_state_dot_cwh, state, jnp.linspace(0.0, step_size, 11), control + ) return sol[-1, :] def compute_state_dot_cwh(self, x, t, u): - """Computes state dot for ODE integration (only CWH equations) - """ + """Computes state dot for ODE integration (only CWH equations)""" xd = self.A @ x[0:6] + self.B @ u + 0 * t return xd @@ -301,18 +352,20 @@ def state_transition_input(self, state: jnp.ndarray) -> jnp.ndarray: return jnp.vstack((self.B, jnp.zeros((2, 3)))) def _get_state(self, input_state) -> jnp.ndarray: - assert isinstance(input_state, (np.ndarray, jnp.ndarray)), ("input_state must be an RTAState or numpy array.") + assert isinstance( + input_state, (np.ndarray, jnp.ndarray) + ), "input_state must be an RTAState or numpy array." input_state = np.array(input_state) if len(input_state) < 8: - input_state = np.concatenate((input_state, np.array([0., 0.]))) + input_state = np.concatenate((input_state, np.array([0.0, 0.0]))) self.sun_vel = 0 return to_jnp_array_jit(input_state) def get_sun_vector(self, state: jnp.ndarray) -> jnp.ndarray: """Function to get vector pointing from sun to chief""" - return -jnp.array([jnp.cos(state[6]), jnp.sin(state[6]), 0.]) + return -jnp.array([jnp.cos(state[6]), jnp.sin(state[6]), 0.0]) def get_pos_vector(self, state: jnp.ndarray) -> jnp.ndarray: """Function to get position vector""" @@ -366,8 +419,8 @@ class DiscreteInspection1v1RTA(DiscreteASIFModule): def __init__( self, *args, - m: float = M_DEFAULT, - n: float = N_DEFAULT, + m: float = defaults.M_DEFAULT, + n: float = defaults.N_DEFAULT, chief_radius: float = CHIEF_RADIUS_DEFAULT, deputy_radius: float = DEPUTY_RADIUS_DEFAULT, v0: float = V0_DEFAULT, @@ -378,9 +431,13 @@ def __init__( vel_limit: float = VEL_LIMIT_DEFAULT, delta_v_limit: float = DELTA_V_LIMIT_DEFAULT, sun_vel: float = SUN_VEL_DEFAULT, - control_bounds_high: Union[float, list, np.ndarray, jnp.ndarray] = U_MAX_DEFAULT, - control_bounds_low: Union[float, list, np.ndarray, jnp.ndarray] = -U_MAX_DEFAULT, - **kwargs + control_bounds_high: Union[ + float, list, np.ndarray, jnp.ndarray + ] = U_MAX_DEFAULT, + control_bounds_low: Union[ + float, list, np.ndarray, jnp.ndarray + ] = -U_MAX_DEFAULT, + **kwargs, ): self.m = m self.n = n @@ -398,7 +455,9 @@ def __init__( self.u_max = U_MAX_DEFAULT vmax = min(self.vel_limit, self.v0 + self.v1 * self.r_max) - self.a_max = self.u_max / self.m - 3 * self.n**2 * self.r_max - 2 * self.n * vmax + self.a_max = ( + self.u_max / self.m - 3 * self.n**2 * self.r_max - 2 * self.n * vmax + ) A, B = generate_cwh_matrices(self.m, self.n, mode="3d") self.A = jnp.array(A) self.B = jnp.array(B) @@ -406,25 +465,35 @@ def __init__( self.control_dim = self.B.shape[1] super().__init__( - *args, control_dim=self.control_dim, control_bounds_high=control_bounds_high, control_bounds_low=control_bounds_low, **kwargs + *args, + control_dim=self.control_dim, + control_bounds_high=control_bounds_high, + control_bounds_low=control_bounds_low, + **kwargs, ) def _setup_constraints(self) -> OrderedDict: constraint_dict = OrderedDict( [ - ('chief_collision', ConstraintCWHChiefCollision(collision_radius=self.chief_radius + self.deputy_radius, a_max=self.a_max)), ( - 'rel_vel', + "chief_collision", + ConstraintCWHChiefCollision( + collision_radius=self.chief_radius + self.deputy_radius, + a_max=self.a_max, + ), + ), + ( + "rel_vel", ConstraintCWH3dRelativeVelocity( v0=self.v0, v1=self.v1, v0_distance=self.v0_distance, bias=-1e-3, - alpha=PolynomialConstraintStrengthener([0, 0.01, 0, 0.001]) - ) + alpha=PolynomialConstraintStrengthener([0, 0.01, 0, 0.001]), + ), ), ( - 'sun', + "sun", ConstraintCWHConicKeepOutZone( a_max=self.a_max, fov=self.fov, @@ -434,50 +503,67 @@ def _setup_constraints(self) -> OrderedDict: cone_ang_vel=jnp.array([0, 0, self.sun_vel]), bias=-1e-3, alpha=PolynomialConstraintStrengthener([0, 0.001, 0, 0.001]), - ) + ), ), ( - 'r_max', + "r_max", ConstraintCWHMaxDistance( - r_max=self.r_max, a_max=self.a_max, bias=-1e-3, alpha=PolynomialConstraintStrengthener([0, 0.001, 0, 0.001]) - ) + r_max=self.r_max, + a_max=self.a_max, + bias=-1e-3, + alpha=PolynomialConstraintStrengthener([0, 0.001, 0, 0.001]), + ), ), ( - 'PSM', + "PSM", ConstraintPassivelySafeManeuver( collision_radius=self.chief_radius + self.deputy_radius, m=self.m, n=self.n, dt=1, steps=100, - alpha=PolynomialConstraintStrengthener([0, 0.001, 0, 0.001]) - ) + alpha=PolynomialConstraintStrengthener([0, 0.001, 0, 0.001]), + ), ), ( - 'x_vel', + "x_vel", ConstraintMagnitudeStateLimit( - limit_val=self.vel_limit, state_index=3, alpha=PolynomialConstraintStrengthener([0, 0.1, 0, 0.01]), bias=-0.001 - ) + limit_val=self.vel_limit, + state_index=3, + alpha=PolynomialConstraintStrengthener([0, 0.1, 0, 0.01]), + bias=-0.001, + ), ), ( - 'y_vel', + "y_vel", ConstraintMagnitudeStateLimit( - limit_val=self.vel_limit, state_index=4, alpha=PolynomialConstraintStrengthener([0, 0.1, 0, 0.01]), bias=-0.001 - ) + limit_val=self.vel_limit, + state_index=4, + alpha=PolynomialConstraintStrengthener([0, 0.1, 0, 0.01]), + bias=-0.001, + ), ), ( - 'z_vel', + "z_vel", ConstraintMagnitudeStateLimit( - limit_val=self.vel_limit, state_index=5, alpha=PolynomialConstraintStrengthener([0, 0.1, 0, 0.01]), bias=-0.001 - ) + limit_val=self.vel_limit, + state_index=5, + alpha=PolynomialConstraintStrengthener([0, 0.1, 0, 0.01]), + bias=-0.001, + ), ), ] ) return constraint_dict def next_state_differentiable(self, state, step_size, control): - next_state = self.discrete_a_mat(step_size) @ state[0:6] + self.discrete_b_mat(step_size) @ control - return jnp.concatenate((next_state, jnp.array([state[6] - self.sun_vel * step_size, 0.]))) # TODO: fix fuel derivative + next_state = ( + self.discrete_a_mat(step_size) @ state[0:6] + + self.discrete_b_mat(step_size) @ control + ) + return jnp.concatenate( + (next_state, jnp.array([state[6] - self.sun_vel * step_size, 0.0])) + ) # TODO: fix fuel derivative def discrete_a_mat(self, dt): """Discrete CWH dynamics""" @@ -486,7 +572,14 @@ def discrete_a_mat(self, dt): return jnp.array( [ [4 - 3 * c, 0, 0, 1 / self.n * s, 2 / self.n * (1 - c), 0], - [6 * (s - self.n * dt), 1, 0, -2 / self.n * (1 - c), 1 / self.n * (4 * s - 3 * self.n * dt), 0], + [ + 6 * (s - self.n * dt), + 1, + 0, + -2 / self.n * (1 - c), + 1 / self.n * (4 * s - 3 * self.n * dt), + 0, + ], [0, 0, c, 0, 0, 1 / self.n * s], [3 * self.n * s, 0, 0, c, 2 * s, 0], [-6 * self.n * (1 - c), 0, 0, -2 * s, 4 * c - 3, 0], @@ -498,20 +591,27 @@ def discrete_b_mat(self, dt): """Discrete CWH dynamics""" c = jnp.cos(self.n * dt) s = jnp.sin(self.n * dt) - return jnp.array( - [ - [1 / self.n**2 * (1 - c), 2 / self.n**2 * (self.n * dt - s), 0], - [-2 / self.n**2 * (self.n * dt - s), 4 / self.n**2 * (1 - c) - 3 / 2 * dt**2, 0], - [0, 0, 1 / self.n**2 * (1 - c)], - [1 / self.n * s, 2 / self.n * (1 - c), 0], - [-2 / self.n * (1 - c), 4 / self.n * s - 3 * dt, 0], - [0, 0, 1 / self.n * s], - ] - ) / self.m + return ( + jnp.array( + [ + [1 / self.n**2 * (1 - c), 2 / self.n**2 * (self.n * dt - s), 0], + [ + -2 / self.n**2 * (self.n * dt - s), + 4 / self.n**2 * (1 - c) - 3 / 2 * dt**2, + 0, + ], + [0, 0, 1 / self.n**2 * (1 - c)], + [1 / self.n * s, 2 / self.n * (1 - c), 0], + [-2 / self.n * (1 - c), 4 / self.n * s - 3 * dt, 0], + [0, 0, 1 / self.n * s], + ] + ) + / self.m + ) def get_sun_vector(self, state: jnp.ndarray) -> jnp.ndarray: """Function to get vector pointing from sun to chief""" - return -jnp.array([jnp.cos(state[6]), jnp.sin(state[6]), 0.]) + return -jnp.array([jnp.cos(state[6]), jnp.sin(state[6]), 0.0]) def get_pos_vector(self, state: jnp.ndarray) -> jnp.ndarray: """Function to get position vector""" @@ -545,13 +645,13 @@ class SwitchingDeltaVLimitRTA(RTAModule): def __init__( self, *args, - m: float = M_DEFAULT, - n: float = N_DEFAULT, + m: float = defaults.M_DEFAULT, + n: float = defaults.N_DEFAULT, delta_v_limit: float = DELTA_V_LIMIT_DEFAULT, n_steps: int = 500, control_bounds_high: Union[float, np.ndarray] = U_MAX_DEFAULT, control_bounds_low: Union[float, np.ndarray] = -U_MAX_DEFAULT, - **kwargs + **kwargs, ): self.m = m self.n = n @@ -566,7 +666,9 @@ def __init__( C = np.eye(6) self.A, self.B = generate_cwh_matrices(m, self.n, mode="3d") - A_int = np.vstack((np.hstack((self.A, np.zeros((6, 6)))), np.hstack((C, np.zeros((6, 6)))))) + A_int = np.vstack( + (np.hstack((self.A, np.zeros((6, 6)))), np.hstack((C, np.zeros((6, 6))))) + ) B_int = np.vstack((self.B, np.zeros((6, 3)))) # Solve the Algebraic Ricatti equation for the given system P = scipy.linalg.solve_continuous_are(A_int, B_int, Q, R) @@ -577,43 +679,66 @@ def __init__( self.latched = False - super().__init__(*args, control_bounds_high=control_bounds_high, control_bounds_low=control_bounds_low, **kwargs) + super().__init__( + *args, + control_bounds_high=control_bounds_high, + control_bounds_low=control_bounds_low, + **kwargs, + ) - def _pred_state(self, state: np.ndarray, step_size: float, control: np.ndarray) -> np.ndarray: + def _pred_state( + self, state: np.ndarray, step_size: float, control: np.ndarray + ) -> np.ndarray: xd = self.A @ state[0:6] + self.B @ control sun_dot = np.array([-self.n]) delta_v_dot = np.array([np.sum(np.abs(control)) / (self.m)]) return state + np.concatenate((xd, sun_dot, delta_v_dot)) * step_size - def compute_filtered_control(self, input_state, step_size: float, control_desired: np.ndarray) -> np.ndarray: + def compute_filtered_control( + self, input_state, step_size: float, control_desired: np.ndarray + ) -> np.ndarray: if not self.latched: pred_state = self._pred_state(input_state, step_size, control_desired) error_integral = self.error_integral for _ in range(self.n_steps): - ub, error_integral = self.backup_control(pred_state, step_size, error_integral) + ub, error_integral = self.backup_control( + pred_state, step_size, error_integral + ) pred_state = self._pred_state(pred_state, step_size, ub) if pred_state[7] > self.delta_v_limit: - ub, self.error_integral = self.backup_control(input_state, step_size, self.error_integral) + ub, self.error_integral = self.backup_control( + input_state, step_size, self.error_integral + ) self.latched = True self.intervening = True out = ub out = control_desired else: - ub, self.error_integral = self.backup_control(input_state, step_size, self.error_integral) + ub, self.error_integral = self.backup_control( + input_state, step_size, self.error_integral + ) out = ub return out def backup_control(self, state, step_size, error_integral): """LQT backup controller to eNMT""" - error = np.array([0, 0, 0, state[3] - self.n / 2 * state[1], state[4] + 2 * self.n * state[0], 0]) + error = np.array( + [ + 0, + 0, + 0, + state[3] - self.n / 2 * state[1], + state[4] + 2 * self.n * state[0], + 0, + ] + ) backup_action = -self.K_1 @ error - self.K_2 @ error_integral error_integral = error_integral + error * step_size return np.clip(backup_action, -1, 1), error_integral class InspectionCascadedRTA(CascadedRTA): - """Combines ASIF Inspection RTA with switching-based delta v limit - """ + """Combines ASIF Inspection RTA with switching-based delta v limit""" def _setup_rta_list(self): return [Inspection1v1RTA(), SwitchingDeltaVLimitRTA()] @@ -633,36 +758,56 @@ class ConstraintCWHChiefCollision(ConstraintModule): Defaults to PolynomialConstraintStrengthener([0, 0.005, 0, 0.05]) """ - def __init__(self, collision_radius: float, a_max: float, alpha: ConstraintStrengthener = None, **kwargs): + def __init__( + self, + collision_radius: float, + a_max: float, + alpha: ConstraintStrengthener = None, + **kwargs, + ): if alpha is None: alpha = PolynomialConstraintStrengthener([0, 0.01, 0, 0.001]) - super().__init__(alpha=alpha, params={'collision_radius': collision_radius, 'a_max': a_max}, **kwargs) + super().__init__( + alpha=alpha, + params={"collision_radius": collision_radius, "a_max": a_max}, + **kwargs, + ) def _compute(self, state: jnp.ndarray, params: dict) -> float: delta_p = state[0:3] mag_delta_p = jnp.linalg.norm(delta_p) h = lax.cond( - mag_delta_p >= params['collision_radius'], self.positive_distance_constraint, self.negative_distance_constraint, state, params + mag_delta_p >= params["collision_radius"], + self.positive_distance_constraint, + self.negative_distance_constraint, + state, + params, ) return h def _phi(self, state: jnp.ndarray, params: dict) -> float: delta_p = state[0:3] - return jnp.linalg.norm(delta_p) - params['collision_radius'] + return jnp.linalg.norm(delta_p) - params["collision_radius"] def positive_distance_constraint(self, state, params): """Constraint value when sqrt component is real""" delta_p = state[0:3] delta_v = state[3:6] mag_delta_p = jnp.linalg.norm(delta_p) - return jnp.sqrt(2 * params['a_max'] * (mag_delta_p - params['collision_radius'])) + delta_p.T @ delta_v / mag_delta_p + return ( + jnp.sqrt(2 * params["a_max"] * (mag_delta_p - params["collision_radius"])) + + delta_p.T @ delta_v / mag_delta_p + ) def negative_distance_constraint(self, state, params): """Constraint value when sqrt component is imaginary""" delta_p = state[0:3] delta_v = state[3:6] mag_delta_p = jnp.linalg.norm(delta_p) - return -jnp.sqrt(2 * params['a_max'] * (-mag_delta_p + params['collision_radius'])) + delta_p.T @ delta_v / mag_delta_p + return ( + -jnp.sqrt(2 * params["a_max"] * (-mag_delta_p + params["collision_radius"])) + + delta_p.T @ delta_v / mag_delta_p + ) class ConstraintCWHConicKeepOutZone(ConstraintModule): @@ -693,7 +838,7 @@ def __init__( get_cone_vec, cone_ang_vel: jnp.ndarray, alpha: ConstraintStrengthener = None, - **kwargs + **kwargs, ): self.get_pos = get_pos self.get_vel = get_vel @@ -702,7 +847,7 @@ def __init__( if alpha is None: alpha = PolynomialConstraintStrengthener([0, 0.001, 0, 0.0001]) - super().__init__(alpha=alpha, params={'fov': fov, 'a_max': a_max}, **kwargs) + super().__init__(alpha=alpha, params={"fov": fov, "a_max": a_max}, **kwargs) def _compute(self, state: jnp.ndarray, params: dict) -> float: pos = self.get_pos(state) @@ -710,17 +855,27 @@ def _compute(self, state: jnp.ndarray, params: dict) -> float: cone_vec = self.get_cone_vec(state) cone_unit_vec = cone_vec / jnp.linalg.norm(cone_vec) cone_ang_vel = self.cone_ang_vel - theta = params['fov'] / 2 + theta = params["fov"] / 2 pos_cone = pos - jnp.dot(pos, cone_unit_vec) * cone_unit_vec - mult = jnp.cos(theta) * (jnp.linalg.norm(pos_cone) - jnp.tan(theta) * jnp.dot(pos, cone_unit_vec)) - proj = pos + mult * jnp.sin(theta) * cone_unit_vec + mult * jnp.cos(theta) * (jnp.dot(pos, cone_unit_vec) * cone_unit_vec - - pos) / jnp.linalg.norm(pos_cone) + mult = jnp.cos(theta) * ( + jnp.linalg.norm(pos_cone) - jnp.tan(theta) * jnp.dot(pos, cone_unit_vec) + ) + proj = ( + pos + + mult * jnp.sin(theta) * cone_unit_vec + + mult + * jnp.cos(theta) + * (jnp.dot(pos, cone_unit_vec) * cone_unit_vec - pos) + / jnp.linalg.norm(pos_cone) + ) vel_proj = jnp.cross(cone_ang_vel, proj) delta_p = pos - proj delta_v = vel - vel_proj - mag_delta_p = jnp.linalg.norm(pos) * jnp.sin(jnp.arccos(jnp.dot(cone_unit_vec, pos / jnp.linalg.norm(pos))) - theta) + mag_delta_p = jnp.linalg.norm(pos) * jnp.sin( + jnp.arccos(jnp.dot(cone_unit_vec, pos / jnp.linalg.norm(pos))) - theta + ) h = lax.cond( mag_delta_p >= 0, @@ -729,7 +884,7 @@ def _compute(self, state: jnp.ndarray, params: dict) -> float: delta_p, delta_v, mag_delta_p, - params['a_max'] + params["a_max"], ) return h @@ -738,7 +893,7 @@ def _phi(self, state: jnp.ndarray, params: dict) -> float: cone_vec = self.get_cone_vec(state) p_hat = pos / jnp.linalg.norm(pos) c_hat = cone_vec / jnp.linalg.norm(cone_vec) - h = jnp.arccos(jnp.dot(p_hat, c_hat)) - params['fov'] / 2 + h = jnp.arccos(jnp.dot(p_hat, c_hat)) - params["fov"] / 2 return h def positive_distance_constraint(self, delta_p, delta_v, mag_delta_p, a_max): @@ -771,7 +926,16 @@ class ConstraintPassivelySafeManeuver(ConstraintModule): Defaults to PolynomialConstraintStrengthener([0, 0.01, 0, 0.001]) """ - def __init__(self, collision_radius: float, m: float, n: float, dt: float, steps: int, alpha: ConstraintStrengthener = None, **kwargs): + def __init__( + self, + collision_radius: float, + m: float, + n: float, + dt: float, + steps: int, + alpha: ConstraintStrengthener = None, + **kwargs, + ): self.n = n self.steps = steps A, _ = generate_cwh_matrices(m, n, mode="3d") @@ -779,30 +943,45 @@ def __init__(self, collision_radius: float, m: float, n: float, dt: float, steps if alpha is None: alpha = PolynomialConstraintStrengthener([0, 0.01, 0, 0.001]) - super().__init__(alpha=alpha, params={'collision_radius': collision_radius, 'dt': dt}, **kwargs) + super().__init__( + alpha=alpha, + params={"collision_radius": collision_radius, "dt": dt}, + **kwargs, + ) def _compute(self, state: jnp.ndarray, params: dict) -> float: vmapped_get_future_state = vmap(self.get_future_state, (None, 0, None), 0) phi_array = vmapped_get_future_state( - state, jnp.linspace(params['dt'], self.steps * params['dt'], self.steps), params['collision_radius'] + state, + jnp.linspace(params["dt"], self.steps * params["dt"], self.steps), + params["collision_radius"], ) return jnp.min(phi_array) def get_future_state(self, state, t, collision_radius): - """Gets future state using closed form CWH dynamics (http://www.ae.utexas.edu/courses/ase366k/cw_equations.pdf) - """ - x = (4 - 3 * jnp.cos(self.n * t)) * state[0] + jnp.sin(self.n * t - ) * state[3] / self.n + 2 / self.n * (1 - jnp.cos(self.n * t)) * state[4] - y = 6 * (jnp.sin(self.n * t) - self.n * t) * state[0] + state[ - 1] - 2 / self.n * (1 - jnp.cos(self.n * t)) * state[3] + (4 * jnp.sin(self.n * t) - 3 * self.n * t) * state[4] / self.n + """Gets future state using closed form CWH dynamics (http://www.ae.utexas.edu/courses/ase366k/cw_equations.pdf)""" + x = ( + (4 - 3 * jnp.cos(self.n * t)) * state[0] + + jnp.sin(self.n * t) * state[3] / self.n + + 2 / self.n * (1 - jnp.cos(self.n * t)) * state[4] + ) + y = ( + 6 * (jnp.sin(self.n * t) - self.n * t) * state[0] + + state[1] + - 2 / self.n * (1 - jnp.cos(self.n * t)) * state[3] + + (4 * jnp.sin(self.n * t) - 3 * self.n * t) * state[4] / self.n + ) z = state[2] * jnp.cos(self.n * t) + state[5] / self.n * jnp.sin(self.n * t) return jnp.linalg.norm(jnp.array([x, y, z])) - collision_radius def get_array(self, state: jnp.ndarray, params: dict) -> float: - """Gets entire trajectory array - """ + """Gets entire trajectory array""" vmapped_get_future_state = vmap(self.get_future_state, (None, 0, None), 0) - phi_array = vmapped_get_future_state(state, jnp.linspace(0, self.steps * params['dt'], self.steps + 1), params['collision_radius']) + phi_array = vmapped_get_future_state( + state, + jnp.linspace(0, self.steps * params["dt"], self.steps + 1), + params["collision_radius"], + ) return phi_array @@ -820,34 +999,48 @@ class ConstraintCWHMaxDistance(ConstraintModule): Defaults to PolynomialConstraintStrengthener([0, 0.005, 0, 0.05]) """ - def __init__(self, r_max: float, a_max: float, alpha: ConstraintStrengthener = None, **kwargs): + def __init__( + self, r_max: float, a_max: float, alpha: ConstraintStrengthener = None, **kwargs + ): if alpha is None: alpha = PolynomialConstraintStrengthener([0, 0.01, 0, 0.001]) - super().__init__(alpha=alpha, params={'r_max': r_max, 'a_max': a_max}, **kwargs) + super().__init__(alpha=alpha, params={"r_max": r_max, "a_max": a_max}, **kwargs) def _compute(self, state: jnp.ndarray, params: dict) -> float: delta_p = state[0:3] mag_delta_p = jnp.linalg.norm(delta_p) - h = lax.cond(params['r_max'] >= mag_delta_p, self.positive_distance_constraint, self.negative_distance_constraint, state, params) + h = lax.cond( + params["r_max"] >= mag_delta_p, + self.positive_distance_constraint, + self.negative_distance_constraint, + state, + params, + ) return h def _phi(self, state: jnp.ndarray, params: dict) -> float: delta_p = state[0:3] - return params['r_max'] - jnp.linalg.norm(delta_p) + return params["r_max"] - jnp.linalg.norm(delta_p) def positive_distance_constraint(self, state, params): """Constraint value when sqrt component is real""" delta_p = state[0:3] delta_v = state[3:6] mag_delta_p = jnp.linalg.norm(delta_p) - return jnp.sqrt(2 * params['a_max'] * (params['r_max'] - mag_delta_p)) - delta_p.T @ delta_v / mag_delta_p + return ( + jnp.sqrt(2 * params["a_max"] * (params["r_max"] - mag_delta_p)) + - delta_p.T @ delta_v / mag_delta_p + ) def negative_distance_constraint(self, state, params): """Constraint value when sqrt component is imaginary""" delta_p = state[0:3] delta_v = state[3:6] mag_delta_p = jnp.linalg.norm(delta_p) - return -jnp.sqrt(2 * params['a_max'] * (-params['r_max'] + mag_delta_p)) - delta_p.T @ delta_v / mag_delta_p + return ( + -jnp.sqrt(2 * params["a_max"] * (-params["r_max"] + mag_delta_p)) + - delta_p.T @ delta_v / mag_delta_p + ) class HOCBFExampleChiefCollision(ConstraintModule): @@ -862,11 +1055,15 @@ class HOCBFExampleChiefCollision(ConstraintModule): Defaults to PolynomialConstraintStrengthener([0, 0.01, 0, 0.01]) """ - def __init__(self, collision_radius: float, alpha: ConstraintStrengthener = None, **kwargs): + def __init__( + self, collision_radius: float, alpha: ConstraintStrengthener = None, **kwargs + ): if alpha is None: alpha = PolynomialConstraintStrengthener([0, 0.01, 0, 0.01]) - super().__init__(alpha=alpha, params={'collision_radius': collision_radius}, **kwargs) + super().__init__( + alpha=alpha, params={"collision_radius": collision_radius}, **kwargs + ) def _compute(self, state: jnp.ndarray, params: dict) -> float: delta_p = state[0:3] - return jnp.linalg.norm(delta_p) - params['collision_radius'] + return jnp.linalg.norm(delta_p) - params["collision_radius"] diff --git a/run_time_assurance/zoo/cwh/inspection_3d.py b/run_time_assurance/zoo/cwh/inspection_3d.py index b69ab2b..f64e651 100644 --- a/run_time_assurance/zoo/cwh/inspection_3d.py +++ b/run_time_assurance/zoo/cwh/inspection_3d.py @@ -1,5 +1,5 @@ -"""This module implements RTA methods for the multiagent inspection problem with 3D CWH dynamics models -""" +"""This module implements RTA methods for the multiagent inspection problem with 3D CWH dynamics models""" + from collections import OrderedDict from functools import partial from typing import Any, Union @@ -8,7 +8,7 @@ import numpy as np import scipy from jax import lax, vmap -from safe_autonomy_dynamics.cwh.point_model import M_DEFAULT, N_DEFAULT, generate_cwh_matrices +import safe_autonomy_simulation.sims.spacecraft.defaults as defaults from run_time_assurance.constraint import ( ConstraintMagnitudeStateLimit, @@ -35,6 +35,7 @@ ConstraintCWHMaxDistance, ConstraintPassivelySafeManeuver, ) +from run_time_assurance.zoo.cwh.utils import generate_cwh_matrices NUM_DEPUTIES_DEFAULT = 5 # Number of deputies for inspection problem @@ -83,8 +84,8 @@ def __init__( self, *args, num_deputies: int = NUM_DEPUTIES_DEFAULT, - m: float = M_DEFAULT, - n: float = N_DEFAULT, + m: float = defaults.M_DEFAULT, + n: float = defaults.N_DEFAULT, chief_radius: float = CHIEF_RADIUS_DEFAULT, deputy_radius: float = DEPUTY_RADIUS_DEFAULT, v0: float = V0_DEFAULT, @@ -94,9 +95,13 @@ def __init__( fov: float = FOV_DEFAULT, vel_limit: float = VEL_LIMIT_DEFAULT, sun_vel: float = SUN_VEL_DEFAULT, - control_bounds_high: Union[float, list, np.ndarray, jnp.ndarray] = U_MAX_DEFAULT, - control_bounds_low: Union[float, list, np.ndarray, jnp.ndarray] = -U_MAX_DEFAULT, - **kwargs + control_bounds_high: Union[ + float, list, np.ndarray, jnp.ndarray + ] = U_MAX_DEFAULT, + control_bounds_low: Union[ + float, list, np.ndarray, jnp.ndarray + ] = -U_MAX_DEFAULT, + **kwargs, ): self.num_deputies = num_deputies self.m = m @@ -114,7 +119,9 @@ def __init__( self.u_max = U_MAX_DEFAULT vmax = min(self.vel_limit, self.v0 + self.v1 * self.r_max) - self.a_max = self.u_max / self.m - 3 * self.n**2 * self.r_max - 2 * self.n * vmax + self.a_max = ( + self.u_max / self.m - 3 * self.n**2 * self.r_max - 2 * self.n * vmax + ) A, B = generate_cwh_matrices(self.m, self.n, mode="3d") self.A = jnp.array(A) self.B = jnp.array(B) @@ -132,25 +139,35 @@ def __init__( self.control_dim = self.B.shape[1] super().__init__( - *args, control_dim=self.control_dim, control_bounds_high=control_bounds_high, control_bounds_low=control_bounds_low, **kwargs + *args, + control_dim=self.control_dim, + control_bounds_high=control_bounds_high, + control_bounds_low=control_bounds_low, + **kwargs, ) def _setup_constraints(self) -> OrderedDict: constraint_dict = OrderedDict( [ - ('chief_collision', ConstraintCWHChiefCollision(collision_radius=self.chief_radius + self.deputy_radius, a_max=self.a_max)), ( - 'rel_vel', + "chief_collision", + ConstraintCWHChiefCollision( + collision_radius=self.chief_radius + self.deputy_radius, + a_max=self.a_max, + ), + ), + ( + "rel_vel", ConstraintCWH3dRelativeVelocity( v0=self.v0, v1=self.v1, v0_distance=self.v0_distance, bias=-2e-3, - alpha=PolynomialConstraintStrengthener([0, 0, 0, .1]) - ) + alpha=PolynomialConstraintStrengthener([0, 0, 0, 0.1]), + ), ), ( - 'chief_sun', + "chief_sun", ConstraintCWHConicKeepOutZone( a_max=self.a_max, fov=self.fov, @@ -158,40 +175,54 @@ def _setup_constraints(self) -> OrderedDict: get_vel=self.get_vel_vector, get_cone_vec=self.get_sun_vector, cone_ang_vel=jnp.array([0, 0, self.sun_vel]), - bias=-1e-3 - ) - ), ('r_max', ConstraintCWHMaxDistance(r_max=self.r_max, a_max=self.a_max)), + bias=-1e-3, + ), + ), + ("r_max", ConstraintCWHMaxDistance(r_max=self.r_max, a_max=self.a_max)), ( - 'PSM', + "PSM", ConstraintPassivelySafeManeuver( - collision_radius=self.chief_radius + self.deputy_radius, m=self.m, n=self.n, dt=1, steps=100 - ) + collision_radius=self.chief_radius + self.deputy_radius, + m=self.m, + n=self.n, + dt=1, + steps=100, + ), ), ( - 'x_vel', + "x_vel", ConstraintMagnitudeStateLimit( - limit_val=self.vel_limit, state_index=3, alpha=PolynomialConstraintStrengthener([0, 0.1, 0, 0.01]), bias=-0.001 - ) + limit_val=self.vel_limit, + state_index=3, + alpha=PolynomialConstraintStrengthener([0, 0.1, 0, 0.01]), + bias=-0.001, + ), ), ( - 'y_vel', + "y_vel", ConstraintMagnitudeStateLimit( - limit_val=self.vel_limit, state_index=4, alpha=PolynomialConstraintStrengthener([0, 0.1, 0, 0.01]), bias=-0.001 - ) + limit_val=self.vel_limit, + state_index=4, + alpha=PolynomialConstraintStrengthener([0, 0.1, 0, 0.01]), + bias=-0.001, + ), ), ( - 'z_vel', + "z_vel", ConstraintMagnitudeStateLimit( - limit_val=self.vel_limit, state_index=5, alpha=PolynomialConstraintStrengthener([0, 0.1, 0, 0.01]), bias=-0.001 - ) - ) + limit_val=self.vel_limit, + state_index=5, + alpha=PolynomialConstraintStrengthener([0, 0.1, 0, 0.01]), + bias=-0.001, + ), + ), ] ) for i in range(1, self.num_deputies): - constraint_dict[f'deputy_collision_{i}'] = ConstraintCWHDeputyCollision( + constraint_dict[f"deputy_collision_{i}"] = ConstraintCWHDeputyCollision( collision_radius=self.deputy_radius * 2, a_max=self.a_max, deputy=i ) - constraint_dict[f'deputy_sun_{i}'] = ConstraintCWHConicKeepOutZone( + constraint_dict[f"deputy_sun_{i}"] = ConstraintCWHConicKeepOutZone( a_max=self.a_max, fov=self.fov, get_pos=partial(self.get_p1_p2, i), @@ -199,15 +230,20 @@ def _setup_constraints(self) -> OrderedDict: get_cone_vec=self.get_sun_vector, cone_ang_vel=jnp.array([0, 0, self.sun_vel]), bias=-1e-3, - alpha=PolynomialConstraintStrengthener([0, 0, 0, .1]) + alpha=PolynomialConstraintStrengthener([0, 0, 0, 0.1]), ) - constraint_dict[f'deputy_PSM_{i}'] = ConstraintPSMDeputy( - collision_radius=self.chief_radius + self.deputy_radius, m=self.m, n=self.n, dt=1, steps=100, deputy=i + constraint_dict[f"deputy_PSM_{i}"] = ConstraintPSMDeputy( + collision_radius=self.chief_radius + self.deputy_radius, + m=self.m, + n=self.n, + dt=1, + steps=100, + deputy=i, ) return constraint_dict def state_transition_system(self, state: jnp.ndarray) -> jnp.ndarray: - cwh = self.A_n @ state[0:self.num_deputies * 6] + cwh = self.A_n @ state[0 : self.num_deputies * 6] return jnp.concatenate((cwh, jnp.array([self.sun_vel]))) def state_transition_input(self, state: jnp.ndarray) -> jnp.ndarray: @@ -215,18 +251,20 @@ def state_transition_input(self, state: jnp.ndarray) -> jnp.ndarray: return jnp.vstack((cwh, jnp.zeros((1, 3)))) def _get_state(self, input_state) -> jnp.ndarray: - assert isinstance(input_state, (np.ndarray, jnp.ndarray)), ("input_state must be an RTAState or numpy array.") + assert isinstance( + input_state, (np.ndarray, jnp.ndarray) + ), "input_state must be an RTAState or numpy array." input_state = np.array(input_state) if len(input_state) < 6 * self.num_deputies + 1: - input_state = np.concatenate((input_state, np.array([0.]))) + input_state = np.concatenate((input_state, np.array([0.0]))) self.sun_vel = 0 return to_jnp_array_jit(input_state) def get_sun_vector(self, state: jnp.ndarray) -> jnp.ndarray: """Function to get vector pointing from chief to sun""" - return -jnp.array([jnp.cos(state[-1]), jnp.sin(state[-1]), 0.]) + return -jnp.array([jnp.cos(state[-1]), jnp.sin(state[-1]), 0.0]) def get_pos_vector(self, state: jnp.ndarray) -> jnp.ndarray: """Function to get position vector""" @@ -238,23 +276,25 @@ def get_vel_vector(self, state: jnp.ndarray) -> jnp.ndarray: def get_p1_p2(self, i: int, state: jnp.ndarray) -> jnp.ndarray: """Function to get p1-p2 vector""" - pos = state[0:3] - state[6 * i:6 * i + 3] + pos = state[0:3] - state[6 * i : 6 * i + 3] sign = self.get_sign(i, state) return sign * pos def get_v1_v2(self, i: int, state: jnp.ndarray) -> jnp.ndarray: """Function to get v1-v2 vector""" - vel = state[3:6] - state[6 * i + 3:6 * i + 6] + vel = state[3:6] - state[6 * i + 3 : 6 * i + 6] sign = self.get_sign(i, state) return sign * vel def get_sign(self, i, state): """Gets sign for pos/vel""" - pos = state[0:3] - state[6 * i:6 * i + 3] + pos = state[0:3] - state[6 * i : 6 * i + 3] cone_vec = self.get_sun_vector(state) p_hat = pos / jnp.linalg.norm(pos) c_hat = cone_vec / jnp.linalg.norm(cone_vec) - return lax.cond(jnp.arccos(jnp.dot(p_hat, c_hat)) > jnp.pi / 2, lambda x: -1, lambda x: 1, 0) + return lax.cond( + jnp.arccos(jnp.dot(p_hat, c_hat)) > jnp.pi / 2, lambda x: -1, lambda x: 1, 0 + ) class ConstraintCWHDeputyCollision(ConstraintModule): @@ -273,38 +313,63 @@ class ConstraintCWHDeputyCollision(ConstraintModule): Defaults to PolynomialConstraintStrengthener([0, 0.005, 0, 0.05]) """ - def __init__(self, collision_radius: float, a_max: float, deputy: int, alpha: ConstraintStrengthener = None, **kwargs): + def __init__( + self, + collision_radius: float, + a_max: float, + deputy: int, + alpha: ConstraintStrengthener = None, + **kwargs, + ): self.deputy = deputy if alpha is None: alpha = PolynomialConstraintStrengthener([0, 0.01, 0, 0.001]) - super().__init__(alpha=alpha, params={'collision_radius': collision_radius, 'a_max': a_max}, **kwargs) + super().__init__( + alpha=alpha, + params={"collision_radius": collision_radius, "a_max": a_max}, + **kwargs, + ) def _compute(self, state: jnp.ndarray, params: dict) -> float: - delta_p = state[0:3] - state[int(self.deputy * 6):int(self.deputy * 6 + 3)] + delta_p = state[0:3] - state[int(self.deputy * 6) : int(self.deputy * 6 + 3)] mag_delta_p = jnp.linalg.norm(delta_p) h = lax.cond( - mag_delta_p >= params['collision_radius'], self.positive_distance_constraint, self.negative_distance_constraint, state, params + mag_delta_p >= params["collision_radius"], + self.positive_distance_constraint, + self.negative_distance_constraint, + state, + params, ) return h def _phi(self, state: jnp.ndarray, params: dict) -> float: - delta_p = state[0:3] - state[int(self.deputy * 6):int(self.deputy * 6 + 3)] - return jnp.linalg.norm(delta_p) - params['collision_radius'] + delta_p = state[0:3] - state[int(self.deputy * 6) : int(self.deputy * 6 + 3)] + return jnp.linalg.norm(delta_p) - params["collision_radius"] def positive_distance_constraint(self, state, params): """Constraint value when sqrt component is real""" - delta_p = state[0:3] - state[int(self.deputy * 6):int(self.deputy * 6 + 3)] - delta_v = state[3:6] - state[int(self.deputy * 6 + 3):int(self.deputy * 6 + 6)] + delta_p = state[0:3] - state[int(self.deputy * 6) : int(self.deputy * 6 + 3)] + delta_v = ( + state[3:6] - state[int(self.deputy * 6 + 3) : int(self.deputy * 6 + 6)] + ) mag_delta_p = jnp.linalg.norm(delta_p) - return jnp.sqrt(4 * params['a_max'] * (mag_delta_p - params['collision_radius'])) + delta_p.T @ delta_v / mag_delta_p + return ( + jnp.sqrt(4 * params["a_max"] * (mag_delta_p - params["collision_radius"])) + + delta_p.T @ delta_v / mag_delta_p + ) def negative_distance_constraint(self, state, params): """Constraint value when sqrt component is imaginary""" - delta_p = state[0:3] - state[int(self.deputy * 6):int(self.deputy * 6 + 3)] - delta_v = state[3:6] - state[int(self.deputy * 6 + 3):int(self.deputy * 6 + 6)] + delta_p = state[0:3] - state[int(self.deputy * 6) : int(self.deputy * 6 + 3)] + delta_v = ( + state[3:6] - state[int(self.deputy * 6 + 3) : int(self.deputy * 6 + 6)] + ) mag_delta_p = jnp.linalg.norm(delta_p) - return -jnp.sqrt(4 * params['a_max'] * (-mag_delta_p + params['collision_radius'])) + delta_p.T @ delta_v / mag_delta_p + return ( + -jnp.sqrt(4 * params["a_max"] * (-mag_delta_p + params["collision_radius"])) + + delta_p.T @ delta_v / mag_delta_p + ) class ConstraintPSMDeputy(ConstraintModule): @@ -339,7 +404,7 @@ def __init__( steps: int, deputy: int, alpha: ConstraintStrengthener = None, - **kwargs + **kwargs, ): self.n = n self.steps = steps @@ -349,38 +414,63 @@ def __init__( if alpha is None: alpha = PolynomialConstraintStrengthener([0, 0.01, 0, 0.001]) - super().__init__(alpha=alpha, params={'collision_radius': collision_radius, 'dt': dt}, **kwargs) + super().__init__( + alpha=alpha, + params={"collision_radius": collision_radius, "dt": dt}, + **kwargs, + ) def _compute(self, state: jnp.ndarray, params: dict) -> float: vmapped_get_future_state = vmap(self.get_future_state, (None, 0, None), 0) phi_array = vmapped_get_future_state( - state, jnp.linspace(params['dt'], self.steps * params['dt'], self.steps), params['collision_radius'] + state, + jnp.linspace(params["dt"], self.steps * params["dt"], self.steps), + params["collision_radius"], ) return jnp.min(phi_array) def get_future_state(self, state, t, collision_radius): - """Gets future state using closed form CWH dynamics (http://www.ae.utexas.edu/courses/ase366k/cw_equations.pdf) - """ - x = (4 - 3 * jnp.cos(self.n * t)) * state[0] + jnp.sin(self.n * t - ) * state[3] / self.n + 2 / self.n * (1 - jnp.cos(self.n * t)) * state[4] - y = 6 * (jnp.sin(self.n * t) - self.n * t) * state[0] + state[ - 1] - 2 / self.n * (1 - jnp.cos(self.n * t)) * state[3] + (4 * jnp.sin(self.n * t) - 3 * self.n * t) * state[4] / self.n + """Gets future state using closed form CWH dynamics (http://www.ae.utexas.edu/courses/ase366k/cw_equations.pdf)""" + x = ( + (4 - 3 * jnp.cos(self.n * t)) * state[0] + + jnp.sin(self.n * t) * state[3] / self.n + + 2 / self.n * (1 - jnp.cos(self.n * t)) * state[4] + ) + y = ( + 6 * (jnp.sin(self.n * t) - self.n * t) * state[0] + + state[1] + - 2 / self.n * (1 - jnp.cos(self.n * t)) * state[3] + + (4 * jnp.sin(self.n * t) - 3 * self.n * t) * state[4] / self.n + ) z = state[2] * jnp.cos(self.n * t) + state[5] / self.n * jnp.sin(self.n * t) - xd = (4 - 3 * jnp.cos(self.n * t)) * state[int(self.deputy * 6)] + jnp.sin(self.n * t) * state[ - int(self.deputy * 6) + 3] / self.n + 2 / self.n * (1 - jnp.cos(self.n * t)) * state[int(self.deputy * 6) + 4] - yd = 6 * (jnp.sin(self.n * t) - self.n * t) * state[int(self.deputy * 6) + 0] + state[int(self.deputy * 6) + 1] - 2 / self.n * ( - 1 - jnp.cos(self.n * t) - ) * state[int(self.deputy * 6) + 3] + (4 * jnp.sin(self.n * t) - 3 * self.n * t) * state[int(self.deputy * 6) + 4] / self.n - zd = state[int(self.deputy * 6) + 2] * jnp.cos(self.n * t) + state[int(self.deputy * 6) + 5] / self.n * jnp.sin(self.n * t) + xd = ( + (4 - 3 * jnp.cos(self.n * t)) * state[int(self.deputy * 6)] + + jnp.sin(self.n * t) * state[int(self.deputy * 6) + 3] / self.n + + 2 / self.n * (1 - jnp.cos(self.n * t)) * state[int(self.deputy * 6) + 4] + ) + yd = ( + 6 * (jnp.sin(self.n * t) - self.n * t) * state[int(self.deputy * 6) + 0] + + state[int(self.deputy * 6) + 1] + - 2 / self.n * (1 - jnp.cos(self.n * t)) * state[int(self.deputy * 6) + 3] + + (4 * jnp.sin(self.n * t) - 3 * self.n * t) + * state[int(self.deputy * 6) + 4] + / self.n + ) + zd = state[int(self.deputy * 6) + 2] * jnp.cos(self.n * t) + state[ + int(self.deputy * 6) + 5 + ] / self.n * jnp.sin(self.n * t) return jnp.linalg.norm(jnp.array([x - xd, y - yd, z - zd])) - collision_radius def get_array(self, state: jnp.ndarray, params: dict) -> float: - """Gets entire trajectory array - """ + """Gets entire trajectory array""" vmapped_get_future_state = vmap(self.get_future_state, (None, 0), 0) - phi_array = vmapped_get_future_state(state, jnp.linspace(0, self.steps * params['dt'], self.steps + 1), params['collision_radius']) + phi_array = vmapped_get_future_state( + state, + jnp.linspace(0, self.steps * params["dt"], self.steps + 1), + params["collision_radius"], + ) return phi_array @@ -407,23 +497,32 @@ def __init__( deputy_rta: RTAModule = InspectionRTA(), control_bounds_high: Union[float, int, list, np.ndarray] = U_MAX_DEFAULT, control_bounds_low: Union[float, int, list, np.ndarray] = -U_MAX_DEFAULT, - **kwargs: Any + **kwargs: Any, ): self.num_deputies = num_deputies self.deputy_rta = deputy_rta - super().__init__(*args, control_bounds_high=control_bounds_high, control_bounds_low=control_bounds_low, **kwargs) + super().__init__( + *args, + control_bounds_high=control_bounds_high, + control_bounds_low=control_bounds_low, + **kwargs, + ) - def compute_filtered_control(self, input_state: Any, step_size: float, control_desired: np.ndarray) -> np.ndarray: + def compute_filtered_control( + self, input_state: Any, step_size: float, control_desired: np.ndarray + ) -> np.ndarray: u_act = np.zeros(self.num_deputies * 3) for i in range(self.num_deputies): - u_des = control_desired[3 * i:3 * i + 3] + u_des = control_desired[3 * i : 3 * i + 3] x_new = self.get_agent_state(input_state, i) - u_act[3 * i:3 * i + 3] = self.deputy_rta.filter_control(x_new, step_size, u_des) + u_act[3 * i : 3 * i + 3] = self.deputy_rta.filter_control( + x_new, step_size, u_des + ) return u_act def get_agent_state(self, state, i): """Places current agent state at beginning of array""" - x_i = state[6 * i:6 * i + 6] - x_old = np.delete(state, np.s_[6 * i:6 * i + 6], 0) + x_i = state[6 * i : 6 * i + 6] + x_old = np.delete(state, np.s_[6 * i : 6 * i + 6], 0) x = np.concatenate((x_i, x_old)) return x diff --git a/run_time_assurance/zoo/cwh/random_sample_testing.py b/run_time_assurance/zoo/cwh/random_sample_testing.py index 6f3292f..a1137f3 100644 --- a/run_time_assurance/zoo/cwh/random_sample_testing.py +++ b/run_time_assurance/zoo/cwh/random_sample_testing.py @@ -1,11 +1,16 @@ """Module for example random sample tests for 2d docking""" + import numpy as np -from safe_autonomy_dynamics.base_models import BaseLinearODESolverDynamics -from safe_autonomy_dynamics.cwh import M_DEFAULT, N_DEFAULT, generate_cwh_matrices +import safe_autonomy_simulation.dynamics as dynamics +import safe_autonomy_simulation.sims.spacecraft.defaults as defaults from run_time_assurance.rta.base import ConstraintBasedRTA -from run_time_assurance.utils.sample_testing import LatinHypercubeRandomSampleTestingModule, ParametricRandomSampleTestingModule +from run_time_assurance.utils.sample_testing import ( + LatinHypercubeRandomSampleTestingModule, + ParametricRandomSampleTestingModule, +) from run_time_assurance.zoo.cwh.docking_2d import Docking2dExplicitOptimizationRTA +from run_time_assurance.zoo.cwh.utils import generate_cwh_matrices class Docking2DLatinHypercubeRandomSampleTest(LatinHypercubeRandomSampleTestingModule): @@ -15,8 +20,8 @@ def __init__( self, rta: ConstraintBasedRTA = None, n_points: int = 100, - simulation_time: float = 100., - step_size: float = 1., + simulation_time: float = 100.0, + step_size: float = 1.0, control_dim: int = 2, state_dim: int = 4, bounds: np.ndarray = None, @@ -27,8 +32,8 @@ def __init__( if bounds is None: bounds = np.array([[-10000, -10000, -5, -5], [10000, 10000, 5, 5]]) - A, B = generate_cwh_matrices(M_DEFAULT, N_DEFAULT, mode="2d") - self.dynamics = BaseLinearODESolverDynamics(A=A, B=B, integration_method='RK45') + A, B = generate_cwh_matrices(defaults.M_DEFAULT, defaults.N_DEFAULT, mode="2d") + self.dynamics = dynamics.LinearODEDynamics(A=A, B=B, integration_method="RK45") super().__init__( rta=rta, @@ -41,7 +46,9 @@ def __init__( multiplier=multiplier, ) - def _pred_state(self, state: np.ndarray, step_size: float, control: np.ndarray) -> np.ndarray: + def _pred_state( + self, state: np.ndarray, step_size: float, control: np.ndarray + ) -> np.ndarray: next_state_vec, _ = self.dynamics.step(step_size, state, control) return next_state_vec @@ -53,8 +60,8 @@ def __init__( self, rta: ConstraintBasedRTA = None, n_points: int = 100, - simulation_time: float = 100., - step_size: float = 1., + simulation_time: float = 100.0, + step_size: float = 1.0, control_dim: int = 2, state_dim: int = 4, state_pdfs: list = None, @@ -67,8 +74,8 @@ def __init__( if distribution_params is None: distribution_params = np.array([[0, 0, 0, 0], [5000, 5000, 2, 2]]) - A, B = generate_cwh_matrices(M_DEFAULT, N_DEFAULT, mode="2d") - self.dynamics = BaseLinearODESolverDynamics(A=A, B=B, integration_method='RK45') + A, B = generate_cwh_matrices(defaults.M_DEFAULT, defaults.N_DEFAULT, mode="2d") + self.dynamics = dynamics.LinearODEDynamics(A=A, B=B, integration_method="RK45") super().__init__( rta=rta, @@ -81,12 +88,14 @@ def __init__( distribution_params=distribution_params, ) - def _pred_state(self, state: np.ndarray, step_size: float, control: np.ndarray) -> np.ndarray: + def _pred_state( + self, state: np.ndarray, step_size: float, control: np.ndarray + ) -> np.ndarray: next_state_vec, _ = self.dynamics.step(step_size, state, control) return next_state_vec -if __name__ == '__main__': +if __name__ == "__main__": mode = "LatinHypercube" if mode == "LatinHypercube": mc = Docking2DLatinHypercubeRandomSampleTest() diff --git a/run_time_assurance/zoo/cwh/utils.py b/run_time_assurance/zoo/cwh/utils.py new file mode 100644 index 0000000..9675cd3 --- /dev/null +++ b/run_time_assurance/zoo/cwh/utils.py @@ -0,0 +1,74 @@ +"""Utility functions for the CWH Zoo example.""" + +import typing +import numpy as np + + +def generate_cwh_matrices( + m: float, n: float, mode: str = "2d" +) -> typing.Tuple[np.ndarray, np.ndarray]: + """Generates A and B Matrices from Clohessy-Wiltshire linearized dynamics of dx/dt = Ax + Bu + + Parameters + ---------- + m : float + mass in kg of spacecraft + n : float + orbital mean motion in rad/s of current Hill's reference frame + mode : str, optional + dimensionality of dynamics matrices. '2d' or '3d', by default '2d' + + Returns + ------- + np.ndarray + A dynamics matrix + np.ndarray + B dynamics matrix + """ + assert mode in ["2d", "3d"], "mode must be on of ['2d', '3d']" + if mode == "2d": + A = np.array( + [ + [0, 0, 1, 0], + [0, 0, 0, 1], + [3 * n**2, 0, 0, 2 * n], + [0, 0, -2 * n, 0], + ], + dtype=np.float64, + ) + + B = np.array( + [ + [0, 0], + [0, 0], + [1 / m, 0], + [0, 1 / m], + ], + dtype=np.float64, + ) + else: + A = np.array( + [ + [0, 0, 0, 1, 0, 0], + [0, 0, 0, 0, 1, 0], + [0, 0, 0, 0, 0, 1], + [3 * n**2, 0, 0, 0, 2 * n, 0], + [0, 0, 0, -2 * n, 0, 0], + [0, 0, -(n**2), 0, 0, 0], + ], + dtype=np.float64, + ) + + B = np.array( + [ + [0, 0, 0], + [0, 0, 0], + [0, 0, 0], + [1 / m, 0, 0], + [0, 1 / m, 0], + [0, 0, 1 / m], + ], + dtype=np.float64, + ) + + return A, B diff --git a/run_time_assurance/zoo/integrators/integrator_1d.py b/run_time_assurance/zoo/integrators/integrator_1d.py index b432ba0..e74784d 100644 --- a/run_time_assurance/zoo/integrators/integrator_1d.py +++ b/run_time_assurance/zoo/integrators/integrator_1d.py @@ -1,16 +1,27 @@ -"""This module implements RTA methods for the 1D integrator problem applied to spacecraft docking -""" +"""This module implements RTA methods for the 1D integrator problem applied to spacecraft docking""" + from collections import OrderedDict from typing import Dict, Tuple, Union import jax.numpy as jnp import numpy as np -from safe_autonomy_dynamics.base_models import BaseLinearODESolverDynamics -from safe_autonomy_dynamics.integrators import M_DEFAULT, generate_dynamics_matrices - -from run_time_assurance.constraint import ConstraintModule, ConstraintStrengthener, PolynomialConstraintStrengthener +from safe_autonomy_simulation.entities.integrator import ( + M_DEFAULT, + PointMassIntegratorDynamics, +) + +from run_time_assurance.constraint import ( + ConstraintModule, + ConstraintStrengthener, + PolynomialConstraintStrengthener, +) from run_time_assurance.controller import RTABackupController -from run_time_assurance.rta import ExplicitASIFModule, ExplicitSimplexModule, ImplicitASIFModule, ImplicitSimplexModule +from run_time_assurance.rta import ( + ExplicitASIFModule, + ExplicitSimplexModule, + ImplicitASIFModule, + ImplicitSimplexModule, +) from run_time_assurance.state import RTAStateWrapper @@ -43,35 +54,47 @@ class Integrator1dDockingRTAMixin: Must call mixin methods using the RTA interface methods """ - def _setup_docking_properties(self, m: float, jit_compile_dict: Dict[str, bool], integration_method: str): + def _setup_docking_properties( + self, m: float, jit_compile_dict: Dict[str, bool], integration_method: str + ): """Initializes docking specific properties from other class members""" - A, B = generate_dynamics_matrices(m=m, mode='1d') - self.A = jnp.array(A) - self.B = jnp.array(B) - self.dynamics = BaseLinearODESolverDynamics(A=A, B=B, integration_method=integration_method, use_jax=True) - - if integration_method == 'RK45': - jit_compile_dict.setdefault('pred_state', False) - jit_compile_dict.setdefault('integrate', False) - if jit_compile_dict.get('pred_state'): - raise ValueError('pred_state uses RK45 integration and can not be compiled using jit') - if jit_compile_dict.get('integrate'): - raise ValueError('integrate uses RK45 integration and can not be compiled using jit') - elif integration_method in ('Euler', 'RK45_JAX'): - jit_compile_dict.setdefault('pred_state', True) - jit_compile_dict.setdefault('integrate', True) - else: - raise ValueError('integration_method must be either RK45_JAX, RK45, or Euler') + self.dynamics = PointMassIntegratorDynamics( + m=m, + mode="1d", + integration_method=integration_method, + ) + self.A = jnp.array(self.dynamics.A) + self.B = jnp.array(self.dynamics.B) + + assert ( + integration_method in ("RK45", "Euler") + ), f"Invalid integration method {integration_method}, must be either 'RK45' or 'Euler'" + + jit_compile_dict.setdefault("pred_state", True) + jit_compile_dict.setdefault("integrate", True) def _setup_docking_constraints_explicit(self) -> OrderedDict: """generates explicit constraints used in the docking problem""" - return OrderedDict([('rel_vel', ConstraintIntegrator1dDockingCollisionExplicit())]) + return OrderedDict( + [("rel_vel", ConstraintIntegrator1dDockingCollisionExplicit())] + ) def _setup_docking_constraints_implicit(self) -> OrderedDict: """generates implicit constraints used in the docking problem""" - return OrderedDict([('rel_vel', ConstraintIntegrator1dDockingCollisionImplicit(subsample_constraints_num_least=1))]) + return OrderedDict( + [ + ( + "rel_vel", + ConstraintIntegrator1dDockingCollisionImplicit( + subsample_constraints_num_least=1 + ), + ) + ] + ) - def _docking_pred_state(self, state: jnp.ndarray, step_size: float, control: jnp.ndarray) -> jnp.ndarray: + def _docking_pred_state( + self, state: jnp.ndarray, step_size: float, control: jnp.ndarray + ) -> jnp.ndarray: """Predicts the next state given the current state and control action""" out, _ = self.dynamics.step(step_size, state, control) return out @@ -85,7 +108,9 @@ def _docking_g_x(self, _: jnp.ndarray) -> jnp.ndarray: return jnp.copy(self.B) -class Integrator1dDockingExplicitSwitchingRTA(ExplicitSimplexModule, Integrator1dDockingRTAMixin): +class Integrator1dDockingExplicitSwitchingRTA( + ExplicitSimplexModule, Integrator1dDockingRTAMixin +): """Implements Explicit Switching RTA for the Integrator 1d Docking problem Parameters @@ -98,7 +123,7 @@ class Integrator1dDockingExplicitSwitchingRTA(ExplicitSimplexModule, Integrator1 backup controller object utilized by rta module to generate backup control. By default Integrator1dDockingBackupController integration_method: str, optional - Integration method to use, either 'RK45_JAX', 'RK45', or 'Euler' + Integration method to use, either 'RK45' or 'Euler' """ def __init__( @@ -109,8 +134,8 @@ def __init__( control_bounds_low: Union[float, np.ndarray] = -1, backup_controller: RTABackupController = None, jit_compile_dict: Dict[str, bool] = None, - integration_method: str = 'Euler', - **kwargs + integration_method: str = "Euler", + **kwargs, ): self.m = m self.integration_method = integration_method @@ -119,7 +144,7 @@ def __init__( backup_controller = Integrator1dDockingBackupController() if jit_compile_dict is None: - jit_compile_dict = {'constraint_violation': True} + jit_compile_dict = {"constraint_violation": True} super().__init__( *args, @@ -127,20 +152,26 @@ def __init__( control_bounds_low=control_bounds_low, backup_controller=backup_controller, jit_compile_dict=jit_compile_dict, - **kwargs + **kwargs, ) def _setup_properties(self): - self._setup_docking_properties(self.m, self.jit_compile_dict, self.integration_method) + self._setup_docking_properties( + self.m, self.jit_compile_dict, self.integration_method + ) def _setup_constraints(self) -> OrderedDict: return self._setup_docking_constraints_explicit() - def _pred_state(self, state: jnp.ndarray, step_size: float, control: jnp.ndarray) -> jnp.ndarray: + def _pred_state( + self, state: jnp.ndarray, step_size: float, control: jnp.ndarray + ) -> jnp.ndarray: return self._docking_pred_state(state, step_size, control) -class Integrator1dDockingImplicitSwitchingRTA(ImplicitSimplexModule, Integrator1dDockingRTAMixin): +class Integrator1dDockingImplicitSwitchingRTA( + ImplicitSimplexModule, Integrator1dDockingRTAMixin +): """Implements Implicit Switching RTA for the Integrator 1d Docking problem Parameters @@ -157,7 +188,7 @@ class Integrator1dDockingImplicitSwitchingRTA(ImplicitSimplexModule, Integrator1 backup controller object utilized by rta module to generate backup control. By default Integrator1dDockingBackupController integration_method: str, optional - Integration method to use, either 'RK45_JAX', 'RK45', or 'Euler' + Integration method to use, either 'RK45' or 'Euler' """ def __init__( @@ -169,8 +200,8 @@ def __init__( control_bounds_low: Union[float, np.ndarray] = -1, backup_controller: RTABackupController = None, jit_compile_dict: Dict[str, bool] = None, - integration_method: str = 'Euler', - **kwargs + integration_method: str = "Euler", + **kwargs, ): self.m = m self.integration_method = integration_method @@ -179,7 +210,7 @@ def __init__( backup_controller = Integrator1dDockingBackupController() if jit_compile_dict is None: - jit_compile_dict = {'constraint_violation': True} + jit_compile_dict = {"constraint_violation": True} super().__init__( *args, @@ -188,20 +219,26 @@ def __init__( control_bounds_high=control_bounds_high, control_bounds_low=control_bounds_low, jit_compile_dict=jit_compile_dict, - **kwargs + **kwargs, ) def _setup_properties(self): - self._setup_docking_properties(self.m, self.jit_compile_dict, self.integration_method) + self._setup_docking_properties( + self.m, self.jit_compile_dict, self.integration_method + ) def _setup_constraints(self) -> OrderedDict: return self._setup_docking_constraints_implicit() - def _pred_state(self, state: jnp.ndarray, step_size: float, control: jnp.ndarray) -> jnp.ndarray: + def _pred_state( + self, state: jnp.ndarray, step_size: float, control: jnp.ndarray + ) -> jnp.ndarray: return self._docking_pred_state(state, step_size, control) -class Integrator1dDockingExplicitOptimizationRTA(ExplicitASIFModule, Integrator1dDockingRTAMixin): +class Integrator1dDockingExplicitOptimizationRTA( + ExplicitASIFModule, Integrator1dDockingRTAMixin +): """ Implements Explicit Optimization RTA for the Integrator 1d Docking problem @@ -224,11 +261,11 @@ def __init__( control_bounds_high: Union[float, np.ndarray] = 1, control_bounds_low: Union[float, np.ndarray] = -1, jit_compile_dict: Dict[str, bool] = None, - **kwargs + **kwargs, ): self.m = m if jit_compile_dict is None: - jit_compile_dict = {'generate_barrier_constraint_mats': True} + jit_compile_dict = {"generate_barrier_constraint_mats": True} super().__init__( *args, @@ -236,11 +273,11 @@ def __init__( control_bounds_low=control_bounds_low, control_dim=1, jit_compile_dict=jit_compile_dict, - **kwargs + **kwargs, ) def _setup_properties(self): - self._setup_docking_properties(self.m, self.jit_compile_dict, 'RK45_JAX') + self._setup_docking_properties(self.m, self.jit_compile_dict, "RK45") def _setup_constraints(self) -> OrderedDict: return self._setup_docking_constraints_explicit() @@ -252,7 +289,9 @@ def state_transition_input(self, state: jnp.ndarray) -> jnp.ndarray: return self._docking_g_x(state) -class Integrator1dDockingImplicitOptimizationRTA(ImplicitASIFModule, Integrator1dDockingRTAMixin): +class Integrator1dDockingImplicitOptimizationRTA( + ImplicitASIFModule, Integrator1dDockingRTAMixin +): """ Implements Implicit Optimization RTA for the Integrator 1d Docking problem @@ -272,7 +311,7 @@ class Integrator1dDockingImplicitOptimizationRTA(ImplicitASIFModule, Integrator1 backup controller object utilized by rta module to generate backup control. By default Integrator1dDockingBackupController integration_method: str, optional - Integration method to use, either 'RK45_JAX', 'RK45', or 'Euler' + Integration method to use, either 'RK45' or 'Euler' """ def __init__( @@ -284,8 +323,8 @@ def __init__( control_bounds_low: Union[float, np.ndarray] = -1, backup_controller: RTABackupController = None, jit_compile_dict: Dict[str, bool] = None, - integration_method: str = 'Euler', - **kwargs + integration_method: str = "Euler", + **kwargs, ): self.m = m self.integration_method = integration_method @@ -293,7 +332,7 @@ def __init__( backup_controller = Integrator1dDockingBackupController() if jit_compile_dict is None: - jit_compile_dict = {'generate_ineq_constraint_mats': True} + jit_compile_dict = {"generate_ineq_constraint_mats": True} super().__init__( *args, @@ -304,11 +343,13 @@ def __init__( control_bounds_low=control_bounds_low, control_dim=1, jit_compile_dict=jit_compile_dict, - **kwargs + **kwargs, ) def _setup_properties(self): - self._setup_docking_properties(self.m, self.jit_compile_dict, self.integration_method) + self._setup_docking_properties( + self.m, self.jit_compile_dict, self.integration_method + ) def _setup_constraints(self) -> OrderedDict: return self._setup_docking_constraints_implicit() @@ -321,16 +362,14 @@ def state_transition_input(self, state: jnp.ndarray) -> jnp.ndarray: class Integrator1dDockingBackupController(RTABackupController): - """Max braking backup controller to bring velocity to zero for 1d Integrator - """ + """Max braking backup controller to bring velocity to zero for 1d Integrator""" def _generate_control( self, state: jnp.ndarray, step_size: float, - controller_state: Union[jnp.ndarray, Dict[str, jnp.ndarray], None] = None + controller_state: Union[jnp.ndarray, Dict[str, jnp.ndarray], None] = None, ) -> Tuple[jnp.ndarray, None]: - return jnp.array([-1]), None @@ -349,8 +388,8 @@ def __init__(self, alpha: ConstraintStrengthener = None, **kwargs): alpha = PolynomialConstraintStrengthener([0, 1, 0, 30]) super().__init__(alpha=alpha, **kwargs) - def _compute(self, state: jnp.ndarray) -> float: - return -2 * state[0] - state[1]**2 + def _compute(self, state: jnp.ndarray, params: dict) -> float: + return -2 * state[0] - state[1] ** 2 class ConstraintIntegrator1dDockingCollisionImplicit(ConstraintModule): @@ -368,5 +407,5 @@ def __init__(self, alpha: ConstraintStrengthener = None, **kwargs): alpha = PolynomialConstraintStrengthener([0, 10, 0, 30]) super().__init__(alpha=alpha, **kwargs) - def _compute(self, state: jnp.ndarray) -> float: + def _compute(self, state: jnp.ndarray, params: dict) -> float: return -state[0] diff --git a/test/cwh_inspection/acc_2023_sim.py b/test/cwh_inspection/acc_2023_sim.py index 1b1b21a..f8407d0 100644 --- a/test/cwh_inspection/acc_2023_sim.py +++ b/test/cwh_inspection/acc_2023_sim.py @@ -6,7 +6,7 @@ from jax import jit from jax.experimental.ode import odeint import jax.numpy as jnp -from safe_autonomy_dynamics.cwh import generate_cwh_matrices +from run_time_assurance.zoo.cwh.utils import generate_cwh_matrices from run_time_assurance.zoo.cwh.inspection_3d import CombinedInspectionRTA, InspectionRTA from run_time_assurance.utils.sample_testing import DataTrackingSampleTestingModule import os diff --git a/test/cwh_inspection/test_inspection_1v1.py b/test/cwh_inspection/test_inspection_1v1.py index 88f7656..0d9ffb1 100644 --- a/test/cwh_inspection/test_inspection_1v1.py +++ b/test/cwh_inspection/test_inspection_1v1.py @@ -5,8 +5,9 @@ from collections import OrderedDict import os -from safe_autonomy_dynamics.cwh import M_DEFAULT, N_DEFAULT, generate_cwh_matrices -from safe_autonomy_dynamics.base_models import BaseLinearODESolverDynamics +from safe_autonomy_simulation.sims.spacecraft.defaults import M_DEFAULT, N_DEFAULT +from safe_autonomy_simulation.dynamics import LinearODEDynamics +from run_time_assurance.zoo.cwh.utils import generate_cwh_matrices from run_time_assurance.zoo.cwh.inspection_1v1 import U_MAX_DEFAULT, Inspection1v1RTA, InspectionCascadedRTA, DiscreteInspection1v1RTA from run_time_assurance.utils.sample_testing import DataTrackingSampleTestingModule from run_time_assurance.utils import to_jnp_array_jit @@ -29,7 +30,7 @@ def __init__(self, rta, constraint_keys=[], step_size=1, **kwargs): self.rta.constraints = new_constraints A, B = generate_cwh_matrices(M_DEFAULT, N_DEFAULT, mode="3d") - self.dynamics = BaseLinearODESolverDynamics(A=A, B=B, integration_method='RK45') + self.dynamics = LinearODEDynamics(A=A, B=B, integration_method='RK45') # Specify LQR gains Q = np.eye(6) * 0.05 # State cost diff --git a/test/cwh_inspection/test_inspection_3d.py b/test/cwh_inspection/test_inspection_3d.py index 6bd70f7..51ba3de 100644 --- a/test/cwh_inspection/test_inspection_3d.py +++ b/test/cwh_inspection/test_inspection_3d.py @@ -6,7 +6,8 @@ from jax import jit from jax.experimental.ode import odeint import jax.numpy as jnp -from safe_autonomy_dynamics.cwh import M_DEFAULT, N_DEFAULT, generate_cwh_matrices +from safe_autonomy_simulation.sims.spacecraft.defaults import M_DEFAULT, N_DEFAULT +from run_time_assurance.zoo.cwh.utils import generate_cwh_matrices from run_time_assurance.zoo.cwh.inspection_3d import NUM_DEPUTIES_DEFAULT, U_MAX_DEFAULT, SUN_VEL_DEFAULT, CombinedInspectionRTA, InspectionRTA from run_time_assurance.utils.sample_testing import DataTrackingSampleTestingModule from run_time_assurance.utils import to_jnp_array_jit diff --git a/test/docking_rta/test_docking_rta_2d.py b/test/docking_rta/test_docking_rta_2d.py index 9130150..d2929e7 100644 --- a/test/docking_rta/test_docking_rta_2d.py +++ b/test/docking_rta/test_docking_rta_2d.py @@ -4,8 +4,9 @@ import time import os -from safe_autonomy_dynamics.cwh import M_DEFAULT, N_DEFAULT, generate_cwh_matrices -from safe_autonomy_dynamics.base_models import BaseLinearODESolverDynamics +from safe_autonomy_simulation.sims.spacecraft.defaults import M_DEFAULT, N_DEFAULT +from safe_autonomy_simulation.dynamics import LinearODEDynamics +from run_time_assurance.zoo.cwh.utils import generate_cwh_matrices from run_time_assurance.zoo.cwh.docking_2d import Docking2dExplicitSwitchingRTA, Docking2dImplicitSwitchingRTA, \ Docking2dExplicitOptimizationRTA, Docking2dImplicitOptimizationRTA from run_time_assurance.utils.sample_testing import DataTrackingSampleTestingModule @@ -19,7 +20,7 @@ def __init__(self, rta, random_init=False): self.docking_region = 1 # m A, B = generate_cwh_matrices(M_DEFAULT, N_DEFAULT, mode="2d") - self.dynamics = BaseLinearODESolverDynamics(A=A, B=B, integration_method='RK45') + self.dynamics = LinearODEDynamics(A=A, B=B, integration_method='RK45') # Specify LQR gains Q = np.eye(4) * 0.05 # State cost diff --git a/test/docking_rta/test_docking_rta_3d.py b/test/docking_rta/test_docking_rta_3d.py index c02f90d..f5f8044 100644 --- a/test/docking_rta/test_docking_rta_3d.py +++ b/test/docking_rta/test_docking_rta_3d.py @@ -4,8 +4,9 @@ import matplotlib.pyplot as plt import time -from safe_autonomy_dynamics.cwh import M_DEFAULT, N_DEFAULT, generate_cwh_matrices -from safe_autonomy_dynamics.base_models import BaseLinearODESolverDynamics +from safe_autonomy_simulation.sims.spacecraft.defaults import M_DEFAULT, N_DEFAULT +from safe_autonomy_simulation.dynamics import LinearODEDynamics +from run_time_assurance.zoo.cwh.utils import generate_cwh_matrices from run_time_assurance.zoo.cwh.docking_3d import Docking3dExplicitSwitchingRTA, Docking3dImplicitSwitchingRTA, \ Docking3dExplicitOptimizationRTA, Docking3dImplicitOptimizationRTA from run_time_assurance.utils.sample_testing import DataTrackingSampleTestingModule @@ -19,7 +20,7 @@ def __init__(self, rta, random_init=False): self.docking_region = 1 # m A, B = generate_cwh_matrices(M_DEFAULT, N_DEFAULT, mode="3d") - self.dynamics = BaseLinearODESolverDynamics(A=A, B=B, integration_method='RK45') + self.dynamics = LinearODEDynamics(A=A, B=B, integration_method='RK45') # Specify LQR gains Q = np.eye(6) * 0.05 # State cost diff --git a/test/integrator_rta/test_integrator_rta_1d.py b/test/integrator_rta/test_integrator_rta_1d.py index 1e52040..3841918 100644 --- a/test/integrator_rta/test_integrator_rta_1d.py +++ b/test/integrator_rta/test_integrator_rta_1d.py @@ -3,10 +3,16 @@ import time import os -from safe_autonomy_dynamics.integrators import M_DEFAULT, generate_dynamics_matrices -from safe_autonomy_dynamics.base_models import BaseLinearODESolverDynamics -from run_time_assurance.zoo.integrators.integrator_1d import Integrator1dDockingExplicitSwitchingRTA, Integrator1dDockingImplicitSwitchingRTA, \ - Integrator1dDockingExplicitOptimizationRTA, Integrator1dDockingImplicitOptimizationRTA +from safe_autonomy_simulation.entities.integrator import ( + M_DEFAULT, + PointMassIntegratorDynamics, +) +from run_time_assurance.zoo.integrators.integrator_1d import ( + Integrator1dDockingExplicitSwitchingRTA, + Integrator1dDockingImplicitSwitchingRTA, + Integrator1dDockingExplicitOptimizationRTA, + Integrator1dDockingImplicitOptimizationRTA, +) from run_time_assurance.utils.sample_testing import DataTrackingSampleTestingModule @@ -15,25 +21,29 @@ def __init__(self, rta): self.u_max = 1 # Actuation constraint self.docking_region = 0.1 self.docking_max_vel = 0.1 + self.dynamics = PointMassIntegratorDynamics( + m=M_DEFAULT, mode="1d", integration_method="RK45" + ) - A, B = generate_dynamics_matrices(m=M_DEFAULT, mode='1d') - self.dynamics = BaseLinearODESolverDynamics(A=A, B=B, integration_method='RK45') - - super().__init__(rta=rta, simulation_time=4, step_size=0.02, control_dim=1, state_dim=2) + super().__init__( + rta=rta, simulation_time=4, step_size=0.02, control_dim=1, state_dim=2 + ) def _desired_control(self, state): return np.array([self.u_max]) def _get_initial_state(self): - return np.array([-1.75, 0.]) + return np.array([-1.75, 0.0]) def _pred_state(self, state, step_size, control): # return next_state out, _ = self.dynamics.step(step_size, state, control) return out - + def _check_done_conditions(self, state, time): - docking_done = abs(state[0]) < self.docking_region and state[1] < self.docking_max_vel + docking_done = ( + abs(state[0]) < self.docking_region and state[1] < self.docking_max_vel + ) time_done = super()._check_done_conditions(state, time) return bool(docking_done or time_done) @@ -41,50 +51,75 @@ def plotter(self, array, control, intervening): fig = plt.figure(figsize=(20, 10)) lim_x1 = np.linspace(-2, 0, 10000) - lim_x2 = np.sqrt(-2*lim_x1) + lim_x2 = np.sqrt(-2 * lim_x1) ax1 = fig.add_subplot(121) - ax1.plot(lim_x1, lim_x2, 'k--', linewidth=2, label='Constraint') - ax1.plot([0, 0], [-1, 0], 'k--', linewidth=2) - ax1.plot(0, 0, 'k*', markersize=15, label='Desired State') - ax1.fill_between(lim_x1, 0, lim_x2, color=(244/255, 249/255, 241/255)) # green - ax1.fill_between(lim_x1, -1, 0, color=(244/255, 249/255, 241/255)) # green - ax1.fill_between(lim_x1, lim_x2, 10, color=(255/255, 239/255, 239/255)) # red - ax1.fill_between([0, 1], -1, 10, color=(255/255, 239/255, 239/255)) # red - ax1.plot(array[0, 0], array[0, 1], 'b*', markersize=15, label='Initial State') - ax1.plot(array[:, 0], array[:, 1], 'b', linewidth=2, label='Trajectory') + ax1.plot(lim_x1, lim_x2, "k--", linewidth=2, label="Constraint") + ax1.plot([0, 0], [-1, 0], "k--", linewidth=2) + ax1.plot(0, 0, "k*", markersize=15, label="Desired State") + ax1.fill_between( + lim_x1, 0, lim_x2, color=(244 / 255, 249 / 255, 241 / 255) + ) # green + ax1.fill_between( + lim_x1, -1, 0, color=(244 / 255, 249 / 255, 241 / 255) + ) # green + ax1.fill_between( + lim_x1, lim_x2, 10, color=(255 / 255, 239 / 255, 239 / 255) + ) # red + ax1.fill_between([0, 1], -1, 10, color=(255 / 255, 239 / 255, 239 / 255)) # red + ax1.plot(array[0, 0], array[0, 1], "b*", markersize=15, label="Initial State") + ax1.plot(array[:, 0], array[:, 1], "b", linewidth=2, label="Trajectory") ax1.set_xlim([-2, 0.1]) ax1.set_ylim([-0.2, 2.2]) - ax1.set_xlabel(r'$x_1$ (position) [m]') - ax1.set_ylabel(r'$x_2$ (velocity) [m/s]') - ax1.set_title('Trajectory') + ax1.set_xlabel(r"$x_1$ (position) [m]") + ax1.set_ylabel(r"$x_2$ (velocity) [m/s]") + ax1.set_title("Trajectory") ax1.grid(True) ax1.legend() ax2 = fig.add_subplot(122) xlim = len(control[:, 0]) * self.step_size * 1.1 - ax2.plot([0, xlim], [1, 1], 'k--', linewidth=2) - ax2.plot([0, xlim], [-1, -1], 'k--', linewidth=2) - ax2.fill_between([0, xlim], -1, 1, color=(244/255, 249/255, 241/255)) # green - ax2.fill_between([0, xlim], -2, -1, color=(255/255, 239/255, 239/255)) # red - ax2.fill_between([0, xlim], 1, 2, color=(255/255, 239/255, 239/255)) # red - ax2.plot(np.array(range(len(control[:, 0])))*self.step_size, control[:, 0], 'b', linewidth=2) + ax2.plot([0, xlim], [1, 1], "k--", linewidth=2) + ax2.plot([0, xlim], [-1, -1], "k--", linewidth=2) + ax2.fill_between( + [0, xlim], -1, 1, color=(244 / 255, 249 / 255, 241 / 255) + ) # green + ax2.fill_between( + [0, xlim], -2, -1, color=(255 / 255, 239 / 255, 239 / 255) + ) # red + ax2.fill_between( + [0, xlim], 1, 2, color=(255 / 255, 239 / 255, 239 / 255) + ) # red + ax2.plot( + np.array(range(len(control[:, 0]))) * self.step_size, + control[:, 0], + "b", + linewidth=2, + ) ax2.grid(True) ax2.set_xlim([0, xlim]) ax2.set_ylim([-1.1, 1.1]) - ax2.set_xlabel('Time [s]') - ax2.set_ylabel('Control [N]') - ax2.set_title('Control') + ax2.set_xlabel("Time [s]") + ax2.set_ylabel("Control [N]") + ax2.set_title("Control") -if __name__ == '__main__': +if __name__ == "__main__": plot_fig = True save_fig = True - output_dir = 'figs/1d' - - rtas = [Integrator1dDockingExplicitSwitchingRTA(), Integrator1dDockingImplicitSwitchingRTA(), - Integrator1dDockingExplicitOptimizationRTA(), Integrator1dDockingImplicitOptimizationRTA()] - output_names = ['rta_test_integrator_1d_explicit_switching', 'rta_test_integrator_1d_implicit_switching', - 'rta_test_integrator_1d_explicit_optimization', 'rta_test_integrator_1d_implicit_optimization'] + output_dir = "figs/1d" + + rtas = [ + Integrator1dDockingExplicitSwitchingRTA(), + Integrator1dDockingImplicitSwitchingRTA(), + Integrator1dDockingExplicitOptimizationRTA(), + Integrator1dDockingImplicitOptimizationRTA(), + ] + output_names = [ + "rta_test_integrator_1d_explicit_switching", + "rta_test_integrator_1d_implicit_switching", + "rta_test_integrator_1d_explicit_optimization", + "rta_test_integrator_1d_implicit_optimization", + ] os.makedirs(output_dir, exist_ok=True) diff --git a/tutorials/Double_Integrator_Tutorial.ipynb b/tutorials/Double_Integrator_Tutorial.ipynb index d5f036f..1c77b41 100644 --- a/tutorials/Double_Integrator_Tutorial.ipynb +++ b/tutorials/Double_Integrator_Tutorial.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "id": "yl_CW_ZpRlDv" }, @@ -135,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "id": "2Lofr82QSkGO" }, @@ -180,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "id": "vlbxTu_9Shzy" }, @@ -194,7 +194,7 @@ " alpha = PolynomialConstraintStrengthener([0, 10, 0, 30])\n", " super().__init__(alpha=alpha)\n", "\n", - " def _compute(self, state):\n", + " def _compute(self, state, params):\n", " # -x1 >= 0\n", " x1 = state[0]\n", " return -x1" @@ -227,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "id": "_ls5Qxa7Sec-" }, @@ -241,7 +241,7 @@ " alpha = PolynomialConstraintStrengthener([0, 1, 0, 30])\n", " super().__init__(alpha=alpha)\n", "\n", - " def _compute(self, state):\n", + " def _compute(self, state, params):\n", " x1 = state[0]\n", " x2 = state[1]\n", " return -2 * x1 - x2**2" @@ -259,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "id": "kVGsaWhvSi4s" }, @@ -285,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "id": "m4C8EkwgsuiC" }, @@ -308,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "id": "YgTt2CdNSsO9" }, @@ -424,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -443,7 +443,7 @@ }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -474,7 +474,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "id": "ZcPSFyn0SmOx" }, @@ -510,7 +510,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -520,13 +520,6 @@ "outputId": "42ac1d3e-8575-4582-b33c-2ee2b57bce75" }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -536,7 +529,7 @@ }, { "data": { - "image/png": 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" ] @@ -566,7 +559,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "id": "1hOsnzxGZBhB" }, @@ -603,7 +596,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -622,7 +615,7 @@ }, { "data": { - "image/png": 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" ] @@ -652,7 +645,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": { "id": "Q67iWXxFa-wd" }, @@ -691,7 +684,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -710,7 +703,7 @@ }, { "data": { - "image/png": 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" ] @@ -740,7 +733,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "metadata": { "id": "_WwC5wT-dDU-" }, @@ -752,14 +745,12 @@ " def __init__(self):\n", " u_max = 1\n", " backup_window = 2\n", - " num_check_all = 0\n", - " skip_length = 1\n", "\n", " self.dynamics = IntegratorDynamics()\n", " backup_controller = BrakingBackupController()\n", " jit_compile_dict = {'pred_state': True, 'integrate': True}\n", "\n", - " super().__init__(backup_window=backup_window, num_check_all=num_check_all, skip_length=skip_length, backup_controller=backup_controller, control_bounds_high=u_max, control_bounds_low=-u_max, control_dim=1, jit_compile_dict=jit_compile_dict)\n", + " super().__init__(backup_window=backup_window, backup_controller=backup_controller, control_bounds_high=u_max, control_bounds_low=-u_max, control_dim=1, jit_compile_dict=jit_compile_dict)\n", "\n", " def _setup_constraints(self):\n", " return OrderedDict([('collision', ConstraintCollisionImplicit())])\n", @@ -785,7 +776,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -804,14 +795,12 @@ }, { "data": { - "image/png": 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