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Open-AI Gym extension for robotics based on V-REP

Environments

  1. Mobile robot navigation - the mobile robot contains ultrasonic sensors, IMU and two DC motors. Number of ultrasonic sensors depends on task type. The task of this agent is to navigate from position A to position B. There are implemented two variants presented below.
Environment Description
RoomNavigation Environment with static obstacles like walls.
DynamicRoomNavigation Environment with dynamic obstacles like randomly moving mobile robots.

The environment can be customized with four parameters:

  • navigation_type - Selects the type of the navigation algorithms from the supported ones (ideal, odometry, gyrodometry). Default is ideal.
  • enable_vision - Whether to include visual feedback to the observation space. Default is False.
  • goal_threshold - Defines the threshold value for reaching the goal. Default value is set to 0.05.
  • collision_threshold - Defines the threshold distance for the proximity sensors, when the collision occurs. Default value is set to 0.05.

Action space are desired motor angular velocities in rad/s. They are limited to (0, 15.0) rad/s.

Environment observation space description:

  • distances from n proximity sensors
  • polar coordinates
  • linear and angular velocities
  • (optional) image from camera sensor

Getting started

Here are provided basic requirements for the project:

  • CoppeliaSim 4.1.0+
  • Python 3.6+
  • Ubuntu 20.04
  • OpenAI gym
  • PyRep

Python

To install this package run following commands:

git clone https://github.com/Souphis/gym-vrep.git
cd gym-vrep
python3 setup.py install

Basic usage

To run the environment with default configuration just add follwing code to your project:

import gym
import gym_coppelia_sim

env = gym.make("RoomNavigation-v0") # or env = gym.make("DynamicRoomNavigation-v0")

Below is the example how to run environment in the headless mode:

import gym
import gym_coppelia_sim

env = gym.make("RoomNavigation-v0", headless_mode=True)

Changing the navigation algorithm can be done with following example:

import gym
import gym_coppelia_sim

env = gym.make("RoomNavigation-v0", navigation_algos="odometry")