Intelligent Robot Simulator (IR-SIM)

Documentation: https://ir-sim.readthedocs.io/en

IR-SIM is an open-source, lightweight robot simulator based on Python, specifically designed for intelligent robotics navigation and learning. Primarily intended for research and educational purposes, it is user-friendly and easily customizable.

It provides the following features:

• A versatile and easy-to-use framework for simulating a variety of robot platforms with kinematics and sensors.
• Customizable configurations and parameters using yaml files.
• Real-time visualization of simulation outcomes.
• Ideal for developing and testing algorithms related to robot navigation, motion planning, reinforcement learning.

Robot	Car

Prerequisite

• Python: >= 3.7

Installation

• Install this package from PyPi:

pip install ir-sim

This does not include dependencies for all features of the simulator. To install additional optional dependencies, use the following pip commands:

# install dependencies for keyboard control
pip install ir-sim[keyboard]

# install all optional dependencies
pip install ir-sim[all]  

• Or for development, you may install from source:

git clone https://github.com/hanruihua/ir-sim.git    
cd ir-sim   
pip install -e .  

Usage

Quick Start

import irsim

env = irsim.make('robot_world.yaml') # initialize the environment with the configuration file

for i in range(300): # run the simulation for 300 steps

    env.step()  # update the environment
    env.render() # render the environment

    if env.done(): break # check if the simulation is done
        
env.end() # close the environment

YAML Configuration: robot_world.yaml

world:
  height: 10  # the height of the world
  width: 10   # the height of the world
  step_time: 0.1  # 10Hz calculate each step
  sample_time: 0.1  # 10 Hz for render and data extraction 
  offset: [0, 0] # the offset of the world on x and y 

robot:
  kinematics: {name: 'diff'}  # omni, diff, acker
  shape: {name: 'circle', radius: 0.2}  # radius
  state: [1, 1, 0]  # x, y, theta
  goal: [9, 9, 0]  # x, y, theta
  behavior: {name: 'dash'} # move toward to the goal directly 
  color: 'g' # green

Advanced Usage

The advanced usages are listed in the irsim/usage

Academic Cases

• rl-rvo-nav (RAL & ICRA2023)
• RDA_planner（RAL & IROS2023）

Acknowledgement

• PythonRobotics