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IR-SIM is an open-source, lightweight robot simulator based on Python, designed for robotics navigation, control, and learning. This simulator provides a simple and user-friendly framework for simulating robots, sensors, and environments, facilitating the development and testing of robotics algorithms with minimal hardware requirements.
Documentation: https://ir-sim.readthedocs.io/en
IR-SIM is an open-source, lightweight robot simulator based on Python, designed for robotics navigation, control, and learning. This simulator provides a simple and user-friendly framework for simulating robots, sensors, and environments, thereby lowering the barrier to developing, training, and testing AI & robotics algorithms with minimal coding and hardware requirements.
Scenarios | Description |
---|---|
In scenarios involving multiple circular differential robots, each robot employs Reciprocal Velocity Obstacle (RVO) behavior to avoid collisions. See Usage - collision avoidance | |
A car-like robot controlled via keyboard navigates a binary map using a 2D LiDAR sensor to detect obstacles. See Usage - grid map | |
A car-like robot controlled via keyboard navigates a grid map generated from 3D habitat spaces datasets like HM3D, MatterPort3D, Gibson, etc. See Usage - grid map hm3d | |
Each robot employing RVO behavior is equipped with a field of view (FOV) to detect other robots within this area. See Usage - fov | |
A car-like robot navigates through the randomly generated and moving obstacles. See Usage - dynamic random obstacles |
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]
git clone https://github.com/hanruihua/ir-sim.git
cd ir-sim
pip install -e .
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 width 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
The advanced usages are listed in the irsim/usage
Currently, the simulator supports the following features. Further features, such as additional sensors, behaviors, and robot models, are under development.
Category | Features |
---|---|
Kinematics | Differential Drive mobile Robot Omni-Directional mobile Robot Ackermann Steering mobile Robot |
Sensors | 2D LiDAR FOV detector |
Geometries | Circle Rectangle Polygon linestring Binary Grid Map |
Behaviors | dash (Move directly toward the goal) rvo (Move toward the goal using Reciprocal Velocity Obstacle behavior) |
Academic Projects:
Deep Reinforcement Learning Projects:
This project is under development. I appreciate and welcome all contributions. Just open an issue or a pull request. Here are some simple ways to start contributing:
FAQs
IR-SIM is an open-source, lightweight robot simulator based on Python, designed for robotics navigation, control, and learning. This simulator provides a simple and user-friendly framework for simulating robots, sensors, and environments, facilitating the development and testing of robotics algorithms with minimal hardware requirements.
We found that ir-sim demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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