python-motion-planning 2.0.1

Motion planning algorithms for Python

Introduction

Python Motion Planning repository provides the implementations of common Motion planning algorithms, including path planners on N-D grid, controllers for path-tracking, trajectory optimizers, a visualizer based on matplotlib and a toy physical simulator to test controllers.

Motion planning plans the state sequence of the robot without conflict between the start and goal.

Motion planning mainly includes Path planning and Trajectory planning.

• Path Planning: It's based on path constraints (such as obstacles), planning the optimal path sequence for the robot to travel without conflict between the start and goal.
• Trajectory planning: It plans the motion state to approach the global path based on kinematics, dynamics constraints and path sequence.

The theory analysis can be found at motion-planning.

We also provide ROS C++ version and Matlab version.

Your stars and forks are welcome!

Quick Start

Overview

The source file structure is shown below

python_motion_planning
├─common
|   ├─env
|   |   ├─map
|   |   ├─robot
|   |   └─world
|   ├─utils
|   └─visualizer
├─controller
|   └─path_tracker
├─path_planner
|   ├─graph_search
|   ├─sample_search
|   └─hybrid_search
└─traj_optimizer
    └─curve_generator

Install

(Optional) The code was tested in python=3.10, though other similar versions should also work. We recommend using conda to install the dependencies.

conda create -n pmp python=3.10
conda activate pmp

To install the repository, please run the following command in shell.

pip install python-motion-planning

Run

Please refer to the Tutorials part of online documentation.

Demos

Path Planner

Graph Search

Planner	2D Grid	3D Grid
Dijkstra
GBFS
A*
JPS
Theta*
Lazy Theta*
D*	Implemented in V1.1.1, not migrated	Not implemented
LPA*	Implemented in V1.1.1, not migrated	Not implemented
D* Lite	Implemented in V1.1.1, not migrated	Not implemented
Anya	Not implemented	Not implemented

Sample Search

Planner	2D Grid	3D Grid
RRT
RRT*
RRT-Connect
Informed RRT	Implemented in V1.1.1, not migrated	Not implemented
PRM	Not implemented	Not implemented

Evolutionary Search

Planner	2D Grid	3D Grid
ACO	Implemented in V1.1.1, not migrated	Not implemented
GA	Implemented in V1.1.1, not migrated	Not implemented
PSO	Implemented in V1.1.1, not migrated	Not implemented

Hybrid Search

Planner	2D Grid	3D Grid
Voronoi Planner

Controller

We provide a toy simulator with simple physical simulation to test controllers (path-trakcers). The toy simulator supports multi-agents/multi-robots. The available robots include CircularRobot (Omnidirectional) and DiffDriveRobot (Only support moving forward and backward). Currently only 2D simulator is provided. 3D simulator has not been implemented.

In the following demos, the blue robot 1 is the CircularRobot, and the orange robot 2 is the DiffDriveRobot.

Controller	2D	3D
Path Trakcer		Not implemented
Pure Pursuit		Not implemented
PID		Not implemented
APF		Not implemented
DWA		Not implemented
RPP		Not implemented
LQR	Implemented in V1.1.1, not migrated	Not implemented
MPC	Implemented in V1.1.1, not migrated	Not implemented
MPPI	Not implemented	Not implemented
TEB	Not implemented	Not implemented
Lattice	Not implemented	Not implemented
DQN	Not implemented	Not implemented
DDPG	Implemented in V1.0, not migrated	Not implemented

Trajectory Optimizer

Curve Generator

Point-based

Generator	2D	3D
Cubic Spline		Not implemented
BSpline		Not implemented

Pose-based

Generator	2D	3D
Polynomia		Not implemented
Bezier		Not implemented
Dubins		Not implemented
Reeds-Shepp		Not implemented

Future Works

• N-D controllers (path-trackers).

• Path planning for robotic arms.

• Path planning on topological map.

• Sample search with Dubins or Reeds-Shepp curves.

• Application on ROS2.

• Application in mainstream robot simulation environments (e.g. Gazebo, Carla, Airsim, PyBullet, MuJoCo, Issac Sim).

• More mainstream motion planning algorithms.

• Performance optimization.

Contributors are welcome! For trivial modification, please directly contribute to dev branch. For big modification, please contact us before you contribute.

Contact

Long-term maintainers:

• @omigeft (Wu Maojia)
• @ai-winter (Yang Haodong)

You can contact us via the information provided on our profile.