Introduction

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.

This repository provides the implementations of common Motion planning algorithms. Your stars and forks are welcome. Maintaining this repository requires a huge amount of work. Therefore, you are also welcome to contribute to this repository by opening issues, submitting pull requests or joining our development team.

The theory analysis can be found at motion-planning.

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

Quick Start

The file structure is shown below

python_motion_planning
├─assets
├─docs
├─examples
└─python_motion_planning
    ├─global_planner
    |   ├─graph_search
    |   ├─sample_search
    |   └─evolutionary_search
    ├─local_planner
    ├─curve_generation
    └─utils
        ├─agent
        ├─environment
        ├─helper
        ├─planner
        └─plot

• The global planning algorithm implementation is in the folder global_planner with graph_search, sample_search and evolutionary search.
• The local planning algorithm implementation is in the folder local_planner.
• The curve generation algorithm implementation is in the folder curve_generation.

The code was tested in python=3.10. To install other dependencies, please run the following command in shell.

pip install -r requirements.txt

To start simulation, open the folder example and select the algorithm, for example

if __name__ == '__main__':
    '''
    path searcher constructor
    '''
    search_factory = SearchFactory()

    '''
    graph search
    '''
    # build environment
    start = (5, 5)
    goal = (45, 25)
    env = Grid(51, 31)

    # creat planner
    planner = search_factory("a_star", start=start, goal=goal, env=env)
    # animation
    planner.run()

Version

Global Planner

Planner	Version	Animation
GBFS
Dijkstra
**A***
JPS
**D***
**LPA***
D* Lite
Theta*
Lazy Theta*
S-Theta*
Anya
Voronoi
RRT
**RRT***
Informed RRT
RRT-Connect
ACO
GA
PSO

Local Planner

Planner	Version	Animation
PID
APF
DWA
RPP
LQR
TEB
MPC
MPPI
Lattice
DDPG

Curve Generation

Planner	Version	Animation
Polynomia
Bezier
Cubic Spline
BSpline
Dubins
Reeds-Shepp
Fem-Pos Smoother

Papers

Global Planning

• A*: A Formal Basis for the heuristic Determination of Minimum Cost Paths
• JPS: Online Graph Pruning for Pathfinding On Grid Maps
• Lifelong Planning A*: Lifelong Planning A*
• D*: Optimal and Efficient Path Planning for Partially-Known Environments
• D* Lite: D* Lite
• Theta*: Theta*: Any-Angle Path Planning on Grids
• Lazy Theta*: Lazy Theta*: Any-Angle Path Planning and Path Length Analysis in 3D
• S-Theta*: S-Theta*: low steering path-planning algorithm
• Anya: Optimal Any-Angle Pathfinding In Practice
• RRT: Rapidly-Exploring Random Trees: A New Tool for Path Planning
• RRT-Connect: RRT-Connect: An Efficient Approach to Single-Query Path Planning
• RRT*: Sampling-based algorithms for optimal motion planning
• Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal heuristic
• ACO: Ant Colony Optimization: A New Meta-Heuristic

Local Planning

• DWA: The Dynamic Window Approach to Collision Avoidance
• APF: Real-time obstacle avoidance for manipulators and mobile robots
• RPP: Regulated Pure Pursuit for Robot Path Tracking
• DDPG: Continuous control with deep reinforcement learning

Curve Generation

• [Dubins: ]() On curves of minimal length with a constraint on average curvature, and with prescribed initial and terminal positions and tangents

Acknowledgment

• Our visualization and animation framework of Python Version refers to https://github.com/zhm-real/PathPlanning. Thanks sincerely.