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https://github.com/ai-winter/python_motion_planning

Motion planning and Navigation of AGV/AMR：ROS planner plugin implementation of A*, JPS, D*, LPA*, D* Lite, (Lazy)Theta*, RRT, RRT*, RRT-Connect, Informed RRT*, ACO, Voronoi, PID, DWA, APF, LQR, MPC, Bezier, Dubins etc.
https://github.com/ai-winter/python_motion_planning

a-star ant-colony-optimization artificial-potential-field bezier-curve d-star d-star-lite dijkstra dubins-curve informed-rrt-star jump-point-search lazy-theta-star lpa-star model-predictive-control motion-planning python rrt rrt-connect rrt-star theta-star voronoi

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Motion planning and Navigation of AGV/AMR：ROS planner plugin implementation of A*, JPS, D*, LPA*, D* Lite, (Lazy)Theta*, RRT, RRT*, RRT-Connect, Informed RRT*, ACO, Voronoi, PID, DWA, APF, LQR, MPC, Bezier, Dubins etc.

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README 

          

# 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](https://blog.csdn.net/frigidwinter/category_11410243.html).



We also provide [ROS C++](https://github.com/ai-winter/ros_motion_planning) version and [Matlab](https://github.com/ai-winter/matlab_motion_planning) version.



# Quick Start



## Overview

The source file structure is shown below



```

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`.



## Install

*(Optional)* The code was tested in python=3.10. We recommend using `conda` to install the dependencies.



```shell

conda create -n pmp python=3.10

conda activate pmp

```



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



```shell

pip install python-motion-planning

```



## Run

Below are some simple examples.



1. Run planning and animation separately

```python

import python_motion_planning as pmp

planner = pmp.AStar(start=(5, 5), goal=(45, 25), env=pmp.Grid(51, 31))

cost, path, expand = planner.plan()

planner.plot.animation(path, str(planner), cost, expand)  # animation

```



2. Run planning and animation in one step

```python

import python_motion_planning as pmp

planner = pmp.AStar(start=(5, 5), goal=(45, 25), env=pmp.Grid(51, 31))

planner.run()       # run both planning and animation

```



3. Create planner in factory mode

```python

import python_motion_planning as pmp

search_factory = pmp.SearchFactory()

planner = search_factory("a_star", start=(5, 5), goal=(45, 25), env=pmp.Grid(51, 31))

planner.run()       # run both planning and animation

```



More examples can be found in the folder `examples` in the repository.



## Documentation



For more details, you can refer to [online documentation](https://ai-winter.github.io/python_motion_planning/).



The documentation is auto-generated using mkdocs. To do this, enter the root directory and run



```shell

python generate_mkdocs.py

mkdocs serve

```



Then open the browser and go to [http://127.0.0.1:8000](http://127.0.0.1:8000). That is the generated documentation.



# Version

## Global Planner



Planner      | Version                                                                                                                                                                         | Animation

------------ |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| --------- 

**GBFS**              | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/global_planner/graph_search/gbfs.py)            | ![gbfs_python.png](assets/gbfs_python.png) 

**Dijkstra**                 | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/global_planner/graph_search/dijkstra.py)        | ![dijkstra_python.png](assets/dijkstra_python.png)

**A***               | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/global_planner/graph_search/a_star.py)          |  ![a_star_python.png](assets/a_star_python.png) 

**JPS**                 | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/global_planner/graph_search/jps.py)             | ![jps_python.png](assets/jps_python.png)

**D***                  | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/global_planner/graph_search/d_star.py)          | ![d_star_python.png](assets/d_star_python.png)

**LPA***                 | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/global_planner/graph_search/lpa_star.py)        | ![lpa_star_python.png](assets/lpa_star_python.png) 

**D\* Lite**                | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/global_planner/graph_search/d_star_lite.py)     | ![d_star_lite_python.png](assets/d_star_lite_python.png)

**Theta\***                | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/global_planner/graph_search/theta_star.py)      | ![theta_star_python.png](assets/theta_star_python.png)

**Lazy Theta\***                | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/global_planner/graph_search/lazy_theta_star.py) | ![lazy_theta_star_python.png](assets/lazy_theta_star_python.png)

**S-Theta\***                | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/global_planner/graph_search/s_theta_star.py)    | ![s_theta_star_python.png](assets/s_theta_star_python.png)

**Anya**                | [![Status](https://img.shields.io/badge/develop-v1.0-red)](https://github.com/ai-winter/python_motion_planning/blob/master/global_planner/graph_search/anya.py)                 | ![Status](https://img.shields.io/badge/gif-none-yellow)

**Voronoi**                | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/global_planner/graph_search/voronoi.py)         | ![voronoi_python.png](assets/voronoi_python.png) 

**RRT**                 | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/global_planner/sample_search/rrt.py)            | ![rrt_python.png](assets/rrt_python.png)

**RRT***                 | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/global_planner/sample_search/rrt_star.py)       | ![rrt_star_python.png](assets/rrt_star_python.png)

**Informed RRT**                 | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/global_planner/sample_search/informed_rrt.py)   | ![informed_rrt_python.png](assets/informed_rrt_python.png)

**RRT-Connect**                | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/global_planner/sample_search/rrt_connect.py)    | ![rrt_connect_python.png](assets/rrt_connect_python.png)

| **ACO** | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/global_planner/evolutionary_search/aco.py)      | ![aco_python.png](assets/aco_python.png)

| **GA**  | ![Status](https://img.shields.io/badge/develop-v1.0-red)                                                                                                                        | ![Status](https://img.shields.io/badge/gif-none-yellow) 

| **PSO**  | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/global_planner/evolutionary_search/pso.py)      | ![pso_python.png](assets/pso_python.svg) ![pso_python_cost.png](assets/pso_python_cost.svg) 



## Local Planner



| Planner     | Version                                                                                                                                                | Animation                                     

|-------------|--------------------------------------------------------------------------------------------------------------------------------------------------------| -------------------------------------------------- 

| **PID**     | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/local_planner/pid.py)  | ![pid_python.svg](assets/pid_python.svg) 

| **APF**     | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/local_planner/apf.py)  | ![apf_python.svg](assets/apf_python.svg) 

| **DWA**     | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/local_planner/dwa.py)  | ![dwa_python.svg](assets/dwa_python.svg)

| **RPP**     | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/local_planner/rpp.py)  | ![rpp_python.svg](assets/rpp_python.svg)

| **LQR**     | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/local_planner/lqr.py)  | ![lqr_python.svg](assets/lqr_python.svg) 

| **TEB**     | ![Status](https://img.shields.io/badge/develop-v1.0-red)                                                                                               | ![Status](https://img.shields.io/badge/gif-none-yellow) 

| **MPC**     | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/local_planner/mpc.py)  | ![mpc_python.svg](assets/mpc_python.svg)

| **MPPI**    | ![Status](https://img.shields.io/badge/develop-v1.0-red)                                                                                               |![Status](https://img.shields.io/badge/gif-none-yellow)

| **Lattice** | ![Status](https://img.shields.io/badge/develop-v1.0-red)                                                                                               |![Status](https://img.shields.io/badge/gif-none-yellow)

| **DQN**    | ![Status](https://img.shields.io/badge/develop-v1.0-red)                                                                                               |![Status](https://img.shields.io/badge/gif-none-yellow)

| **DDPG**    | ![Status](https://img.shields.io/badge/develop-v1.0-red)                                                                                               |![Status](https://img.shields.io/badge/gif-none-yellow)



## Curve Generation



| Planner | Version   | Animation                                |

| ------- | -------------------------------------------------------- | -------------------------------------------------------- 

| **Polynomia** | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/curve_generation/polynomial_curve.py) | ![polynomial_curve_python.gif](assets/polynomial_curve_python.gif)

| **Bezier** | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/curve_generation/bezier_curve.py) | ![bezier_curve_python.png](assets/bezier_curve_python.png)

| **Cubic Spline** | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/curve_generation/cubic_spline.py) | ![cubic_spline_python.png](assets/cubic_spline_python.png)

| **BSpline** | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/curve_generation/bspline_curve.py) | ![bspline_curve_python.png](assets/bspline_curve_python.png)

| **Dubins** | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/curve_generation/dubins_curve.py) | ![dubins_curve_python.png](assets/dubins_curve_python.png)

| **Reeds-Shepp** | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/curve_generation/reeds_shepp.py) | ![reeds_shepp_python.png](assets/reeds_shepp_python.gif)

| **Fem-Pos Smoother** | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/curve_generation/fem_pos_smooth.py) | ![fem_pos_smoother_python.png](assets/fem_pos_smoother_python.png)



# Papers

## Global Planning



* [A*: ](https://ieeexplore.ieee.org/document/4082128) A Formal Basis for the heuristic Determination of Minimum Cost Paths

* [JPS:](https://ojs.aaai.org/index.php/AAAI/article/view/7994) Online Graph Pruning for Pathfinding On Grid Maps

* [Lifelong Planning A*: ](https://www.cs.cmu.edu/~maxim/files/aij04.pdf) Lifelong Planning A*

* [D*: ](http://web.mit.edu/16.412j/www/html/papers/original_dstar_icra94.pdf) Optimal and Efficient Path Planning for Partially-Known Environments

* [D* Lite: ](http://idm-lab.org/bib/abstracts/papers/aaai02b.pdf) D* Lite

* [Theta*: ](https://www.jair.org/index.php/jair/article/view/10676) Theta*: Any-Angle Path Planning on Grids

* [Lazy Theta*: ](https://ojs.aaai.org/index.php/AAAI/article/view/7566) Lazy Theta*: Any-Angle Path Planning and Path Length Analysis in 3D

* [S-Theta*: ](https://link.springer.com/chapter/10.1007/978-1-4471-4739-8_8) S-Theta*: low steering path-planning algorithm

* [Anya: ](http://www.grastien.net/ban/articles/hgoa-jair16.pdf) Optimal Any-Angle Pathfinding In Practice

* [RRT: ](http://msl.cs.uiuc.edu/~lavalle/papers/Lav98c.pdf) Rapidly-Exploring Random Trees: A New Tool for Path Planning

* [RRT-Connect: ](http://www-cgi.cs.cmu.edu/afs/cs/academic/class/15494-s12/readings/kuffner_icra2000.pdf) RRT-Connect: An Efficient Approach to Single-Query Path Planning

* [RRT*: ](https://journals.sagepub.com/doi/abs/10.1177/0278364911406761) Sampling-based algorithms for optimal motion planning

* [Informed RRT*: ](https://arxiv.org/abs/1404.2334) Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal heuristic

* [ACO: ](http://www.cs.yale.edu/homes/lans/readings/routing/dorigo-ants-1999.pdf) Ant Colony Optimization: A New Meta-Heuristic



## Local Planning



* [DWA: ](https://www.ri.cmu.edu/pub_files/pub1/fox_dieter_1997_1/fox_dieter_1997_1.pdf) The Dynamic Window Approach to Collision Avoidance

* [APF: ](https://ieeexplore.ieee.org/document/1087247) Real-time obstacle avoidance for manipulators and mobile robots

* [RPP: ](https://arxiv.org/pdf/2305.20026.pdf) Regulated Pure Pursuit for Robot Path Tracking

* [DDPG: ](https://arxiv.org/abs/1509.02971) 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](https://github.com/zhm-real/PathPlanning). Thanks sincerely.