# ddpg_control **Repository Path**: wenb11/ddpg_control ## Basic Information - **Project Name**: ddpg_control - **Description**: DDPG实现路径跟踪，用于控制 - **Primary Language**: Unknown - **License**: GPL-3.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 1 - **Forks**: 0 - **Created**: 2024-10-14 - **Last Updated**: 2025-01-08 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## 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 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. ```shell pip install -r requirements.txt ``` To start simulation, open the folder `example` and select the algorithm, for example ```python 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** | [![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) | **DDPG** | [![Status](https://img.shields.io/badge/done-v1.0-brightgreen)](https://github.com/ai-winter/python_motion_planning/blob/master/local_planner/ddpg.py) |![ddpg_python.svg](assets/ddpg_python.svg) ## 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.