mirror of
https://github.com/zhm-real/PathPlanning.git
synced 2026-08-29 16:40:46 +08:00
Huiming Zhou
120 lines
7.2 KiB
Markdown
120 lines
7.2 KiB
Markdown
Directory Structure
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------
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.
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└── Search-based Planning
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└── Search_2D
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├── bfs.py # breadth-first searching
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├── dfs.py # depth-first searching
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├── dijkstra.py # dijkstra's
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├── a_star.py # A*
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├── bidirectional_a_star.py # Bidirectional A*
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├── ARAstar.py # Anytime Reparing A*
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├── IDAstar.py # Iteratively Deepening A*
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├── LRTAstar.py # Learning Real-time A*
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├── RTAAstar.py # Real-time Adaptive A*
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├── LPAstar.py # Lifelong Planning A*
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├── D_star.py # D* (Dynamic A*)
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├── Anytime_D_star.py # Anytime D*
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└── D_star_Lite.py # D* Lite
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└── Search_3D
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├── Astar3D.py # A*_3D
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├── bidirectional_Astar3D.py # Bidirectional A*_3D
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├── RTA_Astar3D.py # Real-time Adaptive A*_3D
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└── LRT_Astar3D.py # Learning Real-time A*_3D
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└── Sampling-based Planning
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└── rrt_2D
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├── rrt.py # rrt : goal-biased rrt
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└── rrt_star.py
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└── rrt_3D
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├── rrt3D.py # rrt3D : goal-biased rrt3D
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└── rrtstar3D.py
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└── Stochastic Shortest Path
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├── value_iteration.py # value iteration
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├── policy_iteration.py # policy iteration
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├── Q-value_iteration.py # Q-value iteration
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└── Q-policy_iteration.py # Q-policy iteration
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└── Model-free Control
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├── Sarsa.py # SARSA : on-policy TD control
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└── Q-learning.py # Q-learning : off-policy TD control
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## Animations
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### DFS & BFS (Dijkstra)
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<div align=right>
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<table>
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<tr>
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<td><img src="https://github.com/zhm-real/path-planning-algorithms/blob/master/Search-based%20Planning/gif/BF.gif" alt="dfs" width="400"/></a></td>
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<td><img src="https://github.com/zhm-real/path-planning-algorithms/blob/master/Search-based%20Planning/gif/Dijkstra.gif" alt="dijkstra" width="400"/></a></td>
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</tr>
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</table>
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</div>
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### A* and A* Variants
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<div align=right>
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<table>
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<tr>
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<td><img src="https://github.com/zhm-real/path-planning-algorithms/blob/master/Search-based%20Planning/gif/Astar.gif" alt="astar" width="400"/></a></td>
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<td><img src="https://github.com/zhm-real/path-planning-algorithms/blob/master/Search-based%20Planning/gif/Bi-Astar.gif" alt="biastar" width="400"/></a></td>
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</tr>
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</table>
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<table>
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<tr>
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<td><img src="https://github.com/zhm-real/path-planning-algorithms/blob/master/Search-based%20Planning/gif/RepeatedA_star.gif" alt="repeatedastar" width="400"/></a></td>
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<td><img src="https://github.com/zhm-real/path-planning-algorithms/blob/master/Search-based%20Planning/gif/ARA_star.gif" alt="arastar" width="400"/></a></td>
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</tr>
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</table>
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<table>
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<tr>
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<td><img src="https://github.com/zhm-real/path-planning-algorithms/blob/master/Search-based%20Planning/gif/LRTA_star.gif" alt="lrtastar" width="400"/></a></td>
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<td><img src="https://github.com/zhm-real/path-planning-algorithms/blob/master/Search-based%20Planning/gif/RTAA_star.gif" alt="rtaastar" width="400"/></a></td>
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</tr>
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</table>
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<table>
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<tr>
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<td><img src="https://github.com/zhm-real/path-planning-algorithms/blob/master/Search-based%20Planning/gif/LPAstar.gif" alt="lpastar" width="400"/></a></td>
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<td><img src="https://github.com/zhm-real/path-planning-algorithms/blob/master/Search-based%20Planning/gif/D_star_Lite.gif" alt="dstarlite" width="400"/></a></td>
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</tr>
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</table>
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<table>
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<tr>
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<td><img src="https://github.com/zhm-real/path-planning-algorithms/blob/master/Search-based%20Planning/gif/ADstar_small.gif" alt="lpastar" width="400"/></a></td>
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<td><img src="https://github.com/zhm-real/path-planning-algorithms/blob/master/Search-based%20Planning/gif/ADstar_sig.gif" alt="dstarlite" width="400"/></a></td>
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</tr>
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</table>
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</div>
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### Value/Policy/Q-value/Q-policy Iteration
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* Brown: losing states
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<div align=right>
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<table>
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<tr>
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<td><img src="https://github.com/zhm-real/path-planning-algorithms/blob/master/Stochastic%20Shortest%20Path/gif/VI.gif" alt="value iteration" width="400"/></a></td>
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<td><img src="https://github.com/zhm-real/path-planning-algorithms/blob/master/Stochastic%20Shortest%20Path/gif/VI_E.gif" alt="value iteration" width="400"/></a></td>
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</tr>
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</table>
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</div>
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### SARSA(on-policy) & Q-learning(off-policy)
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* Brown: losing states
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<div align=right>
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<table>
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<tr>
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<td><img src="https://github.com/zhm-real/path-planning-algorithms/blob/master/Model-free%20Control/gif/SARSA.gif" alt="value iteration" width="400"/></a></td>
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<td><img src="https://github.com/zhm-real/path-planning-algorithms/blob/master/Model-free%20Control/gif/Qlearning.gif" alt="value iteration" width="400"/></a></td>
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</tr>
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</table>
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</div>
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## Papers
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* [Potential Field, ](https://journals.sagepub.com/doi/abs/10.1177/027836498600500106) [[PPT]: ](https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf) Real-Time Obstacle Avoidance for Manipulators and Mobile Robots
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* [Hybrid A*: ](https://ai.stanford.edu/~ddolgov/papers/dolgov_gpp_stair08.pdf) Practical Search Techniques in Path Planning for Autonomous Driving
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* [Anytime Repairing A*: ](https://papers.nips.cc/paper/2382-ara-anytime-a-with-provable-bounds-on-sub-optimality.pdf) ARA*: Anytime A* with Provable Bounds on Sub-Optimality
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* [Lifelong Planning A*: ](https://www.cs.cmu.edu/~maxim/files/aij04.pdf) Lifelong Planning A*
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* [D*: ](http://web.mit.edu/16.412j/www/html/papers/original_dstar_icra94.pdf) Optimal and Efficient Path Planning for Partially-Known Environments
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* [Focussed D*: ](http://robotics.caltech.edu/~jwb/courses/ME132/handouts/Dstar_ijcai95.pdf) The Focussed D* Algorithm for Real-Time Replanning
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* [D* Lite: ](http://idm-lab.org/bib/abstracts/papers/aaai02b.pdf) D* Lite
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* [Field D*: ](http://robots.stanford.edu/isrr-papers/draft/stentz.pdf) Field D*: An Interpolation-based Path Planner and Replanner
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* [Anytime D*: ](http://www.cs.cmu.edu/~ggordon/likhachev-etal.anytime-dstar.pdf) Anytime Dynamic A*: An Anytime, Replanning Algorithm
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* [Theta* & AP Theta*: ](http://idm-lab.org/bib/abstracts/papers/aaai07a.pdf) Theta*: Any-Angle Path Planning on Grids
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* [Lazy Theta*: ](https://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/download/1930/1945) Lazy Theta*: Any-Angle Path Planning and Path Length Analysis in 3D
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* [Incremental Phi*: ](http://www.cs.cmu.edu/~maxim/files/inctheta_ijcai09.pdf) Incremental Phi*: Incremental Any-Angle Path Planning on Grids
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