mirror of
https://github.com/zhm-real/PathPlanning.git
synced 2026-08-30 00:50:46 +08:00
Huiming Zhou
147 lines
9.2 KiB
Markdown
147 lines
9.2 KiB
Markdown
Directory Structure
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------
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.
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└── Search-based Planning
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├── Breadth-First Searching (BFS)
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├── Depth-First Searching (DFS)
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├── Best-First Searching
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├── Dijkstra's
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├── A*
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├── Bidirectional A*
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├── Anytime Repairing A*
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├── Learning Real-time A* (LRTA*)
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├── Real-time Adaptive A* (RTAA*)
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├── Lifelong Planning A* (LPA*)
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├── Dynamic A* (D*)
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├── D* Lite
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├── Anytime D*
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└── Potential Field
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└── Sampling-based Planning
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├── RRT
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├── RRT-Connect
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├── Extended-RRT
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├── Dynamic-RRT
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├── RRT*
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├── Informed RRT*
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├── RRT* Smart
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├── Anytime RRT*
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├── Closed-Loop RRT*
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├── Spline-RRT*
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├── LQR-RRT*
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├── Fast Marching Trees (FMT*)
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└── Batch Informed Trees (BIT*)
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## Animations - Search-Based
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### Best-First & 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_Planning/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_Planning/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_Planning/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_Planning/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_Planning/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_Planning/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_Planning/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_Planning/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_Planning/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_Planning/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_Planning/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_Planning/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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## Animation - Sampling-Based
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### RRT & 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/PathPlanning/blob/master/Sampling_based_Planning/gif/RRT_2D.gif" alt="value iteration" width="400"/></a></td>
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<td><img src="https://github.com/zhm-real/PathPlanning/blob/master/Sampling_based_Planning/gif/Goal_biasd_RRT_2D.gif" alt="value iteration" 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/PathPlanning/blob/master/Sampling_based_Planning/gif/RRT_CONNECT_2D.gif" alt="value iteration" width="400"/></a></td>
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<td><img src="https://github.com/zhm-real/PathPlanning/blob/master/Sampling_based_Planning/gif/Extended_RRT_2D.gif" alt="value iteration" 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/PathPlanning/blob/master/Sampling_based_Planning/gif/Dynamic_RRT_2D.gif" alt="value iteration" width="400"/></a></td>
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<td><img src="https://github.com/zhm-real/PathPlanning/blob/master/Sampling_based_Planning/gif/RRT_STAR2_2D.gif" alt="value iteration" 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/PathPlanning/blob/master/Sampling_based_Planning/gif/RRT_STAR_SMART_2D.gif" alt="value iteration" width="400"/></a></td>
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<td><img src="https://github.com/zhm-real/PathPlanning/blob/master/Sampling_based_Planning/gif/FMT.gif" alt="value iteration" 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/PathPlanning/blob/master/Sampling_based_Planning/gif/INFORMED_RRT_STAR_2D3.gif" alt="value iteration" width="400"/></a></td>
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<td><img src="https://github.com/zhm-real/PathPlanning/blob/master/Sampling_based_Planning/gif/BIT2.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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### Search-base Planning
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* [A*: ](https://ieeexplore.ieee.org/document/4082128) A Formal Basis for the Heuristic Determination of Minimum Cost Paths
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* [Learning Real-Time A*: ](https://arxiv.org/pdf/1110.4076.pdf) Learning in Real-Time Search: A Unifying Framework
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* [Real-Time Adaptive A*: ](http://idm-lab.org/bib/abstracts/papers/aamas06.pdf) Real-Time Adaptive A*
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* [Lifelong Planning A*: ](https://www.cs.cmu.edu/~maxim/files/aij04.pdf) Lifelong Planning A*
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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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* [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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* [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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* [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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* [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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### Sampling-based Planning
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* [RRT: ](http://msl.cs.uiuc.edu/~lavalle/papers/Lav98c.pdf) Rapidly-Exploring Random Trees: A New Tool for Path Planning
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* [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
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* [Extended-RRT: ](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1.7617&rep=rep1&type=pdf) Real-Time Randomized Path Planning for Robot Navigation
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* [Dynamic-RRT: ](https://www.ri.cmu.edu/pub_files/pub4/ferguson_david_2006_2/ferguson_david_2006_2.pdf) Replanning with RRTs
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* [RRT*: ](https://journals.sagepub.com/doi/abs/10.1177/0278364911406761) Sampling-based algorithms for optimal motion planning
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* [Anytime-RRT*: ](https://dspace.mit.edu/handle/1721.1/63170) Anytime Motion Planning using the RRT*
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* [Closed-loop RRT* (CL-RRT*): ](http://acl.mit.edu/papers/KuwataTCST09.pdf) Real-time Motion Planning with Applications to Autonomous Urban Driving
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* [Spline-RRT*: ](https://ieeexplore.ieee.org/abstract/document/6987895?casa_token=B9GUwVDbbncAAAAA:DWscGFLIa97ptgH7NpUQUL0A2ModiiBDBGklk1z7aDjI11Kyfzo8rpuFstdYcjOofJfCjR-mNw) Optimal path planning based on spline-RRT* for fixed-wing UAVs operating in three-dimensional environments
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* [LQR-RRT*: ](https://lis.csail.mit.edu/pubs/perez-icra12.pdf) Optimal Sampling-Based Motion Planning with Automatically Derived Extension Heuristics
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* [RRT#: ](http://dcsl.gatech.edu/papers/icra13.pdf) Use of Relaxation Methods in Sampling-Based Algorithms for Optimal Motion Planning
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* [RRT*-Smart: ](http://save.seecs.nust.edu.pk/pubs/ICMA2012.pdf) Rapid convergence implementation of RRT* towards optimal solution
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* [Informed RRT*: ](https://arxiv.org/abs/1404.2334) Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic
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* [Fast Marching Trees (FMT*): ](https://arxiv.org/abs/1306.3532) a Fast Marching Sampling-Based Method for Optimal Motion Planning in Many Dimensions
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* [Motion Planning using Lower Bounds (MPLB): ](https://ieeexplore.ieee.org/document/7139773) Asymptotically-optimal Motion Planning using lower bounds on cost
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* [Batch Informed Trees (BIT*): ](https://arxiv.org/abs/1405.5848) Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs
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* [Advanced Batch Informed Trees (ABIT*): ](https://arxiv.org/abs/2002.06589) Sampling-Based Planning with Advanced Graph-Search Techniques ((ICRA) 2020)
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* [Adaptively Informed Trees (AIT*): ](https://arxiv.org/abs/2002.06599) Fast Asymptotically Optimal Path Planning through Adaptive Heuristics ((ICRA) 2020)
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