README.md 8.8 KB
Directory Structure
.
└── Search-based Planning
└── Search_2D
├── bfs.py # breadth-first searching
├── dfs.py # depth-first searching
├── dijkstra.py # dijkstra's
├── a_star.py # A*
├── bidirectional_a_star.py # Bidirectional A*
├── ARAstar.py # Anytime Reparing A*
├── IDAstar.py # Iteratively Deepening A*
├── LRTAstar.py # Learning Real-time A*
├── RTAAstar.py # Real-time Adaptive A*
├── LPAstar.py # Lifelong Planning A*
├── D_star.py # D* (Dynamic A*)
├── Anytime_D_star.py # Anytime D*
└── D_star_Lite.py # D* Lite
└── Search_3D
├── Astar3D.py # A*_3D
├── bidirectional_Astar3D.py # Bidirectional A*_3D
├── RTA_Astar3D.py # Real-time Adaptive A*_3D
└── LRT_Astar3D.py # Learning Real-time A*_3D
└── Sampling-based Planning
└── rrt_2D
├── rrt.py # rrt : goal-biased rrt
└── rrt_star.py
└── rrt_3D
├── rrt3D.py # rrt3D : goal-biased rrt3D
└── rrtstar3D.py
Animations - Search-Based
Best-First & Dijkstra
 |  |
A* and A* Variants
 |  |
 |  |
 |  |
 |  |
 |  |
Animation - Sampling-Based
RRT & Variants
 |  |
 |  |
 |  |
Papers
Search-base Planning
- A*: A Formal Basis for the Heuristic Determination of Minimum Cost Paths
- Learning Real-Time A*: Learning in Real-Time Search: A Unifying Framework
- Real-Time Adaptive A*: Real-Time Adaptive A*
- Lifelong Planning A*: Lifelong Planning A*
- Anytime Repairing A*: ARA: Anytime A with Provable Bounds on Sub-Optimality
- D*: Optimal and Efficient Path Planning for Partially-Known Environments
- D* Lite: D* Lite
- Field D*: Field D*: An Interpolation-based Path Planner and Replanner
- Anytime D*: Anytime Dynamic A*: An Anytime, Replanning Algorithm
- Focussed D*: The Focussed D* Algorithm for Real-Time Replanning
- Potential Field, [PPT]: Real-Time Obstacle Avoidance for Manipulators and Mobile Robots
- Hybrid A*: Practical Search Techniques in Path Planning for Autonomous Driving
Sampling-based Planning
- RRT: Rapidly-Exploring Random Trees: A New Tool for Path Planning
- RRT-Connect: RRT-Connect: An Efficient Approach to Single-Query Path Planning
- Extended-RRT: Real-Time Randomized Path Planning for Robot Navigation
- Dynamic-RRT: Replanning with RRTs
- RRT*: Sampling-based algorithms for optimal motion planning
- Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic
- RRT*-Smart: Rapid convergence implementation of RRT* towards optimal solution
- Anytime-RRT*: Anytime Motion Planning using the RRT*
- Closed-loop RRT* (CL-RRT*): Real-time Motion Planning with Applications to Autonomous Urban Driving
- Spline-RRT*: Optimal path planning based on spline-RRT* for fixed-wing UAVs operating in three-dimensional environments
- LQR-RRT*: Optimal Sampling-Based Motion Planning with Automatically Derived Extension Heuristics
- Fast Marching Trees (FMT*): a Fast Marching Sampling-Based Method for Optimal Motion Planning in Many Dimensions
- Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs