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
└── Stochastic Shortest Path
    ├── value_iteration.py                          # value iteration
    ├── policy_iteration.py                         # policy iteration
    ├── Q-value_iteration.py                        # Q-value iteration
    └── Q-policy_iteration.py                       # Q-policy iteration
└── Model-free Control
    ├── Sarsa.py                                    # SARSA : on-policy TD control
    └── Q-learning.py                               # Q-learning : off-policy TD control

Animations - Search-Based

Best-First & Dijkstra

A* and A* Variants

Animation - Sampling-Based

RRT & Variants

Value/Policy/Q-value/Q-policy Iteration

• Brown: losing states

SARSA(on-policy) & Q-learning(off-policy)

• Brown: losing states

Papers

Search-base Planning

• D*: Optimal and Efficient Path Planning for Partially-Known Environments
• Lifelong Planning A*: Lifelong Planning A*
• Anytime Repairing A*: ARA*: Anytime A* with Provable Bounds on Sub-Optimality
• 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
• Bidirectional-RRT*: Optimal Bidirectional Rapidly-Exploring Random Trees
• 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