furkanhanilci / planning Goto Github PK

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└── Search-based Planning
    ├── Breadth-First Searching (BFS)
    ├── Depth-First Searching (DFS)
    ├── Best-First Searching
    ├── Dijkstra's
    ├── A*
    ├── Bidirectional A*
    ├── Anytime Repairing A*
    ├── Learning Real-time A* (LRTA*)
    ├── Real-time Adaptive A* (RTAA*)
    ├── Lifelong Planning A* (LPA*)
    ├── Dynamic A* (D*)
    ├── D* Lite
    └── Anytime D*
└── Sampling-based Planning
    ├── RRT
    ├── RRT-Connect
    ├── Extended-RRT
    ├── Dynamic-RRT
    ├── RRT*
    ├── Informed RRT*
    ├── RRT* Smart
    ├── Anytime RRT*
    ├── Closed-Loop RRT*
    ├── Spline-RRT*
    ├── Fast Marching Trees (FMT*)
    └── Batch Informed Trees (BIT*)
└── Papers

This repository implemented some common motion planners used on autonomous vehicles, including

• Hybrid A* Planner
• Frenet Optimal Trajectory
• Hierarchical Optimization-Based Collision Avoidance (H-OBCA) (Incomplete)

Also, this repository provides some controllers for path tracking, including

• Pure Pursuit + PID
• Rear-Wheel Feedback + PID
• Front-Wheel Feedback / Stanley + PID
• LQR + PID
• Linear MPC

• Python 3.6 or above
• SciPy
• cvxpy
• Reeds-Shepp Curves
• pycubicspline

This repository uses two models: simple car model and car pulling trailers model.

• Basic Path Planning Algorithms: PathPlanning
• Baidu Apollo Planning module: Recommended Materials
• Survey of Planning and Control algos: A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles
• Hybrid A* Planner: Practical Search Techniques in Path Planning for Autonomous Driving
• Frenet Optimal Trajectory: Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame

• Baidu Apollo Control module: Recommended Materials
• Pure Pursuit: Implementation of the Pure Pursuit Path Tracking Algorithm
• Rear-Wheel Feedback: Automatic Steering Methods for Autonomous Automobile Path Tracking
• Front-Wheel Feedback / Stanley: Stanley: The Robot that Won the DARPA Grand Challenge
• LQR: ApolloAuto/apollo: An open autonomous driving platform
• Linear MPC: MPC-Based Approach to Active Steering for Autonomous Vehicle Systems

• HybridAStarTrailer (Julia): by AtsushiSakai
• Hybrid Path Planner (C++): by KTH Research Concept Vehicle

• 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

• 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
• 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
• RRT#: Use of Relaxation Methods in Sampling-Based Algorithms for Optimal Motion Planning
• RRT*-Smart: Rapid convergence implementation of RRT* towards optimal solution
• Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal heuristic
• Fast Marching Trees (FMT*): a Fast Marching Sampling-Based Method for Optimal Motion Planning in Many Dimensions
• Motion Planning using Lower Bounds (MPLB): Asymptotically-optimal Motion Planning using lower bounds on cost
• Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs
• Advanced Batch Informed Trees (ABIT*): Sampling-Based Planning with Advanced Graph-Search Techniques ((ICRA) 2020)
• Adaptively Informed Trees (AIT*): Fast Asymptotically Optimal Path Planning through Adaptive Heuristics ((ICRA) 2020)