Motion planning algorithms commonly used on autonomous vehicles. (path planning + path tracking)

Overview

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

Requirement

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

Vehicle models

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

Hybrid A* Planner

State Lattice Planner

Controllers

Paper

Planning

• 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

Control

• 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

Useful Material

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