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Course Outline
Introduction to Path Planning for Autonomous Vehicles
- Core concepts and challenges in path planning
- Applications in autonomous driving and robotics
- Overview of traditional versus modern planning methods
Graph-Based Path Planning Algorithms
- Summary of A* and Dijkstra algorithms
- Implementing A* for grid-based pathfinding
- Dynamic adaptations: D* and D* Lite for fluctuating environments
Sampling-Based Path Planning Algorithms
- Random sampling methods: RRT and RRT*
- Path smoothing and optimization techniques
- Managing non-holonomic constraints
Optimization-Based Path Planning
- Defining the path planning task as an optimization problem
- Trajectory refinement using nonlinear programming
- Gradient-based and gradient-free optimization strategies
Learning-Based Path Planning
- Applying deep reinforcement learning (DRL) for path optimization
- Merging DRL with conventional algorithms
- Adaptive path planning utilizing machine learning models
Navigating Dynamic and Uncertain Environments
- Reactive planning methods for immediate response
- Obstacle evasion and predictive control mechanisms
- Integrating perception data for adaptive navigation
Assessing and Benchmarking Path Planning Algorithms
- Key metrics for path efficiency, safety, and computational load
- Simulation and testing within ROS and Gazebo
- Case study: Analysis of RRT* and D* in complex situations
Case Studies and Real-World Implementations
- Path planning solutions for autonomous delivery robots
- Use cases in self-driving cars and UAVs
- Project: Developing an adaptive path planner using RRT*
Requirements
- Strong command of Python programming
- Practical experience with robotic systems and control algorithms
- Working knowledge of autonomous vehicle technologies
Target Audience
- Robotics engineers with a focus on autonomous systems
- AI researchers concentrated on path planning and navigation tasks
- Senior developers involved in self-driving technology projects
21 Hours
