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Course Outline
Introduction to Path Planning for Autonomous Vehicles
- Path planning fundamentals and challenges
- Applications in autonomous driving and robotics
- Review of traditional and modern planning techniques
Graph-Based Path Planning Algorithms
- Overview of A* and Dijkstra algorithms
- Implementing A* for grid-based pathfinding
- Dynamic variants: D* and D* Lite for changing environments
Sampling-Based Path Planning Algorithms
- Random sampling techniques: RRT and RRT*
- Path smoothing and optimisation
- Handling non-holonomic constraints
Optimisation-Based Path Planning
- Formulating the path planning problem as an optimisation problem
- Trajectory optimisation using non-linear programming
- Gradient-based and gradient-free optimisation techniques
Learning-Based Path Planning
- Deep reinforcement learning (DRL) for path optimisation
- Integrating DRL with traditional algorithms
- Adaptive path planning using machine learning models
Handling Dynamic and Uncertain Environments
- Reactive planning techniques for real-time response
- Obstacle avoidance and predictive control
- Integrating perception data for adaptive navigation
Evaluating and Benchmarking Path Planning Algorithms
- Metrics for path efficiency, safety, and computational complexity
- Simulating and testing in ROS and Gazebo
- Case study: Comparing RRT* and D* in complex scenarios
Case Studies and Real-World Applications
- Path planning for autonomous delivery robots
- Applications in self-driving cars and UAVs
- Project: Implementing an adaptive path planner using RRT*
Summary and Next Steps
Requirements
- Proficiency in Python programming
- Experience with robotics systems and control algorithms
- Familiarity with autonomous vehicle technologies
Audience
- Robotics engineers specialising in autonomous systems
- AI researchers focusing on path planning and navigation
- Advanced-level developers working on self-driving technology
21 Hours
