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Course Outline
Introduction to Path Planning for Autonomous Vehicles
- Core principles of path planning and associated challenges
- Applications within autonomous driving and robotics systems
- Review of conventional and contemporary planning techniques
Graph-Based Path Planning Algorithms
- Overview of A* and Dijkstra’s algorithms
- Implementing A* for grid-based navigation
- Dynamic variants: D* and D* Lite for varying environments
Sampling-Based Path Planning Algorithms
- Random sampling methods: RRT and RRT*
- Path smoothing and optimisation strategies
- Managing non-holonomic constraints
Optimization-Based Path Planning
- Formulating path planning as an optimisation challenge
- Trajectory optimisation using nonlinear programming
- Gradient-based and derivative-free optimisation methods
Learning-Based Path Planning
- Applying Deep Reinforcement Learning (DRL) to path optimisation
- Combining DRL with conventional algorithms
- Adaptive path planning leveraged via machine learning models
Navigating Dynamic and Uncertain Environments
- Reactive planning methods for immediate response
- Obstacle avoidance and predictive control mechanisms
- Incorporating perception data for adaptive navigation
Evaluating and Benchmarking Path Planning Algorithms
- Key metrics for path efficiency, safety, and computational complexity
- Simulation and testing within ROS and Gazebo
- Case study: Comparing RRT* and D* in complex scenarios
Case Studies and Real-World Applications
- Path planning solutions for autonomous delivery robots
- Implementation in self-driving cars and UAVs
- Project: Developing an adaptive path planner using RRT*
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 concentrating on path planning and navigation
- Advanced developers working on self-driving technology
21 Hours
