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Course Outline
Introduction to Path Planning for Autonomous Vehicles
- Core principles and key challenges in path planning
- Applications across autonomous driving and robotics
- Survey of conventional and contemporary planning techniques
Graph-Based Path Planning Algorithms
- Overview of A* and Dijkstra’s algorithms
- Implementing A* for grid-based pathfinding solutions
- Dynamic adaptations: D* and D* Lite for evolving environments
Sampling-Based Path Planning Algorithms
- Random sampling methods: RRT and RRT*
- Strategies for path smoothing and optimisation
- Managing non-holonomic constraints
Optimization-Based Path Planning
- Framing path planning as an optimisation challenge
- Trajectory optimisation via nonlinear programming
- Gradient-based and gradient-free optimisation approaches
Learning-Based Path Planning
- Utilising Deep Reinforcement Learning (DRL) for path optimisation
- Fusing DRL with conventional algorithms
- Adaptive path planning through machine learning models
Handling Dynamic and Uncertain Environments
- Reactive planning techniques for immediate response
- Obstacle avoidance strategies and predictive control
- Incorporating perception data for adaptive navigation
Evaluating and Benchmarking Path Planning Algorithms
- Key metrics for path efficiency, safety, and computational load
- Simulation and testing within ROS and Gazebo environments
- Case study: A comparative analysis of RRT* and D* in complex settings
Case Studies and Real-World Applications
- Path planning solutions for autonomous delivery robots
- Implementations in self-driving cars and UAVs
- Practical project: Developing an adaptive path planner using RRT*
Requirements
- Proficiency in Python programming
- Practical experience with robotics systems and control algorithms
- Familiarity with autonomous vehicle technologies
Target Audience
- Robotics engineers specialising in autonomous systems
- AI researchers focused on path planning and navigation
- Senior developers engaged in self-driving technology projects
21 Hours
