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Duration 21 hours
Course Outline
Introduction to Path Planning for Autonomous Vehicles
- Core fundamentals and key challenges in path planning
- Applications in autonomous driving and robotics
- An overview of both traditional and contemporary planning methods
Graph-Based Path Planning Algorithms
- Introduction to A* and Dijkstra algorithms
- Application of A* for grid-based pathfinding
- Dynamic adaptations: D* and D* Lite for evolving environments
Sampling-Based Path Planning Algorithms
- Random sampling methods: RRT and RRT*
- Techniques for path smoothing and optimization
- Managing non-holonomic constraints
Optimization-Based Path Planning
- Defining the path planning challenge as an optimization task
- Trajectory optimization via nonlinear programming
- Gradient-based and derivative-free optimization approaches
Learning-Based Path Planning
- Deep reinforcement learning (DRL) for path refinement
- Combining DRL with conventional algorithms
- Adaptive path planning leveraging machine learning models
Handling Dynamic and Uncertain Environments
- Reactive planning methods for immediate response
- Obstacle evasion and predictive control
- Incorporating perception data for adaptive navigation
Evaluating and Benchmarking Path Planning Algorithms
- Metrics for assessing path efficiency, safety, and computational load
- Simulation and testing within ROS and Gazebo
- Case study: Contrasting RRT* and D* in complex situations
Case Studies and Real-World Applications
- Path planning strategies for autonomous delivery robots
- Use cases in self-driving cars and UAVs
- Project: Building an adaptive path planner utilizing RRT*
Requirements
- Strong proficiency in Python programming
- Hands-on experience with robotic systems and control algorithms
- Knowledge of autonomous vehicle technologies
Target Audience
- Robotics engineers with a specialization in autonomous systems
- AI researchers concentrated on path planning and navigation
- Senior developers working on self-driving technology
