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Course Outline
Introduction to Path Planning for Autonomous Vehicles
- Core concepts and key challenges in path planning
- Use cases in autonomous driving and robotics
- Review of both traditional and contemporary planning methods
Graph-Based Path Planning Algorithms
- Overview of A* and Dijkstra algorithms
- Implementation 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 path planning as an optimization problem
- Trajectory optimization via nonlinear programming
- Gradient-based and gradient-free optimization techniques
Learning-Based Path Planning
- Deep reinforcement learning (DRL) for path optimization
- Integration of DRL with classical algorithms
- Adaptive path planning leveraging machine learning models
Navigating Dynamic and Uncertain Environments
- Reactive planning strategies for real-time responsiveness
- Obstacle avoidance and predictive control mechanisms
- Incorporating perception data for adaptive navigation
Assessing and Benchmarking Path Planning Algorithms
- Key metrics for path efficiency, safety, and computational load
- Simulation and testing using ROS and Gazebo
- Case study: Contrast between RRT* and D* in complex situations
Case Studies and Real-World Applications
- Path planning for autonomous delivery robots
- Applications in self-driving cars and UAVs
- Project: Developing an adaptive path planner using RRT*
Requirements
- Strong proficiency in Python programming
- Practical experience with robotics systems and control algorithms
- Working knowledge of autonomous vehicle technologies
Target Audience
- Robotics engineers specialized in autonomous systems
- AI researchers focused on path planning and navigation
- Senior developers engaged in self-driving technology
21 Hours
