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Course Outline
Introduction to Path Planning for Autonomous Vehicles
- Core concepts and key challenges in path planning
- Applications in autonomous driving and robotics
- Overview of traditional and modern planning techniques
Graph-Based Path Planning Algorithms
- Summary of A* and Dijkstra algorithms
- Application of A* for grid-based pathfinding
- Dynamic variations: D* and D* Lite for changing 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
- Modeling the path planning problem as an optimization task
- Trajectory optimization via nonlinear programming
- Gradient-based and gradient-free optimization methods
Learning-Based Path Planning
- Deep reinforcement learning (DRL) for path optimization
- Combining DRL with traditional algorithms
- Adaptive path planning leveraging machine learning models
Handling Dynamic and Uncertain Environments
- Reactive planning techniques for immediate response
- Obstacle avoidance and predictive control strategies
- Incorporating perception data for adaptive navigation
Evaluating and Benchmarking Path Planning Algorithms
- Metrics for assessing path efficiency, safety, and computational cost
- Simulation and testing using 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: Building an adaptive path planner using RRT*
Requirements
- Strong proficiency in Python programming
- Practical experience with robotics systems and control algorithms
- Basic familiarity with autonomous vehicle technologies
Target Audience
- Robotics engineers specializing in autonomous systems
- AI researchers focused on path planning and navigation
- Advanced developers engaged in self-driving technology projects
21 Hours
