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Course Outline
Foundations of Path Planning for Autonomous Vehicles
- Core principles and key challenges in path planning
- Relevance to autonomous driving and robotics applications
- Comparison of traditional and contemporary planning methods
Graph-Based Path Planning Algorithms
- Introduction to A* and Dijkstra algorithms
- Application of A* for grid-based pathfinding
- Dynamic approaches: 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 challenge
- Trajectory optimization via nonlinear programming
- Application of gradient-based and gradient-free optimization techniques
Learning-Based Path Planning
- Utilizing Deep Reinforcement Learning (DRL) for path optimization
- Combining DRL with classical algorithms
- Adaptive path planning driven by machine learning models
Navigating Dynamic and Uncertain Environments
- Reactive planning methods for immediate responses
- Strategies for obstacle avoidance and predictive control
- Using perception data for adaptive navigation
Evaluation and Benchmarking of Path Planning Algorithms
- Key metrics for efficiency, safety, and computational load
- Simulation and testing using ROS and Gazebo
- Case study: Contrast between RRT* and D* in complex settings
Real-World Case Studies and Applications
- Path planning solutions for autonomous delivery robots
- Use cases in self-driving cars and UAVs
- Project: Building an adaptive path planner with RRT*
Requirements
- Strong proficiency in Python programming
- Hands-on experience with robotics systems and control algorithms
- A solid understanding of autonomous vehicle technologies
Target Audience
- Robotics engineers specializing in autonomous systems
- AI researchers concentrated on path planning and navigation
- Senior developers engaged in self-driving technology projects
21 Hours
