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Course Outline
Foundations of Path Planning for Autonomous Vehicles
- Core concepts of path planning and associated challenges
- Practical applications in autonomous driving and robotics
- Analysis of traditional versus modern planning techniques
Graph-Based Path Planning Approaches
- Introduction to A* and Dijkstra algorithms
- Implementation of A* for grid-based pathfinding
- Dynamic adaptations: D* and D* Lite for shifting environments
Sampling-Based Path Planning Methods
- Random sampling strategies: RRT and RRT*
- Techniques for path smoothing and optimization
- Addressing non-holonomic constraints
Optimization-Driven Path Planning
- Structuring the path planning challenge as an optimization problem
- Trajectory optimization via nonlinear programming
- Application of gradient-based and gradient-free optimization methods
Learning-Enhanced Path Planning
- Deep reinforcement learning (DRL) for path optimization
- Combining DRL with conventional algorithms
- Adaptive path planning leveraging machine learning models
Navigating Dynamic and Uncertain Environments
- Reactive planning methods for immediate real-time response
- Obstacle avoidance strategies and predictive control
- Incorporating perception data for adaptive navigation
Performance Evaluation and Benchmarking of Algorithms
- Key metrics for path efficiency, safety, and computational load
- Simulation and testing within ROS and Gazebo environments
- Case study: Contrasting RRT* and D* in complex situations
Case Studies and Industry 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 robotics systems and control algorithms
- Working knowledge 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
