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Course Outline
Introduction to Path Planning for Autonomous Vehicles
- Core principles and challenges in path planning
- Use cases in autonomous driving and robotics
- Review of conventional and contemporary planning methods
Graph-Based Path Planning Algorithms
- Introduction to A* and Dijkstra algorithms
- Applying A* for grid-based pathfinding
- Dynamic variations: D* and D* Lite for shifting environments
Sampling-Based Path Planning Algorithms
- Random sampling methods: RRT and RRT*
- Path smoothing and refinement
- Managing non-holonomic constraints
Optimization-Based Path Planning
- Structuring the path planning challenge as an optimization task
- Trajectory optimization via nonlinear programming
- Gradient-based and gradient-free optimization approaches
Learning-Based Path Planning
- Deep reinforcement learning (DRL) for path refinement
- Blending DRL with conventional algorithms
- Adaptive path planning leveraging machine learning models
Managing Dynamic and Uncertain Environments
- Reactive planning methods for instant response
- Obstacle avoidance and predictive control
- Incorporating perception data for adaptive navigation
Evaluating and Benchmarking Path Planning Algorithms
- Metrics for path efficiency, safety, and computational load
- Simulation and testing using ROS and Gazebo
- Case study: Contrasting RRT* and D* in complex settings
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
- Knowledge of autonomous vehicle technologies
Audience
- Robotics engineers focused on autonomous systems
- AI researchers concentrating on path planning and navigation
- Advanced-level developers engaged in self-driving technology
21 Hours
