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Course Outline
Introduction to Path Planning for Autonomous Vehicles
- Core principles and challenges in path planning
- Applications in autonomous driving and robotics
- Review of conventional and contemporary planning methods
Graph-Based Path Planning Algorithms
- Overview of A* and Dijkstra algorithms
- Implementing 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 optimization techniques
- 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
- Integrating DRL with traditional algorithms
- Adaptive path planning leveraging machine learning models
Managing Dynamic and Uncertain Environments
- Reactive planning strategies for real-time response
- Obstacle avoidance and predictive control
- Incorporating perception data for adaptive navigation
Assessing and Benchmarking Path Planning Algorithms
- Metrics for path efficiency, safety, and computational complexity
- Simulation and testing using ROS and Gazebo
- Case study: Comparing RRT* and D* in complex situations
Case Studies and Real-World Applications
- Path planning for autonomous delivery robots
- Use cases in self-driving cars and UAVs
- Project: Developing an adaptive path planner using RRT*
Requirements
- Strong proficiency in Python programming
- Hands-on experience with robotics systems and control algorithms
- Knowledge of autonomous vehicle technologies
Target Audience
- Robotics engineers focused on autonomous systems
- AI researchers specializing in path planning and navigation
- Advanced developers working on self-driving technology
21 Hours
