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Course Outline
Foundations of Path Planning for Autonomous Vehicles
- Core principles and key challenges in path planning
- Use cases in autonomous driving and robotics
- Comparison of traditional versus modern planning methodologies
Graph-Based Path Planning Algorithms
- Introduction to A* and Dijkstra's algorithm
- Applying A* for grid-based pathfinding solutions
- Dynamic adaptations: 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
- Utilizing gradient-based and gradient-free optimization methods
Learning-Based Path Planning
- Applying Deep Reinforcement Learning (DRL) to path optimization
- Combining DRL with conventional algorithms
- Adaptive path planning through machine learning models
Navigating Dynamic and Uncertain Environments
- Reactive planning strategies for immediate real-time responses
- Obstacle avoidance techniques and predictive control
- Incorporating perception data for adaptive navigation
Assessment and Benchmarking of Path Planning Algorithms
- Evaluation metrics for efficiency, safety, and computational cost
- Simulation and testing within ROS and Gazebo frameworks
- Case study: Contrast between RRT* and D* in complex situations
Case Studies and Practical Applications
- Path planning for autonomous delivery robots
- Implementations in self-driving vehicles and UAVs
- Project: Developing an adaptive path planner using RRT*
Requirements
- Strong proficiency in Python programming
- Practical experience with robotics systems and control algorithms
- Familiarity with autonomous vehicle technologies
Target Audience
- Robotics engineers focused on autonomous systems
- AI researchers specializing in path planning and navigation
- Advanced developers engaged in self-driving technology development
21 Hours
