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Course Outline
Introduction to Path Planning for Autonomous Vehicles
- Core principles and challenges of path planning
- Use cases in autonomous driving and robotics
- Comparison of traditional and contemporary planning methods
Graph-Based Path Planning Algorithms
- Foundations of A* and Dijkstra’s algorithms
- Applying A* for grid-based pathfinding
- 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
- Addressing non-holonomic constraints
Optimization-Based Path Planning
- Modeling path planning as an optimization challenge
- Trajectory optimization via nonlinear programming
- Exploring gradient-based and gradient-free optimization strategies
Learning-Based Path Planning
- Deep reinforcement learning (DRL) for path optimization
- Combining DRL with conventional algorithms
- Adaptive path planning leveraging machine learning models
Managing Dynamic and Uncertain Environments
- Reactive planning techniques for real-time adaptation
- Strategies for obstacle avoidance and predictive control
- Incorporating perception data for adaptive navigation
Evaluation and Benchmarking of Path Planning Algorithms
- Key metrics: path efficiency, safety, and computational load
- Simulation and testing within ROS and Gazebo
- Case study: Comparative analysis of RRT* and D* in complex settings
Case Studies and Real-World Applications
- Path planning solutions for autonomous delivery robots
- Implementations 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
- Working knowledge of autonomous vehicle technologies
Target Audience
- Robotics engineers specializing in autonomous systems
- AI researchers dedicated to path planning and navigation
- Senior developers focused on self-driving technology
21 Hours
