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Course Outline
Foundations of Path Planning for Autonomous Vehicles
- Core concepts and key challenges in path planning
- Relevance to autonomous driving and robotics applications
- A review of both traditional and contemporary planning methods
Graph-Based Path Planning Algorithms
- Introduction to A* and Dijkstra’s algorithms
- Application of A* for grid-based navigation
- Dynamic adaptations: D* and D* Lite for shifting environments
Sampling-Based Path Planning Algorithms
- Random sampling methods: RRT and RRT*
- Techniques for path smoothing and optimization
- Addressing non-holonomic constraints
Optimization-Driven Path Planning
- Structuring path planning as an optimization challenge
- Trajectory refinement via nonlinear programming
- Utilizing gradient-based and gradient-free optimization approaches
Learning-Driven Path Planning
- Applying Deep Reinforcement Learning (DRL) for path optimization
- Blending DRL with conventional algorithms
- Adaptive path planning leveraging machine learning models
Navigating Dynamic and Uncertain Environments
- Reactive planning strategies for immediate real-time response
- Obstacle avoidance combined with predictive control
- Incorporating perception data for adaptive navigation
Assessment and Benchmarking of Path Planning Algorithms
- Key metrics for path efficiency, safety, and computational load
- Simulation and testing using ROS and Gazebo
- Case study: Performance comparison of RRT* and D* in complex situations
Practical Case Studies and Real-World Applications
- Path planning solutions for autonomous delivery robots
- Implementation 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
- Sound familiarity with autonomous vehicle technologies
Target Audience
- Robotics engineers with a focus on autonomous systems
- AI researchers specializing in path planning and navigation
- Senior developers engaged in self-driving technology development
21 Hours
