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Course Outline
Foundations of Path Planning for Autonomous Vehicles
- Core concepts and key challenges in path planning
- Relevance in autonomous driving and robotics
- Survey of classical and contemporary planning methodologies
Graph-Based Planning Approaches
- Introduction to A* and Dijkstra's algorithms
- Applying A* for grid-based route finding
- Dynamic adaptations: D* and D* Lite for evolving environments
Sampling-Based Planning Techniques
- Stochastic sampling methods: RRT and RRT*
- Strategies for path smoothing and optimization
- Managing non-holonomic constraints
Optimization-Driven Path Planning
- Modeling the planning challenge as an optimization task
- Trajectory refinement via nonlinear programming
- Exploration of gradient-based and gradient-free optimization methods
Learning-Enhanced Path Planning
- Utilizing Deep Reinforcement Learning (DRL) for route improvement
- Combining DRL with conventional algorithmic frameworks
- Adaptive planning strategies leveraging machine learning models
Navigation in Dynamic and Uncertain Conditions
- Reactive planning methods for immediate real-time adjustments
- Techniques for obstacle avoidance and predictive control
- Incorporating perception data to enable adaptive navigation
Performance Evaluation and Benchmarking
- Assessing path efficiency, safety, and computational load
- Simulation and testing within ROS and Gazebo
- Case analysis: Contrasting RRT* and D* in intricate scenarios
Real-World Applications and Case Studies
- Path planning for autonomous delivery robots
- Deployment in self-driving cars and UAVs
- Capstone project: Building an adaptive planner utilizing RRT*
Requirements
- Strong command of Python programming
- Practical experience with robotic systems and control logic
- Knowledge of autonomous vehicle architectures
Target Audience
- Robotics engineers focused on autonomous systems
- AI researchers specializing in navigation and path planning
- Senior developers contributing to self-driving technology
21 Hours
