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Course Outline
Foundations of Path Planning for Autonomous Vehicles
- Core concepts and challenges in path planning
- Use cases in autonomous driving and robotics
- Overview of conventional and contemporary planning methods
Algorithms Based on Graph Structures
- Summary of A* and Dijkstra's algorithm
- Applying A* for grid-based navigation
- Dynamic adaptations: D* and D* Lite for fluctuating environments
Sampling-Driven Path Planning Methods
- Random sampling approaches: RRT and RRT*
- Smoothing paths and optimizing routes
- Managing non-holonomic restrictions
Path Planning via Optimization
- Defining path planning as an optimization challenge
- Trajectory refinement using nonlinear programming
- Techniques using both gradient-based and non-gradient-based methods
Machine Learning-Driven Path Planning
- Applying Deep Reinforcement Learning (DRL) to path optimization
- Merging DRL with classical algorithms
- Adaptive planning strategies utilizing ML models
Managing Dynamic and Uncertain Conditions
- Reactive planning for immediate real-time responses
- Obstacle evasion and predictive control systems
- Utilizing perception data for adaptive maneuvering
Performance Assessment and Benchmarking
- Measures for route efficiency, safety, and computational load
- Simulation and testing within ROS and Gazebo frameworks
- Case analysis: Contrast between RRT* and D* in intricate scenarios
Practical Case Studies and Industry Applications
- Navigation strategies for autonomous delivery bots
- Implementation in self-driving cars and UAVs
- Project task: Building an adaptive path planner with RRT*
Requirements
- Strong competency in Python programming
- Hands-on experience with robotic systems and control algorithms
- General knowledge of autonomous vehicle technologies
Target Audience
- Robotics engineers with a focus on autonomous systems
- AI researchers dedicated to navigation and path planning
- Senior developers engaged in self-driving technology projects
21 Hours
