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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
- Comparison of traditional versus modern planning methods
Graph-Based Path Planning Approaches
- Introduction to A* and Dijkstra's algorithm
- Applying A* for grid-based navigation
- Dynamic adaptations: Utilizing D* and D* Lite for evolving environments
Sampling-Based Path Planning Strategies
- Random sampling methods: RRT and RRT*
- Techniques for path smoothing and optimization
- Addressing non-holonomic constraints
Optimization-Driven Path Planning
- Modeling path planning as an optimization challenge
- Performing trajectory optimization via nonlinear programming
- Exploring gradient-based and gradient-free optimization methods
Learning-Based Path Planning Solutions
- Using Deep Reinforcement Learning (DRL) for path enhancement
- Fusion of DRL with conventional algorithms
- Developing adaptive planning through machine learning models
Navigating Dynamic and Uncertain Contexts
- Reactive planning methods for immediate response
- Strategies for obstacle avoidance and predictive control
- Incorporating perception data for adaptive maneuvering
Assessment and Benchmarking of Path Planning Algorithms
- Defining metrics for path efficiency, safety, and computational load
- Simulation and testing within ROS and Gazebo
- Case study: Contrasting RRT* and D* in complex situations
Real-World Implementations and Case Studies
- Path planning solutions for autonomous delivery robots
- Applications in self-driving vehicles and UAVs
- Project: Building an adaptive path planner using RRT*
Requirements
- Strong proficiency in Python programming
- Practical experience with robotic 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 challenges
- Senior developers involved in self-driving technology projects
21 Hours
