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Duration 21 hours
Course Outline
Introduction to Path Planning for Autonomous Vehicles
- Core concepts and challenges in path planning
- Applications in autonomous driving and robotics
- Overview of traditional versus modern planning techniques
Graph-Based Path Planning Algorithms
- Overview of A* and Dijkstra 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*
- Path smoothing and optimization strategies
- Addressing non-holonomic constraints
Optimization-Based Path Planning
- Defining path planning as an optimization challenge
- Trajectory optimization via nonlinear programming
- Utilizing gradient-based and gradient-free optimization methods
Learning-Based Path Planning
- Using Deep Reinforcement Learning (DRL) for path optimization
- Merging DRL with conventional algorithms
- Adaptive path planning through machine learning models
Managing Dynamic and Uncertain Environments
- Reactive planning strategies for immediate response
- Obstacle avoidance and predictive control
- Integrating perception data for adaptive navigation
Evaluating and Benchmarking Path Planning Algorithms
- Measures for path efficiency, safety, and computational load
- Simulation and testing within ROS and Gazebo
- Case study: Contrasting RRT* and D* in complex situations
Case Studies and Real-World Applications
- Path planning for autonomous delivery robots
- Uses in self-driving cars and UAVs
- Project: Creating an adaptive path planner with RRT*
Requirements
- Strong proficiency in Python programming
- Practical experience with robotics systems and control algorithms
- Knowledge of autonomous vehicle technologies
Target Audience
- Robotics engineers specializing in autonomous systems
- AI researchers focused on path planning and navigation
- Senior developers working on self-driving technology
