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Course Outline
Introduction to Path Planning for Autonomous Vehicles
- Core principles of path planning and associated challenges
- Applications in autonomous driving and robotics
- Review of both traditional and modern planning methods
Graph-Based Path Planning Algorithms
- Overview of A* and Dijkstra algorithms
- Implementation of A* for grid-based pathfinding
- Dynamic variants: D* and D* Lite for shifting environments
Sampling-Based Path Planning Algorithms
- Random sampling techniques: RRT and RRT*
- Path smoothing and optimization
- Addressing non-holonomic constraints
Optimization-Based Path Planning
- Defining the path planning challenge as an optimization problem
- Trajectory optimization using nonlinear programming
- Gradient-based and gradient-free optimization methods
Learning-Based Path Planning
- Deep reinforcement learning (DRL) for path optimization
- Merging DRL with conventional algorithms
- Adaptive path planning utilizing machine learning models
Handling Dynamic and Uncertain Environments
- Reactive planning techniques for real-time response
- Obstacle avoidance and predictive control
- Integrating perception data for adaptive navigation
Evaluating and Benchmarking Path Planning Algorithms
- Metrics for path efficiency, safety, and computational complexity
- Simulating and testing in ROS and Gazebo
- Case study: Comparing RRT* and D* in complex scenarios
Case Studies and Real-World Applications
- Path planning for autonomous delivery robots
- Applications in self-driving cars and UAVs
- Project: Implementing an adaptive path planner using RRT*
Requirements
- Strong proficiency in Python programming
- Practical experience with robotics systems and control algorithms
- Familiarity with autonomous vehicle technologies
Target Audience
- Robotics engineers specializing in autonomous systems
- AI researchers focusing on path planning and navigation
- Advanced-level developers working on self-driving technology
21 Hours
