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Course Outline
Introduction to Path Planning for Autonomous Vehicles
- Core concepts and challenges in path planning.
- Applications in autonomous driving and robotics sectors.
- Review of traditional and contemporary planning methodologies.
Graph-Based Path Planning Algorithms
- Survey of A* and Dijkstra algorithms.
- Implementation of 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 processes.
- Management of non-holonomic constraints.
Optimization-Based Path Planning
- Defining the path planning problem as an optimization challenge.
- Trajectory optimization utilizing nonlinear programming.
- Gradient-based and gradient-free optimization methods.
Learning-Based Path Planning
- Employing deep reinforcement learning (DRL) for path optimization.
- Combining DRL with conventional algorithms.
- Adaptive path planning through machine learning models.
Handling Dynamic and Uncertain Environments
- Reactive planning techniques for immediate responses.
- Obstacle avoidance and predictive control strategies.
- Integration of perception data for adaptive navigation.
Evaluating and Benchmarking Path Planning Algorithms
- Key metrics for path efficiency, safety, and computational complexity.
- Simulation and testing protocols within ROS and Gazebo.
- Case study: Comparative analysis of 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: Developing an adaptive path planner using RRT*.
Summary and Next Steps
Requirements
- Strong proficiency in Python programming.
- Prior experience with robotics systems and control algorithms.
- Familiarity with technologies pertaining to autonomous vehicles.
Target Audience
- Robotics engineers specializing in autonomous systems.
- AI researchers dedicated to path planning and navigation.
- Developers at an advanced level working on self-driving technologies.
21 Hours
