Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Path Planning for Autonomous Vehicles
- Path planning fundamentals and challenges.
- Applications in autonomous driving and robotics.
- Review of traditional and modern planning techniques.
Graph-Based Path Planning Algorithms
- Overview of A* and Dijkstra algorithms.
- Implementing A* for grid-based pathfinding.
- Dynamic variants: D* and D* Lite for changing environments.
Sampling-Based Path Planning Algorithms
- Random sampling techniques: RRT and RRT*.
- Path smoothing and optimization.
- Handling non-holonomic constraints.
Optimization-Based Path Planning
- Formulating the path planning problem as an optimization problem.
- Trajectory optimization using nonlinear programming.
- Gradient-based and gradient-free optimization techniques.
Learning-Based Path Planning
- Deep reinforcement learning (DRL) for path optimization.
- Integrating DRL with traditional algorithms.
- Adaptive path planning using 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*.
Summary and Next Steps
Requirements
- Proficiency in Python programming.
- Experience with robotics systems and control algorithms.
- Familiarity with autonomous vehicle technologies.
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
