Advanced Path Planning Algorithms for Autonomous Vehicles

• 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

Advanced Path Planning Algorithms for Autonomous Vehicles is a specialized course focusing on cutting-edge techniques for efficient and safe navigation in dynamic environments.

This instructor-led, live training (online or onsite) is aimed at advanced-level robotics engineers and AI researchers who wish to implement sophisticated path planning algorithms to enhance autonomous vehicle performance.

By the end of this training, participants will be able to:

• Understand the theoretical foundations of advanced path planning algorithms.
• Implement algorithms such as RRT*, A*, and D* for real-time navigation.
• Optimize path planning for obstacle avoidance and dynamic environments.
• Integrate path planning algorithms with sensor data for enhanced accuracy.
• Evaluate the performance of various algorithms in practical scenarios.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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