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Course Outline
Foundations of Path Planning in Autonomous Vehicles
- Core concepts and key challenges in path planning
- Use cases in autonomous driving and robotics
- Analysis of conventional and contemporary planning methods
Graph-Based Path Planning Techniques
- Introduction to A* and Dijkstra’s algorithms
- Application of A* for grid-based pathfinding
- Dynamic adaptations: D* and D* Lite for evolving environments
Sampling-Based Path Planning Techniques
- Random sampling methods: RRT and RRT*
- Techniques for path smoothing and refinement
- Managing non-holonomic constraints
Optimization-Driven Path Planning
- Defining path planning as an optimization challenge
- Optimizing trajectories via nonlinear programming
- Methods for gradient-based and gradient-free optimization
Learning-Based Path Planning
- Utilizing Deep Reinforcement Learning (DRL) for path optimization
- Blending DRL with established algorithms
- Adaptive path planning powered by machine learning models
Navigating Dynamic and Uncertain Conditions
- Reactive planning strategies for immediate response
- Techniques for obstacle avoidance and predictive control
- Integrating perception data for adaptive navigation
Assessment and Benchmarking of Path Planning Algorithms
- Key metrics for path efficiency, safety, and computational load
- Simulation and testing using ROS and Gazebo
- Case study: Contrast between RRT* and D* in complex situations
Practical Examples and Industry Applications
- Path planning solutions for autonomous delivery bots
- Implementation in self-driving cars and UAVs
- Capstone project: Building an adaptive path planner using RRT*
Requirements
- Strong command of Python programming
- Practical experience with robotic systems and control logic
- Knowledge of autonomous vehicle technologies
Target Audience
- Robotics engineers specializing in autonomous systems
- AI researchers concentrating on path planning and navigation
- Senior developers working on self-driving technology
21 Hours
