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Course Outline
Introduction to Path Planning for Autonomous Vehicles
- Fundamentals of path planning and associated challenges
- Applications in autonomous driving and robotics for government
- Review of traditional and modern planning techniques
Graph-Based Path Planning Algorithms
- Overview of A* and Dijkstra algorithms
- Implementing A* for grid-based pathfinding in public sector applications
- Dynamic variants: D* and D* Lite for changing environments in real-world scenarios
Sampling-Based Path Planning Algorithms
- Random sampling techniques: RRT and RRT*
- Path smoothing and optimization to enhance operational efficiency
- Handling non-holonomic constraints in government vehicles
Optimization-Based Path Planning
- Formulating the path planning problem as an optimization challenge for government use cases
- Trajectory optimization using nonlinear programming methods
- Gradient-based and gradient-free optimization techniques to improve performance
Learning-Based Path Planning
- Deep reinforcement learning (DRL) for path optimization in dynamic environments
- Integrating DRL with traditional algorithms for enhanced accuracy
- Adaptive path planning using machine learning models to support government operations
Handling Dynamic and Uncertain Environments
- Reactive planning techniques for real-time response in public sector applications
- Obstacle avoidance and predictive control to ensure safety and reliability
- Integrating perception data for adaptive navigation in government vehicles
Evaluating and Benchmarking Path Planning Algorithms
- Metrics for path efficiency, safety, and computational complexity in government contexts
- Simulating and testing in ROS and Gazebo to validate performance
- Case study: Comparing RRT* and D* in complex scenarios for government use
Case Studies and Real-World Applications
- Path planning for autonomous delivery robots in public sector settings
- Applications in self-driving cars and UAVs for government operations
- Project: Implementing an adaptive path planner using RRT* for government missions
Summary and Next Steps
Requirements
- Proficiency in Python programming for government applications
- Experience with robotics systems and control algorithms for government projects
- Familiarity with autonomous vehicle technologies for government use
Audience
- Robotics engineers specializing in autonomous systems for government initiatives
- AI researchers focusing on path planning and navigation for government solutions
- Advanced-level developers working on self-driving technology for government applications
21 Hours
