Module #1 Introduction to Path Planning and Obstacle Avoidance Overview of the importance of path planning and obstacle avoidance in robotics and autonomous systems
Module #2 Fundamentals of Robotics and Motion Planning Review of robotics basics, including robot types, configuration spaces, and motion planning concepts
Module #3 Types of Motion Planning Overview of different types of motion planning, including trajectory planning, path planning, and task planning
Module #4 Path Planning Algorithms Introduction to path planning algorithms, including graph-based, sampling-based, and optimization-based methods
Module #5 Graph-Based Path Planning In-depth look at graph-based path planning algorithms, including Dijkstras algorithm and A* algorithm
Module #6 Sampling-Based Path Planning In-depth look at sampling-based path planning algorithms, including PRM and RRT
Module #7 Optimization-Based Path Planning In-depth look at optimization-based path planning algorithms, including linear and nonlinear programming
Module #8 Obstacle Avoidance Fundamentals Overview of obstacle avoidance concepts, including sensor types and obstacle detection methods
Module #9 Sensor Types for Obstacle Avoidance In-depth look at various sensor types used for obstacle avoidance, including lidar, radar, and vision sensors
Module #10 Obstacle Detection Methods In-depth look at obstacle detection methods, including thresholding, edge detection, and machine learning-based approaches
Module #11 Motion Planning with Obstacles Introduction to motion planning with obstacles, including obstacle avoidance and motion planning algorithms
Module #12 Vector Field Methods for Obstacle Avoidance In-depth look at vector field methods for obstacle avoidance, including potential fields and velocity obstacles
Module #13 Sampling-Based Methods for Obstacle Avoidance In-depth look at sampling-based methods for obstacle avoidance, including RRT and PRM with obstacles
Module #14 Reactive Methods for Obstacle Avoidance In-depth look at reactive methods for obstacle avoidance, including fuzzy logic and neural networks
Module #15 Motion Planning under Uncertainty Introduction to motion planning under uncertainty, including probabilistic motion planning and uncertainty propagation
Module #16 Motion Planning with Dynamic Obstacles In-depth look at motion planning with dynamic obstacles, including moving obstacles and uncertain environments
Module #17 Human-Robot Interaction and Obstacle Avoidance Introduction to human-robot interaction and obstacle avoidance, including human-aware motion planning
Module #18 Case Studies in Path Planning and Obstacle Avoidance Real-world examples and case studies of path planning and obstacle avoidance in robotics and autonomous systems
Module #19 Advanced Topics in Path Planning and Obstacle Avoidance Introduction to advanced topics, including multi-robot motion planning and motion planning for non-holonomic systems
Module #20 Path Planning and Obstacle Avoidance in Specific Domains In-depth look at path planning and obstacle avoidance in specific domains, including aerial, ground, and underwater robotics
Module #21 Simulation and Visualization Tools for Path Planning and Obstacle Avoidance Overview of simulation and visualization tools for path planning and obstacle avoidance, including Gazebo and V-REP
Module #22 Motion Planning and Obstacle Avoidance in Real-World Applications Real-world applications of path planning and obstacle avoidance, including autonomous vehicles, robots, and drones
Module #23 Evaluating and Comparing Path Planning and Obstacle Avoidance Algorithms Introduction to evaluating and comparing path planning and obstacle avoidance algorithms, including metrics and benchmarks
Module #24 Challenges and Limitations of Path Planning and Obstacle Avoidance Discussion of challenges and limitations of path planning and obstacle avoidance, including computational complexity and sensor uncertainty
Module #25 Future Directions and Emerging Trends in Path Planning and Obstacle Avoidance Overview of future directions and emerging trends in path planning and obstacle avoidance, including machine learning and swarm intelligence
Module #26 Course Wrap-Up and Final Project Summary of key concepts and final project assignment for implementing path planning and obstacle avoidance algorithms
Module #27 Appendix A:Mathematical Preliminaries Review of mathematical concepts relevant to path planning and obstacle avoidance, including linear algebra and probability theory
Module #28 Appendix B:Robot Operating System (ROS) Basics Introduction to ROS and its relevance to path planning and obstacle avoidance
Module #29 Appendix C:Additional Resources and References List of additional resources and references for further learning and research in path planning and obstacle avoidance
Module #30 Course Wrap-Up & Conclusion Planning next steps in Path Planning and Obstacle Avoidance career
