Autonomous Robots: Nonholonomic Motion Planning Algorithms

🤖 Autonomous Robots: Nonholonomic Motion Planning Algorithm Course

Course Headline:

Master Nonholonomic Vehicle Motion Planning with Cutting-Edge Algorithms!

Introduction: 📚

Important Note: This course serves as a continuation to my free course titled Introduction to Sampling Based Motion Planning Algorithms. While completion of the free course is beneficial, it's not mandatory for this one, as all necessary concepts from the free course are thoroughly covered within this paid course.

Course Highlights: ✨

• Affine Transformations & Vector Rotation Matrices: Understand and apply these to solve real-world problems.
• Dubins Path Derivation: Learn to calculate smooth trajectories between two points on a map, taking into account the vehicle's constraints.
• Integration of Dubins Path with RRT & RRT Algorithms*: Combine trajectory generation with advanced motion planning algorithms for optimal pathfinding.
• Interactive Assignments: Apply your knowledge with hands-on tasks that will solidify your understanding of the concepts taught.
  1. Affine Transformations and Rotation Matrices
  2. Derivation of Dubins Path
  3. RRT & RRT* with Dubins Path
  4. Incremental RRT with Dubins Path for real-world scenarios 🛣️

Course Description: 🧮

Motion planning is a critical aspect of robotics and autonomous vehicles, ensuring safe and efficient navigation through environments. This course delves into the world of nonholonomic motion planning, which is essential for understanding and developing algorithms for vehicles with limited degrees of freedom. 🚗🛫✈️

• Nonholonomic Vehicles: Explore how vehicles like cars, boats, and drones move within a given space and the constraints they face.
• Speed Constraints & Dubins Path: Learn to generate smooth trajectories for vehicles that adhere to their speed limitations and navigate through complex environments.
• Implementing Motion Planning Algorithms: Combine the Dubins Path with RRT and RRT* algorithms to solve motion planning problems.

What You Will Learn: 📍

1. Dubins Path: Derive the shortest path that a nonholonomic vehicle can take between two points while adhering to its constraints.
2. *Motion Planning Algorithms (RRT & RRT)**: Understand and apply these algorithms in conjunction with the Dubins Path for robust pathfinding.
3. Interactive Assignments: Test your skills through real-world scenarios, enhancing your ability to implement motion planning algorithms.
4. For the first assignment, you will use Python, Numpy, and Matplotlib to derive the Dubins Path between two points.
5. In the following assignments, you will integrate the Dubins Path with RRT and RRT* motion planning algorithms to find paths that avoid obstacles while respecting the vehicle's kinematic constraints.
6. The final assignment involves using an incremental version of RRT with Dubins Path to navigate a vehicle in a realistic roadmap scenario, where the vehicle has limited information available.

Course Requirements: 🛠️

• Python: Familiarity with Python programming language is essential for completing the course assignments.
• Numpy & Matplotlib: Basic understanding of Numpy for numerical operations and Matplotlib for visualizing data.
• Mathematical Foundations: A grasp of scalars, vectors, and their applications in robotics. You can also adapt these concepts to another programming language, but you will be responsible for translating the assignments into that language on your own.

Embark on a journey to master nonholonomic motion planning for autonomous vehicles with this comprehensive online course. Enhance your skills in robotics and position yourself at the forefront of autonomous navigation technology. 🚀🔧