Enroll Course: https://www.coursera.org/learn/robotics-motion-planning
Robotics is a fascinating field that blends hardware, software, and a deep understanding of how machines interact with the physical world. A crucial aspect of any robotic system is its ability to navigate and move effectively, which is where motion planning comes in. I recently completed Coursera’s “Robotics: Computational Motion Planning” course, and it provided a comprehensive and insightful journey into this vital area.
The course is structured to build a strong foundation, starting with the basics of graph-based planning methods. Week 1 introduces fundamental concepts like representing movement on grids as graphs and explores algorithms such as Breadth-First Search, Dijkstra’s, and A* search. This initial module is excellent for those new to pathfinding, clearly explaining how these algorithms can find optimal routes between points.
As we progress to Week 2, the course delves into the more abstract but essential concept of Configuration Space. This is where the complexity of robot motion becomes apparent, as we consider the robot’s entire state (position and orientation) and how obstacles in the real world translate into forbidden regions in this space. The module effectively explains how to discretize this continuous space, making it amenable to the graph-based techniques learned in the previous week.
Week 3 introduces Sampling-based Planning Methods, a powerful set of techniques for tackling high-dimensional and complex configuration spaces. Here, we learn about Probabilistic Roadmaps (PRMs) and Rapidly-exploring Random Trees (RRTs). These methods involve strategically sampling the configuration space to build a roadmap that the robot can then use for planning. It’s an intuitive yet highly effective approach that is widely used in modern robotics.
Finally, Week 4 wraps up the course with Artificial Potential Field Methods. This approach uses a concept akin to physics, where a potential field guides the robot. The goal configuration exerts an attractive force, while obstacles create repulsive forces. By following the gradient of this combined field, the robot can navigate towards its objective. The module uses a simple 2D example to clearly illustrate the principles.
Overall, “Robotics: Computational Motion Planning” is an outstanding course for anyone interested in the practical aspects of robot movement. The instructors do a fantastic job of breaking down complex topics into digestible modules, supported by clear explanations and relevant examples. Whether you’re a student, a hobbyist, or a professional looking to enhance your robotics knowledge, this course is highly recommended. It equips you with the theoretical understanding and practical algorithms needed to tackle real-world motion planning challenges.
Enroll Course: https://www.coursera.org/learn/robotics-motion-planning