Go to Course: https://www.coursera.org/learn/robotics-motion-planning
**Course Review: Robotics: Computational Motion Planning on Coursera** **Overview** If you have ever been intrigued by how robots navigate through complex environments or achieve specific tasks, then the "Robotics: Computational Motion Planning" course offered on Coursera is a must-enroll. This course delves into the intricacies of motion planning, a critical component that allows robotic systems to make decisions about their movement in real-time, ensuring optimal interaction with the environment. The course is structured to introduce fundamental concepts and advanced techniques essential for understanding and implementing computational motion planning in robotics. **Course Content** The course spans four weeks, each focusing on different methodologies for motion planning, making it suitable for both beginners and those with some background in robotics or computer science. 1. **Introduction and Graph-based Path Methods** The first week sets the stage for understanding motion planning by introducing graph-based methods. Here, learners will explore how to model the robot's environment as a graph, with nodes representing positions and edges indicating possible paths. Key algorithms such as the breadth-first search, Dijkstra's algorithm, and the A* procedure are covered. This foundational knowledge is invaluable, as it equips students with essential strategies for determining optimal paths in grid-based environments. 2. **Configuration Space** In the second week, the course delves into the concept of configuration space, a vital mathematical tool in robotics. Learners will uncover how to represent all possible positions the robot can occupy while accounting for obstacles. The week includes discussions on configuration space obstacles and techniques to discretize continuous spaces into graphs, enabling the application of graph-based algorithms in more complex environments. This week is particularly rewarding for anyone interested in theoretical underpinnings of motion planning. 3. **Sampling-based Planning Methods** The third week focuses on innovative sampling-based techniques that expand the practical applications of motion planning. Here, students learn to randomly sample points in configuration space to create a structure from which collision-free paths can be derived. The highlight of this module is the exploration of Probabilistic Road Maps and Rapidly Exploring Random Trees (RRTs), both of which are pivotal in more uncertain scenarios, such as robotic navigation in real-world environments. 4. **Artificial Potential Field Methods** The final week introduces the concept of artificial potential fields—an intuitive approach that visualizes how robots can be guided towards goals while avoiding obstacles. The week culminates with practical demonstrations set within a simplified two-dimensional configuration space, solidifying learners' understanding of how theoretical principles can be effectively applied in practice. **Recommendation** "Robotics: Computational Motion Planning" is an outstanding course for anyone looking to deepen their understanding of robotic systems and motion planning. The blend of theoretical concepts and practical methodologies, coupled with accessible explanations, makes complex ideas comprehensible. Whether you are a student, a professional in robotics, or simply an enthusiast wanting to break into the field, this course provides a wealth of knowledge and the necessary skills to approach problems in motion planning. One of the key strengths of this course is its structure. Each week builds upon the previous one, ensuring a comprehensive learning experience that maintains continuity and clarity. The blend of learning styles—reading, visuals, and hands-on coding exercises—keeps engagement high and caters to different learners. In conclusion, I highly recommend the "Robotics: Computational Motion Planning" course on Coursera. It not only serves as an excellent introduction to the world of robotics but also offers insights into advanced concepts that can be applied in real-world scenarios. Whether you're seeking to enhance your resume or explore a new hobby, this course is a definite step in the right direction. Enroll today and embark on your journey into the fascinating world of robotics!
Introduction and Graph-based Plan Methods
Welcome to Week 1! In this module, we will introduce the problem of planning routes through grids where the robot can only take on discrete positions. We can model these situations as graphs where the nodes correspond to the grid locations and the edges to routes between adjacent grid cells. We present a few algorithms that can be used to plan paths between a start node and a goal node including the breadth first search or grassfire algorithm, Dijkstra’s algorithm and the A Star procedure.
Configuration SpaceWelcome to Week 2! In this module, we begin by introducing the concept of configuration space which is a mathematical tool that we use to think about the set of positions that our robot can attain. We then discuss the notion of configuration space obstacles which are regions in configuration space that the robot cannot take on because of obstacles or other impediments. This formulation allows us to think about path planning problems in terms of constructing trajectories for a point through configuration space. We also describe a few approaches that can be used to discretize the continuous configuration space into graphs so that we can apply graph-based tools to solve our motion planning problems.
Sampling-based Planning MethodsWelcome to Week 3! In this module, we introduce the concept of sample-based path planning techniques. These involve sampling points randomly in the configuration space and then forging collision free edges between neighboring sample points to form a graph that captures the structure of the robots configuration space. We will talk about Probabilistic Road Maps and Randomly Exploring Rapid Trees (RRTs) and their application to motion planning problems.
Artificial Potential Field MethodsWelcome to Week 4, the last week of the course! Another approach to motion planning involves constructing artificial potential fields which are designed to attract the robot to the desired goal configuration and repel it from configuration space obstacles. The robot’s motion can then be guided by considering the gradient of this potential function. In this module we will illustrate these techniques in the context of a simple two dimensional configuration space.
Robotic systems typically include three components: a mechanism which is capable of exerting forces and torques on the environment, a perception system for sensing the world and a decision and control system which modulates the robot's behavior to achieve the desired ends. In this course we will consider the problem of how a robot decides what to do to achieve its goals. This problem is often referred to as Motion Planning and it has been formulated in various ways to model different situations
Good course, completely satisfied. Could have used a bit more material though. For example more tie-ins to the first course would not have been difficult.
I would say beginners will struggle with the contents of this course because it is heavily conceptual and requires you to read other resources to understand fully, In all, it was a splendid course.
The last assignment had no hints. Also was extremely fragile with the grading. Step size cannot be fixed to a value, because otherwise the route count is wrong.
A very good course in motion planning. Here are introduced some basic approach to motion and the Matlab assignments are very helpful to understand the topics of the course. Absolutely suggested.
I enjoyed this course, and learnt a lot. The only reason I'm not giving it 5/5 is that the programming assignments were a bit too easy, especially the last two.