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. You will learn some of the most common approaches to addressing this problem including graph-based methods, randomized planners and artificial potential fields. Throughout the course, we will discuss the aspects of the problem that make planning challenging.
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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 Space
Welcome 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 Methods
Welcome 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 Methods
Welcome 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.
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4.2
ROBOTICS: COMPUTATIONAL MOTION PLANNING의 최상위 리뷰
The course was challenging, but fulfilling. Thank you Coursera and University of Pennsylvania for giving this wonderful experience and opportunity that I might not experience in our local community!
The topic was very interesting, and the assignments weren't overly complicated. Overall, the lesson was fun and informative , despite the bugs in the learning tool(especially, the last assignment.)
This course is supposed to be easier but somehow it also makes it difficult because implementations of the algorithms in Matlab are bit non-standard as I am used to. Altogether very challenging.
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.
The course material and videos are very good. Small bugs in the exercise can be a bit of headache. Luckily, digging the community forum there is always a high chance to solve your issue.
I like this course because they are covering really complicated topics in very less material. And the assignments are amazing. They are worth the learning effect they create.
The assignments had a lot of ambiguity. The course content was wonderful. It would have been nice if 3d computation problems were involved. Week 4 was really interesting !
A good course to get started with robotic motion planning. It starts from shortest path algorithm, configuration space to probabilistic roadmap and potential filed.
A beautiful course for robotics beginners. Videos are mostly conceptual yet superficial while programming assignments give a feel for the technicalities involved.
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.
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.
It's nice! I particularly like the programming homework. However, the course is pretty much beginner-level, and the course is too short (for only 4 weeks).
Great Introductory Course. It is a bit short on course content, but teaches you enough to be able to learn from other sources such as research papers, etc.
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.
it's a nice to learn a lot from the course. Some of the assignment is quite difficult. But with the discussion forum's help, I can pass all of them.
Good course..but I wish the course was longer and the lectures and quizzes more detailed. Looking to more courses on these topics.
Good Introduction to some of the Algorithms in Computational Planning . More of training in assignment than explanation in video
A great course ,this course will introduce you to the planning algorithms and will make you do some cool stuff as assignments.
Everything was so relevant and finely explained, but It clearly wasn't a beginner's course, it's more an Intermediate Course.
There should be more materials for completing the assignment problems. I find it Insufficient. The course overall is great!
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The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.
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