Robotics Computational Motion Planning
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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Syllabus
Syllabus - What you will learn from this course
Week 1
Introduction and Graph-based Plan Methods
Week 2
Configuration Space
Week 3
Sampling-based Planning Methods
Week 4
Artificial Potential Field Methods
FAQ
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
Reviews
Good Introduction to some of the Algorithms in Computational Planning . More of training in assignment than explanation in video
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.
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.
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.
