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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About this Course
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Try Coursera for BusinessSkills you will gain
- Motion Planning
- Automated Planning And Scheduling
- A* Search Algorithm
- Matlab
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Syllabus - What you will learn from this course
Introduction and Graph-based Plan Methods
Configuration Space
Sampling-based Planning Methods
Artificial Potential Field Methods
Reviews
- 5 stars55.30%
- 4 stars26.95%
- 3 stars10.50%
- 2 stars3.86%
- 1 star3.36%
TOP REVIEWS FROM ROBOTICS: COMPUTATIONAL MOTION PLANNING
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 !
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.
Good Introduction to some of the Algorithms in Computational Planning . More of training in assignment than explanation in video
About the Robotics Specialization

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