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.
This course is part of the Robotics Specialization
About this Course
Skills you will gain
- Motion Planning
- Automated Planning And Scheduling
- A* Search Algorithm
Syllabus - What you will learn from this course
Introduction and Graph-based Plan Methods
Sampling-based Planning Methods
Artificial Potential Field Methods
- 5 stars55.30%
- 4 stars26.95%
- 3 stars10.50%
- 2 stars3.86%
- 1 star3.36%
TOP REVIEWS FROM ROBOTICS: COMPUTATIONAL MOTION PLANNING
Good course..but I wish the course was longer and the lectures and quizzes more detailed. Looking to more courses on these topics.
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 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 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).
About the Robotics Specialization
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