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
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 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.
Good Introduction to some of the Algorithms in Computational Planning . More of training in assignment than explanation in video
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.
About the Robotics Specialization
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