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.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Robotik
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- Motion Planning
- Automated Planning And Scheduling
- A* Search Algorithm
- Matlab
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Introduction and Graph-based Plan Methods
Configuration Space
Sampling-based Planning Methods
Artificial Potential Field Methods
Bewertungen
- 5 stars55,30 %
- 4 stars26,95 %
- 3 stars10,50 %
- 2 stars3,86 %
- 1 star3,36 %
Top-Bewertungen von ROBOTICS: COMPUTATIONAL MOTION PLANNING
THE BEST COURSE IN THE FIELD OF ROBOTICS. VERY WELL EXPLAINED BY SIR.
I AM GRATEFUL AND FILLED WITH MUCH GRATITUDE TO BE THE PART OF THIS COURSE.
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.
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.
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