UPenn - Robotics: Computational Motion Planning
- Offered byCoursera
Robotics: Computational Motion Planning at Coursera Overview
Duration | 11 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Robotics: Computational Motion Planning at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 2 of 6 in the Robotics Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Approx. 11 hours to complete
- English Subtitles: English, Spanish
Robotics: Computational Motion Planning at Coursera Course details
- 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.
Robotics: Computational Motion Planning at Coursera Curriculum
Introduction and Graph-based Plan Methods
1.1: Introduction to Computational Motion Planning
1.2: Grassfire Algorithm
1.3: Dijkstra's Algorithm
1.4: A* Algorithm
Getting Started with the Programming Assignments
Computational Motion Planning Honor Code
Getting Started with MATLAB
Resources for Computational Motion Planning
Graded MATLAB Assignments
Graph-based Planning Methods
Configuration Space
2.1: Introduction to Configuration Space
2.2: RR arm
2.3: Piano Mover?s Problem
2.4: Visibility Graph
2.5: Trapezoidal Decomposition
2.6: Collision Detection and Freespace Sampling Methods
Configuration Space
Sampling-based Planning Methods
3.1: Introduction to Probabilistic Road Maps
3.2: Issues with Probabilistic Road Maps
3.3: Introduction to Rapidly Exploring Random Trees
Sampling-based Methods
Artificial Potential Field Methods
4.1: Constructing Artificial Potential Fields
4.2: Issues with Local Minima
4.3: Generalizing Potential Fields
4.4: Course Summary
Artificial Potential Fields
Robotics: Computational Motion Planning at Coursera Admission Process
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