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$49

Robotics: Computational Motion Planning

Created by -

CJ Taylor
,
University of Pennsylvania

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English

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Overview

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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USD 49

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Type: Online

This course includes

  • Approx. 11 hours to complete
  • Earn a Certificate upon completion
  • Start instantly and learn at your own schedule.

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course image

USD 49

provider image

Type: Online

This course includes

  • Approx. 11 hours to complete
  • Earn a Certificate upon completion
  • Start instantly and learn at your own schedule.

Taken this course?

Share your experience with other students

Share

Add Review

Robotics: Computational Motion Planning

Created by -

CJ Taylor
,
University of Pennsylvania

0.00

(0 ratings)

All Levels

Start Date: February 10th 2021

Course Description

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.

The information used on this page is how each course is described on the Coursera platform.

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CJ Taylor,University of Pennsylvania

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