Robotics: Computational Motion Planning

Robotics: Computational Motion Planning

课程
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英语
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12 时
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  • 来自www.coursera.org
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  • 4 序列
  • 等级 介绍
  • 字幕在 Spanish

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课程详情

教学大纲

  • Week 1 - Introduction and Graph-based Plan Methods
    Welcome to Week 1! In this module, we will introduce the problem of planning routes through grids where the robot can only take on discrete positions. We can model these situations as graphs where the nodes correspond to the grid locations and the edges to rou...
  • Week 2 - Configuration Space
    Welcome to Week 2! In this module, we begin by introducing the concept of configuration space which is a mathematical tool that we use to think about the set of positions that our robot can attain. We then discuss the notion of configuration space obstacles wh...
  • Week 3 - Sampling-based Planning Methods
    Welcome to Week 3! In this module, we introduce the concept of sample-based path planning techniques. These involve sampling points randomly in the configuration space and then forging collision free edges between neighboring sample points to form a graph that...
  • Week 4 - Artificial Potential Field Methods
    Welcome to Week 4, the last week of the course! Another approach to motion planning involves constructing artificial potential fields which are designed to attract the robot to the desired goal configuration and repel it from configuration space obstacles. The...

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讲师

CJ Taylor
Professor of Computer and Information Science
School of Engineering and Applied Science

编辑

宾夕法尼亚大学(俗称宾大)成立于 1740 年,是一所位于美国宾夕法尼亚州费城的私立大学。作为常春藤联盟的成员,宾大是美国第四古老的高等学府,也是美国第一所同时提供本科和研究生学位的大学。

平台

Coursera是一家数字公司,提供由位于加利福尼亚州山景城的计算机教师Andrew Ng和达芙妮科勒斯坦福大学创建的大型开放式在线课程。

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