Robotics: Computational Motion Planning

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课程主页: https://www.coursera.org/archive/robotics-motion-planning

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课程简介

University of Pennsylvania

课程大纲

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 routes between adjacent grid cells. We present a few algorithms that can be used to plan paths between a start node and a goal node including the breadth first search or grassfire algorithm, Dijkstra’s algorithm and the A Star procedure.

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课程详情

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.

机器人技术:计算运动计划:机器人系统通常包括三个组件:能够在环境上施加力和扭矩的机构,感知世界的感知系统以及调节机器人行为以达到预期目的的决策和控制系统。 。在本课程中,我们将考虑机器人如何决定如何实现目标的问题。这个问题通常被称为运动计划,并且已经以各种方式对不同的情况进行建模。您将学习一些解决此问题的最常用方法,包括基于图的方法,随机规划器和人工势场。在整个课程中,我们将讨论使计划具有挑战性的问题的各个方面。

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