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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/robotics-motion-planning
课程评论：没有评论
课程名称：机器人学：计算运动规划 概述：机器人系统通常包括三个组件：能够对环境施加力和扭矩的机械装置、用于感知世界的感知系统以及调节机器人行为以实现预期目标的决策和控制系统。本课程将探讨机器人如何决定采取何种行动以实现其目标的问题，这个问题通常被称为运动规划。我们将学习一些常见的方法来解决此问题，包括基于图的方法、随机规划器和人工势场等。 Throughout the course, we will discuss the aspects of the problem that make planning challenging. 课程大纲： 第1部分：介绍和基于图的规划方法 描述：欢迎来到第一周！在本模块中，我们将介绍通过网格规划路径的问题，其中机器人只能在离散的位置上移动。我们可以将这些情况建模为图，节点对应于网格位置，边对应于相邻网格单元之间的路径。我们将介绍几种可以用于在起始节点和目标节点之间规划路径的算法，包括广度优先搜索算法、Dijkstra算法和A星算法。 第2部分：配置空间 描述：欢迎来到第二周！在本模块中，我们将首先介绍配置空间的概念，这是一个数学工具，帮助我们思考机器人能够达到的所有位置的集合。接着，我们讨论配置空间障碍的概念，这些是机器人因障碍物或其他阻碍无法进入的配置空间区域。这个表达方式使我们能够从构建点在配置空间中轨迹的角度思考路径规划问题。我们还会描述几种方法，将连续的配置空间离散化为图，以便我们可以应用基于图的工具来解决运动规划问题。 第3部分：基于采样的规划方法 描述：欢迎来到第三周！在本模块中，我们介绍基于样本的路径规划技术。该技术涉及在配置空间中随机采样点，然后在相邻样本点之间形成无碰撞的边，从而形成一个捕捉机器人配置空间结构的图。我们将讨论概率路图（Probabilistic Road Maps）和快速随机树（RRT）及其在运动规划问题中的应用。 第4部分：人工势场方法 描述：欢迎来到课程的最后一周！另一种运动规划方法涉及构建人工势场，这些势场旨在将机器人吸引到目标配置，同时将其从配置空间障碍中排斥出去。机器人运动可以通过考虑该势函数的梯度来引导。在本模块中，我们将在一个简单的二维配置空间中演示这些技术。
Part: 1
Title:Introduction and Graph-based Plan Methods
Description: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.
Part: 2
Title:Configuration Space
Description: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 which are regions in configuration space that the robot cannot take on because of obstacles or other impediments. This formulation allows us to think about path planning problems in terms of constructing trajectories for a point through configuration space. We also describe a few approaches that can be used to discretize the continuous configuration space into graphs so that we can apply graph-based tools to solve our motion planning problems.
Part: 3
Title:Sampling-based Planning Methods
Description: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 captures the structure of the robots configuration space. We will talk about Probabilistic Road Maps and Randomly Exploring Rapid Trees (RRTs) and their application to motion planning problems.
Part: 4
Title:Artificial Potential Field Methods
Description: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 robot’s motion can then be guided by considering the gradient of this potential function. In this module we will illustrate these techniques in the context of a simple two dimensional configuration space.
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.
