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.
2.4: Visibility Graph
Habilidades que você aprenderá
Motion Planning, Automated Planning And Scheduling, A* Search Algorithm, Matlab
Avaliações
I like this course because they are covering really complicated topics in very less material. And the assignments are amazing. They are worth the learning effect they create.
A good course to get started with robotic motion planning. It starts from shortest path algorithm, configuration space to probabilistic roadmap and potential filed.
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.
Ministrado por
CJ Taylor
Professor of Computer and Information Science
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