2.1: Introduction to Configuration Space

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来自 University of Pennsylvania 的课程

Robotics: Computational Motion Planning

635 个评分

University of Pennsylvania

Robotics: Computational Motion Planning

635 个评分

课程 2（共 6 门，Specialization Robotics）

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.

从本节课中

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 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.

2.1: Introduction to Configuration Space4:01

2.2: RR arm2:17

2.3: Piano Mover’s Problem3:03

与讲师见面

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

[MUSIC]

In the motion planning problems we've considered so far,

we've basically reduced the problem to planning on a graph, where the robot can

take on various discrete positions, which we can enumerate and connect by edges.

Now in the real world,

most of the robots we are going to build can move continuously through space.

Configuration space is a handy mathematical and conceptual tool, which

was developed to help us think about these kinds of problems in a unified framework.

Basically, the configuration space of a robot

is the set of all configurations and/or positions that the robot can attain.

This slide shows a simple example of a robot

that can translate freely in the plane.

Here we can quantify the positions that the robot can take on with a tuple

composed of two numbers, tx and ty, which denote the coordinates of a particular

reference point on the robot, with respect to a fixed coordinate frame of reference.

Here are a couple of configurations that this translating robot can take on,

along with the associated coordinates.

In this case, we would say that our robot has 2 degrees of freedom, and

we can associate the configuration space of the robot with the points on

the 2D plane, namely these tx, ty coordinates.

Now we'll make the story little bit more interesting

by introducing fixed obstacles into our model.

What these obstacles do is make certain configurations in the configuration space

unattainable.

This figure shows the tx,

ty configurations that the robot cannot attain because of the obstacle.

This set of configurations that the robot cannot inhabit

Is referred to as a configuration space obstacle.

Conversely, the region of configuration space that the robot can attain

is referred to as the free space of the robot.

On the right-hand side of this figure, we plot the configuration space obstacle,

corresponding to the geometric obstacle shown in the left side of the figure.

Again, the configuration space obstacle denotes the set of configurations that

the robot cannot attain because of collision with the obstacle.

Note that the dimensions and shape of the configuration space obstacle

are obtained by considering both the obstacle and the shape of the robot.

More formally, in this case, the configuration space obstacle is defined by

what's known as the Minkowski sum of the obstacle and the robot shape.

If we have multiple obstacles in space, we can visualize the union of

all of the configuration space obstacles, and we get a picture like this.

Again, the configuration of the robot corresponds to a point

in the configuration space.

And the dark areas correspond to configurations that the robot cannot

attain.

In this setting, the task of planning a path for

our robot corresponds to planning a trajectory through configuration space

from the starting configuration to the ending configuration.

Here we are showing the motion of the robot

through the space that avoids the obstacles, and

the corresponding motion of the robot's coordinates in configuration space.

Note that by thinking about this problem in configuration space,

we are now just planning the path for a point through configuration space,

avoiding the configuration space obstacles.

All of the geometry of the robot and

the obstacles are captured by the configuration space obstacles.

This is really the beauty of formulating things in configuration space.

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