# Configuration Space

A robot is mechanically constructed by connecting a set of bodies, called links, to each other using various types of joints.

We describe the position of these links and joints using the configuration space.

Definition 2.1. The configuration of a robot is a complete specification of the position of every point of the robot. The minimum number of real-valued coordinates needed to represent the configuration is the number of degrees of freedom (dof) of the robot. The -dimensional space containing all possible configurations of the robot is called the configuration space (C-space). The configuration of a robot is represented by a point in its C-space.

### Configuration Space Topology

The shape of the configuration space is also important. We call this study its topology. While both a plane and the surface of a sphere are two dimensional, clearly they do not have the same shape â€“ the plane extends infinitely while the sphere wraps around.

### Configuration Space Representation

To perform computations, we must have a numerical representation of the space, consisting of a set of real numbers.

For Euclidean Space, denoted , this is straightforward, choose a vector to represent points.

However, what about the others? Like a sphere. We distinguish two ways to represent our configuration space.

#### Explicit Parametrization

Use coordinates, or parameters, to represent an -dimensional space.

This, sometimes, can be hard to implement. There are also problems like running into singularities. Imagine a sphere. The north and south poles are singularities of the representation, since we use coordinates [-180, 180] for example.

Why is this a problem? As the time rate of change of coordinates, since these representations may tend to infinity near singularities even if the point on the sphere is moving at a constant speed. To address this problem, we look at 2 solutions.

• Use more than one coordinate chart on the space. As the configuration representation approaches a singularity in one chart, e.g., the North or South Pole, you simply switch to another chart where the North and South Poles are far from singularities The other is Implicit representation.

#### Implicit representation

this is what is used in the book. Implicit representation views the -dimensional space as embedded in a Euclidean space of more than dimensions.

• No singularities in the representation â€“ a point moving smoothly around the sphere is represented by a smoothly changing , even at the North and South poles

• The representation has more numbers than the number of degrees of freedom

### Configuration and Velocity Constraints

As we said, itâ€™s easier to implement an implicit representation.

Holonomic constraints: Pfaffian constraints:

holonomic constraints of the form are also called integrable constraints

A Pfaffian constraint that is nonintegrable is called a nonholonomic constraint