# Robotics Kinematics and Dynamics/Description of Position and Orientation

In general, a rigid body in three-dimensional space has **six degrees of freedom**: three rotational and three translational.

A conventional way to describe the position and orientation of a rigid body is to attach a frame to it. After defining a reference coordinate system, the position and orientation of the rigid body are fully described by the position of the frame's origin and the orientation of its axes, relative to the reference frame.

## Rotation Matrix[edit | edit source]

A **rotation matrix** describes the relative orientation of two such frames. The columns of this 3 × 3 matrix consist of the unit vectors along the axes of one frame, relative to the other, reference frame. Thus, the relative orientation of a frame with respect to a reference frame is given by the rotation matrix :

Rotation matrices can be **interpreted in two ways**:

- As the representation of the
**rotation**of the first frame into the second (**active**interpretation). - As the representation of the mutual
**orientation**between two coordinate systems (**passive**interpretation).

The coordinates, relative to the reference frame , of a point , of which the coordinates are known with respect to a frame with the same origin, can then be calculated as follows: .

### Properties[edit | edit source]

Some of the properties of the rotation matrix that may be of practical value, are:

- The column vectors of are normal to each other.
- The length of the column vectors of equals 1.
- A rotation matrix is a non-minimal description of a rigid body's orientation. That is, it uses nine numbers to represent an orientation instead of just three. (The two above properties correspond to six relations between the nine matrix elements. Hence, only three of them are independent.) Non-minimal representations often have some numerical advantages, though, as they do not exhibit coordinate singularities.
- Since is orthonormal, .

### Elementary Rotations about Frame Axes[edit | edit source]

The expressions for elementary rotations about frame axes can easily be derived. From the figure on the right, it can be seen that the rotation of a frame by an angle about the z-axis, is described by:

Similarly, it can be shown that the rotation of a frame by an angle about the x-axis, is given by:

Derived in exactly the same manner, the rotation of a frame by an angle about the y-axis, is described by:

### Compound Rotations[edit | edit source]

Compound rotations are found by multiplication of the different elementary rotation matrices.

The matrix corresponding to a set of rotations about **moving** axes can be found by **postmultiplying** the rotation matrices, thus multiplying them in the same order in which the rotations take place. The rotation matrix formed by a rotation by an angle about the z-axis followed by a rotation by an angle about the **moved** y-axis, is then given by:

The composition of rotations about **fixed** axes, on the other hand, is found by **premultiplying** the different elementary rotation matrices.

### Inverse Rotations[edit | edit source]

The inverse of a **single** rotation about a frame axis is a rotation by the negative of the rotation angle about the same axis:

The inverse of a compound rotation follows from the inverse of the matrix product:

### MATLAB Example[edit | edit source]

**Note**: These examples require the Robotics Toolbox to be properly installed.

theta = pi/2; T_x = rotx(theta); % Returns a 4x4 pose matrix. The upper-left 3x3 submatrix is the % rotation matrix representing a rotation by theta about the x-axis. R_x = tr2rt(T_x); % Returns the 3x3 rotation matrix corresponding with T_x. T_y = roty(theta); % A rotation about the y-axis. T_z = rotz(theta); % A rotation about the z-axis.

## Euler Angles[edit | edit source]

Contrary to the rotation matrix, **Euler angles** are a minimal representation (a set of just **three** numbers, that is) of relative orientation. This set of three angles describes a sequence of rotations about the axes of a moving reference frame. There are, however, many (12, to be exact) sets that describe the same orientation: different combinations of axes (e.g. ZXZ, ZYZ, and so on) lead to different Euler angles. Euler angles are often used for the description of the orientation of the wrist-like end-effectors of many serial manipulator robots.

**Note**: Identical axes should not be in consecutive places (e.g. ZZX). Also, the range of the Euler angles should be limited in order to avoid different angles for the same orientation. E.g.: for the case of ZYZ Euler angles, the first rotation about the z-axis should be within . The second rotation, about the moved y-axis, has a range of . The last rotation, about the moved z-axis, has a range of .

### Forward Mapping[edit | edit source]

Forward mapping, or finding the orientation of the end-effector with respect to the base frame, follows from the composition of rotations about moving axes. For a rotation by an angle about the z-axis, followed by a rotation by an angle about the moved x-axis, and a final rotation by an angle about the moved z-axis, the resulting rotation matrix is:

After writing out:

**Note:** Notice the shorthand notation: stands for , stands for , and so on.

### Inverse Mapping[edit | edit source]

In order to drive the end-effector, the inverse problem must be solved: given a certain orientation matrix, which are the Euler angles that accomplish this orientation?

For the above case, the Euler angles , and are found by inspection of the rotation matrix:

### Coordinate Singularities[edit | edit source]

In the above example, a coordinate singularity exists for . The above equations are badly numerically conditioned for small values of : the first and last equaton become undefined. This corresponds with an alignment of the first and last axes of the end-effector. The occurrence of a coordinate singularity involves the **loss of a degree of freedom**: in the case of the above example, small rotations about the y-axis require impossibly large rotations about the x- and z-axes.

No minimal representation of orientation can globally describe all orientations without coordinate singularities occurring.

### Roll-Pitch-Yaw Angles[edit | edit source]

The orientation of a rigid body can equally well be described by three consecutive rotations about fixed axes. This leads to a notation with **Roll-Pitch-Yaw** (RPY) angles.

#### Forward Mapping[edit | edit source]

The forward mapping of RPY angles to a rotation matrix similar to that of Euler angles. Since the frame now rotates about fixed axes instead of moving axes, the order in which the different rotation matrices are multiplied is inversed:

After writing out:

#### Inverse Mapping[edit | edit source]

The inverse relationships are found from inspection of the rotation matrix above:

**Note:** The above equations are badly numerically conditioned for values of near and .

## Unit Quaternions[edit | edit source]

Unit quaternions (quaternions of which the absolute value equals 1) are another representation of orientation. They can be seen as a compromise between the advantages and disadvantages of rotation matrices and Euler angle sets.

## Homogeneous Transform[edit | edit source]

The notations above describe only relative orientation. The coordinates of a point, relative to a frame , rotated and translated with respect to a reference frame , are given by:

This can be compacted into the form of a **homogeneous transformation matrix** or **pose (matrix)**. It is defined as follows:

This matrix represents the position and orientation of a frame whose origin, relative to a reference frame , is described by , and whose orientation, relative to the same reference frame , is described by the rotation matrix .

is, thus, the representation of a frame in three-dimensional space. If the coordinates of a point are known with respect to a frame , then its coordinates, relative to are found by:

This is the same as writing:

Note that the above vectors are extended with a fourth coordinate equal to one: they're made **homogeneous**.

As was the case with rotation matrices, homogeneous transformation matrices can be interpreted in an **active** ("**displacement**"), and a **passive** ("**pose**") manner. It is also a **non-minimal** representation of a pose, that does not suffer from coordinate singularities.

### Compound Poses[edit | edit source]

If the pose of a frame is known, relative to , whose pose is known with respect to a third frame , the resulting pose is found as follows:

### MATLAB Example[edit | edit source]

x = 1; y = 1.3; z = 0.4; T = transl(x,y,z); % Returns the pose matrix corresponding with a translation over % the vector (x, y, z)'.

## Finite Displacement Twist[edit | edit source]

A pose matrix is a non-minimal way of describing a pose. A frequently used **minimal** alternative is the **finite displacement twist**:

Here, , and are a valid set (any) of Euler angles, while , and are the coordinates of a reference point on the rigid body.