The problem presented here is for a robot described in the previous
section. We have seen that the hand of the robot was moving with
respect to its base by using linear actuators that allow to change the
distance between the pairs of points . But in practice these
actuators have a limited stroke and consequently the distance between
the points has to lie within a given range:

Furthermore the actuator is attached at point by joints (typically ball-and-socket or universal joints) that have limited motion. Hence the platform may reach only a limited set of position/orientation that is called its

Assume now that the hand has to follow a time-dependent trajectory: it
is clearly important to verify that all the points of this trajectory
lie within the workspace of the robot.
The trajectory is defined in the following manner: the
position/orientation parameter are written as analytic function of the
time T (which is assumed to lie in the range [0,1] without lack of
generality). Using the solution of the inverse kinematics (see the
previous section) it is then possible to express the distance
as functions of the time. For the trajectory to lie in the workspace
we have to verify the 12 inequalities:

when T lie in the range [0,1]. As the analytical form of the position/orientation parameters may be arbitrary we are looking for a generic algorithm that may deal with such arbitrary trajectory.

This can easily be done with interval analysis
(see [9] for a detailed version). First we define the
trajectory in Maple and compute the analytical form of (and
of any other constraint that may limit the workspace of the robot). We
get a set of inequalities that has to be satisfied for any T in [0,1]
if the trajectory
lie within the workspace. The analytical form of these inequalities
are written in a file: this allow their interval evaluation for any T
range by using the `ALIAS` parser. Then the general solving
procedure of `ALIAS` may be used to determine if there is a T such
that at least one constraint is violated.

An important point is that the algorithm allow to deal with the uncertainties in the problem. A first uncertainty occurs when controlling the robot along its nominal trajectory. Indeed the robot controller is not perfect and there will be a positioning error: for a nominal value of the position/orientation parameters the reached pose will be where can be bounded. A second uncertainty source is due to the differences between the theoretical geometrical model of the robot and its real geometry. Indeed to solve the inverse kinematics we use the coordinates of the in the reference frame and of the in the model frame. In practice however these coordinates are known only up to a given accuracy: hence these coordinates for the real robot may have any value within given ranges. Hence the inequalities of the problem have not fixed value coefficients but interval coefficients. But this no problem for interval analysis as the general solving procedures may deal with such inequalities. Hence if the algorithm find out that all inequalities are verified for any T in [0,1], then this means that whatever is the real robot and the positioning error of the robot controller the trajectory followed by the robot will fully lie within the robot workspace.