Predictive Path-Accurate Scaling of a Sensor-Based Defined Trajectory

Abstract

The paper considers an a priori given robot trajectory which has to be recomputed when online sensed information on the environment is available. Then the original trajectory is adapted in order to continue the so far commanded motion by the sensed geometric shape. The adapted trajectory has to comply with restrictions on velocity, acceleration and jerk. Furthermore it is desired to converge to the original trajectory. At least if the robot is in contact with the environment it is further essential that the geometrical path is not left when modifying the trajectory. This means that preferably only the temporal profile is changed by scaling or rescaling the velocity. In order to inhibit overshooting, future restrictions are predicted and backtracked in the case of a violation. All this computation is done within a single sampling step, i.e. within 4~ms for a standard KUKA industrial robot. This precludes accurate optimization algorithms. When applied without an a priori given trajectory the method results in an near time-optimal solution.