Iterative path-accurate trajectory generation for fast sensor-based motion of robot arms

Abstract

Sensor-based trajectory generation of industrial robots can be seen as the task of, first, adaptation of a given robot program according to the actually sensed world, and second, its modification that complies with robot constraints regarding its velocity, acceleration, and jerk. The second task is investigated in this paper. Whenever the sensed trajectory violates a constraint, a transient trajectory is computed that, both, keeps the sensed path, and reaches the sensed trajectory as fast as possible while satisfying the constraints. This is done by an iteration of forward scaling and backtracking. In contrast to previous papers a new backtracking algorithm and an adaptation of the prediction length are presented that are favorable for high-speed trajectories. Arc Length Interpolation is used in order to improve the path accuracy. This is completed by provisions against cutting short corners or omitting of loops in the given path. The refined trajectory is computed within a single sampling step of 4 ms using a standard KUKA industrial robot.

An Online Trajectory Generator on SE(3) for Human–Robot Collaboration

“…Another iterative algorithm that considers a sampled input trajectory was proposed by Lange et al 8 and later improved for fast trajectories in ref. [9]. Their work focuses on path-accurate OTG.…”