Trajectory redundancy iterative learning control

Abstract

For tasks which require a robot to track some particular points along a trajectory (instead of the whole trajectory), there exists redundancy. This redundancy results in an increase in the feasibility in the controller design, enabling the possibility of the robot to obtain better performance by satisfying secondary objectives whilst performing the primary objective of tracking the target points. This paper addresses the task redundancy by using point-to-point learning control. It is shown to be an effective tool to accommodate trajectory redundancy since it has the ability to fully explore the increased feasibility resulting from such redundancy. Following the similar idea widely used in kinematic redundancy, a decomposition technique is used. This leads to a simplification of constrained optimization and provides a suboptimal performance in terms of secondary task while the primary task is always achieved. As an example, the formulation is implemented in an on-line fashion to enable a non-redundant robot to track a target point whilst avoiding an obstacle. Simulation results shows good performance from the proposed online algorithms.