Impact-Aware Control using Time-Invariant Reference Spreading

TL;DR

This paper introduces a control method that leverages simultaneous impacts in robotic manipulation using a time-invariant reference spreading framework, improving speed and efficiency through impact modeling and a robust interim-impact control mode, validated experimentally.

Contribution

It presents a novel impact-aware control approach utilizing time-invariant reference spreading and a nonsmooth physics engine for complex impact modeling, with experimental validation.

Findings

Effective impact modeling with a nonsmooth physics engine.

Robust interim-impact control mode enhances contact completion.

Experimental validation with 600 robotic impact experiments.

Abstract

With the goal of increasing the speed and efficiency in robotic manipulation, a control approach is presented that aims to utilize intentional simultaneous impacts to its advantage. This approach exploits the concept of the time-invariant reference spreading framework, in which partly-overlapping ante- and post-impact reference vector fields are used. These vector fields are coupled via an impact model in proximity of the expected impact area, minimizing the otherwise large impact-induced velocity errors and control efforts. We show how a nonsmooth physics engine can be used to construct this impact model for complex scenarios, which warrants applicability to a large range of possible impact states without requiring contact stiffness and damping parameters. In addition, a novel interim-impact control mode provides robustness in the execution against the inevitable lack of exact impact simultaneity and the corresponding unreliable velocity error during the time when contact is only partially established. This interim mode uses a position feedback signal that is derived from the ante-impact velocity reference to promote contact completion, and smoothly transitions into the post-impact mode. An experimental validation of time-invariant reference spreading control is presented for the first time through a set of 600 robotic hit-and-push and dual-arm grabbing experiments.