Literature DB >> 33489276

Synchronization-based control for a collaborative robot.

Henry Eberle1, Slawomir J Nasuto2, Yoshikatsu Hayashi2.   

Abstract

This article introduces a new control scheme for controlling a robotic manipulator in a collaborative task, allowing it to respond proactively to its partner's movements. Unlike conventional robotic systems, humans can operate in an unstructured, dynamic environment due to their ability to anticipate changes before they occur and react accordingly. Recreating this artificially by using a forward model would lead to the huge computational task of simulating a world full of complex nonlinear dynamics and autonomous human agents. In this study, a controller based on anticipating synchronization, where a 'leader' dynamical system is predicted by a coupled 'follower' with delayed self-feedback, is used to modify a robot's dynamical behaviour to follow that of a series of leaky integrators and harmonic oscillators. This allows the robot (follower) to be coupled with a collaborative partner (leader) to anticipate its movements, without a complete model of its behaviour. This is tested by tasking a simulated Baxter robot with performing a collaborative manual coordination task with an autonomous partner under a range of feedback delay conditions, confirming its ability to anticipate using oscillators instead of a detailed forward model.
© 2020 The Authors.

Entities:  

Keywords:  anticipating synchronization; collaborative robotics; coupled oscillators; synchronization; time delay

Year:  2020        PMID: 33489276      PMCID: PMC7813249          DOI: 10.1098/rsos.201267

Source DB:  PubMed          Journal:  R Soc Open Sci        ISSN: 2054-5703            Impact factor:   2.963


  9 in total

1.  Anticipating chaotic synchronization

Authors: 
Journal:  Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics       Date:  2000-05

2.  Dynamic long-term anticipation of chaotic states.

Authors:  H U Voss
Journal:  Phys Rev Lett       Date:  2001-06-19       Impact factor: 9.161

3.  Mosaic model for sensorimotor learning and control.

Authors:  M Haruno; D M Wolpert; M Kawato
Journal:  Neural Comput       Date:  2001-10       Impact factor: 2.026

4.  A negative group delay model for feedback-delayed manual tracking performance.

Authors:  Henning U Voss; Nigel Stepp
Journal:  J Comput Neurosci       Date:  2016-08-17       Impact factor: 1.621

5.  Renormalized time scale for anticipating and lagging synchronization.

Authors:  Yoshikatsu Hayashi; Slawomir J Nasuto; Henry Eberle
Journal:  Phys Rev E       Date:  2016-05-31       Impact factor: 2.529

6.  Human hand moves proactively to the external stimulus: an evolutional strategy for minimizing transient error.

Authors:  Fumihiko Ishida; Yasuji E Sawada
Journal:  Phys Rev Lett       Date:  2004-10-15       Impact factor: 9.161

7.  On Strong Anticipation.

Authors:  N Stepp; M T Turvey
Journal:  Cogn Syst Res       Date:  2010-06-01       Impact factor: 3.523

8.  Anticipation in feedback-delayed manual tracking of a chaotic oscillator.

Authors:  Nigel Stepp
Journal:  Exp Brain Res       Date:  2009-07-16       Impact factor: 1.972

9.  Anticipation from sensation: using anticipating synchronization to stabilize a system with inherent sensory delay.

Authors:  Henry Eberle; Slawomir J Nasuto; Yoshikatsu Hayashi
Journal:  R Soc Open Sci       Date:  2018-03-14       Impact factor: 2.963

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.