Precise Watch-Hand Alignment Under Disturbance Condition by Microrobotic System

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Pages (from-to)278-285
Journal / PublicationIEEE Transactions on Automation Science and Engineering
Volume16
Issue number1
Online published26 Apr 2018
Publication statusPublished - Jan 2019

Abstract

Accurate alignment of the watch hand is a critical procedure in the industrial production of watches. However, compared with the traditional ideal laboratorial case, the watch-hand alignment suffers various external disturbances from the aged equipment and industrial operating conditions, which seriously influence the alignment precision. To achieve accurate watchhand alignment in the complex industrial environment, this paper develops a robotic micromanipulation system and proposes a corresponding robust control strategy. First, a micromanipulation system with five degrees of freedom is set up and integrated with an optical microscope. Then, a set of proper machine vision methods is adopted to obtain accurate position information in disregard of the interference terms. Third, the dynamic model of the controlled watch hand is built with consideration of the external disturbances from the production environment. Since the velocity information cannot be observed accurately through the machine vision process, we design a state observer to acquire the position and velocity values for the following control strategy. After that, a sliding mode controller with the radical basis function neural network is proposed to achieve high-precision alignment while resisting the external disturbance. Finally, simulation and experimental results prove that the proposed method can realize the alignment of the watch hand within 6 s with the accuracy of 2 × 10−3 rad, which is enhanced at least two times compared with the traditional manual method.

Research Area(s)

  • Cameras, Corner detection, External disturbance, Image recognition, Interference, Machine vision, manufacturing automation, micro/nano robot., microfabrication, micromanipulation, Microscopy, Production