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research-article

Fast and Precise Glass Handling with Visual Servo with UKF Dual Estimation

[+] Author and Article Information
Xiaowen Yu

Graduate Research Assistant, Student Member of ASME, Mechanical System Control, Laboratory, Department of Mechanical Engineering, University of California, Berkeley, California 94720
aliceyu@berkeley.edu

Thomas Baker

Graduate Research Assistant, Department of Mechanical Engineering, Technische Universität München, Munich, Germany
tom.baker@tum.de

Yu Zhao

Graduate Research Assistant, Student Member of ASME, Mechanical System Control, Laboratory, Department of Mechanical Engineering, University of California, Berkeley, California 94720
yzhao334@berkeley.edu

Masayoshi Tomizuka

Professor, Department of Mechanical Engineering, University of California, Berkeley, California 94720
tomizuka@berkeley.edu

1Corresponding author.

ASME doi:10.1115/1.4037734 History: Received March 09, 2017; Revised August 12, 2017

Abstract

In the protective glass manufacturing industry for cell phones, placing glass pieces into the slots of the grinder requires sub-millimeter accuracy which only can be achieved by human workers, leading to a bottle neck in the production line. To address such issue, industrial robot equipped with vision sensors is proposed to support human workers. The high placing performance is achieved by a two step approach. In the first step, an Eye-to-Hand camera is installed to detect the glass piece and slot with robust vision, which can put the glass piece close to the slot and ensures a primary precision. In the second step, a closed-loop controller based on visual servo is adopted to guide the glass piece into the slot with dual Eye-in-Hand cameras. However, vision sensor suffers from a very low frame rate and slow image processing speed resulting in a very slow placing performance. In addition, the placing performance is substantially limited by the system parameter uncertainty. To compensate for these limitations, a dual-rate Unscented Kalman Filter (UKF) with dual-estimation is adopted for sensor data filtering and on-line parameter identification without requiring any linear parameterization of the model. Experimental results are presented to confirm the effectiveness of the proposed approach.

Copyright (c) 2017 by ASME
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