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Adaptive Switch Image-based Visual Servoing for Industrial Robots

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  • Robot and Applications
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Abstract

In this paper, an adaptive switch image-based visual servoing (IBVS) controller for industrial robots is presented. The proposed control algorithm decouples the rotational and translational camera motions and decomposes the IBVS control into three separate stages with different gains. This method can increase the system response speed and improve the tracking performance of IBVS while the proposed adaptive law deals with the uncertainties of the monocular camera in eye-in-hand configuration. The stability of the designed controller is proved using Lyapunov method. Experimental results on a 6 degree of freedom (DOF) robot show the significant enhancement of the control performance over other IBVS methods, in terms of the response time and tracking performance. Also the designed visual servoing controller demonstrates its capability to overcome some of the inherent drawbacks of IBVS, such its inability to perform a 180° camera rotation around its center.

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Authors and Affiliations

  1. Department of Mechanical, Industrial & Aerospace Engineering, Concordia University, Montreal, Quebec, Canada

    Ahmad Ghasemi, Pengcheng Li & Wen-Fang Xie

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  1. Ahmad Ghasemi
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  2. Pengcheng Li
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  3. Wen-Fang Xie
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Correspondence to Wen-Fang Xie.

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Ahmad Ghasemi received his B.Sc. and M.Sc. degrees in mechanical engineering from Isfahan University of Technology (IUT), Isfahan, Iran, in 2005 and 2008, respectively. He was a member of the Dynamic and Robotic Research Group, IUT, and was involved in some projects of the group. He is currently doing research on vision-based control of robots as Ph.D. student at Concordia University, Montreal, QC, Canada. His research interests include robotics and control, machine vision, machine learning and nonlinear systems.

Pengcheng Li received his B.Sc. and M.Sc. degrees in aerospace manufacturing engineering from Nanjing University of Aeronautics and Astronautics Nanjing, China, in 2006 and 2014, respectively. He has been working toward a Ph.D. degree in mechanical engineering from 2016 at Concordia University, Montreal, QC, Canada. His research interests include robotic calibration and control, visual servoing, nonlinear systems, and robotic application in aircraft assembly and fiber placement.

Wen-Fang Xie received her Ph.D. from the Hong Kong Polytechnic University in 1999 and her M.Sc degree in 1991 from Beihang University. She is a professor with the Department of Mechanical, Industrial, and Aerospace Engineering at Concordia University, Montreal, Canada. She joined Concordia University as an assistant professor in 2003 and was promoted to associate professor, professor in 2008 and 2014, respectively. Her research interests include nonlinear control and identification in mechatronics, visual servoing, model predictive control, neural network, and advanced process control and system identification.

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Ghasemi, A., Li, P. & Xie, WF. Adaptive Switch Image-based Visual Servoing for Industrial Robots. Int. J. Control Autom. Syst. 18, 1324–1334 (2020). https://doi.org/10.1007/s12555-018-0753-y

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  • DOI: https://doi.org/10.1007/s12555-018-0753-y

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