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ABSTRACT
Dual-arm redundant robot systems are usually required to handle primary tasks, repetitively and synchronously in practical applications. In this paper, a jerk-level synchronous repetitive motion scheme is proposed to remedy the joint-angle drift phenomenon and achieve the synchronous control of a dual-arm redundant robot system. The proposed scheme is novelly resolved at jerk level, which makes the joint variables, i.e. joint angles, joint velocities and joint accelerations, smooth and bounded. In addition, two types of dynamics algorithms, i.e. gradient-type (G-type) and zeroing-type (Z-type) dynamics algorithms, for the design of repetitive motion variable vectors, are presented in detail with the corresponding circuit schematics. Subsequently, the proposed scheme is reformulated as two dynamical quadratic programs (DQPs) and further integrated into a unified DQP (UDQP) for the synchronous control of a dual-arm robot system. The optimal solution of the UDQP is found by the piecewise-linear projection equation neural network. Moreover, simulations and comparisons based on a six-degrees-of-freedom planar dual-arm redundant robot system substantiate the operation effectiveness and tracking accuracy of the robot system with the proposed scheme for repetitive motion and synchronous control.
Acknowledgments
This work is supported by the National Natural Science Foundation of China (with number 61473323), by the Foundation of Key Laboratory of Autonomous Systems and Networked Control, Ministry of Education, China (with number 2013A07), and also by the Science and Technology Program of Guangzhou, China (with number 2014J4100057). Besides, kindly note that both authors of the paper are jointly of the first authorship. The authors would like to thank the editors and anonymous reviewers for their time and effort spent in handling the paper as well as many constructive comments provided for improving much further the presentation and quality of the paper.
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No potential conflict of interest was reported by the authors.
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Dechao Chen
Dechao Chen received the B.S. degree in Electronic Information Science and Technology from Guangdong University of Technology, Guangzhou, China, in 2013. He is currently pursuing his Ph.D. degree in Communication and Information Systems at School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China, under the direction of Professor Yunong Zhang. In addition, he is also with the SYSU-CMU Shunde International Joint Research Institute, Foshan, China, for cooperative research. His research interests include robotics, neural networks, nonlinear dynamics systems.
Yunong Zhang
Yunong Zhang received the B.S. degree from Huazhong University of Science and Technology, Wuhan, China, in 1996, the M.S. degree from South China University of Technology, Guangzhou, China, in 1999, and the Ph.D. degree from Chinese University of Hong Kong, Shatin, Hong Kong, China, in 2003. He is currently a professor at School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China. Before joining Sun Yat-sen University in 2006, Yunong had been with National University of Singapore, University of Strathclyde, and National University of Ireland at Maynooth, since 2003. In addition, he is also currently with the SYSU-CMU Shunde International Joint Research Institute, Foshan, China, for cooperative research. His main research interests include robotics, neural networks, computation and optimization.