Denim-fabric-polishing robot size optimization based on global spatial dexterity
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Published:2021-06-11
Issue:1
Volume:12
Page:649-660
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ISSN:2191-916X
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Container-title:Mechanical Sciences
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language:en
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Short-container-title:Mech. Sci.
Author:
Wang Wenjie,Tao Qing,Wang Xiaohua,Cao Yuting,Chen Congcong
Abstract
Abstract. This paper presents a novel method to make denim-fabric-polishing robots perform their primary task flexibly and efficiently within a limited workspace. Link lengths are optimized based on an adaptive fireworks algorithm to improve the comprehensive dexterity index. A forward kinematics analysis of the denim-fabric-polishing robot is conducted via the D–H method; the workspace is analyzed according to the needs at hand to determine the range of motion of each joint. To solve the movement condition number of the Jacobian matrix, the concept of low-condition-number probability is established, and a comprehensive dexterity indicator is constructed. The influence of the robot's size on the condition number and comprehensive dexterity index is determined. Finally, the adaptive fireworks algorithm is used to establish the objective optimization function by integrating the dexterity index and other performance indicators. The optimization results show that when the comprehensive dexterity index is taken as the optimization objective, the dexterity comprehensive index and other performance indices of the robot are the lowest; that is, the robot is more flexible. Compared with the traditional genetic algorithm and particle swarm algorithm, the adaptive fireworks algorithm proposed in this paper has better solving speed and solving precision. The optimized workspace of the robot meets the requirements of the polishing task. The design also yields a sufficiently flexible, efficient, and effective robot.
Publisher
Copernicus GmbH
Subject
Industrial and Manufacturing Engineering,Fluid Flow and Transfer Processes,Mechanical Engineering,Mechanics of Materials,Civil and Structural Engineering,Control and Systems Engineering
Reference25 articles.
1. Aghazadeh Heris, J. E. and Oskoei, M. A.: Modified genetic algorithm for solving n-queens problem, Iranian Conference on Intelligent Systems (ICIS), Bam, Iran, 4–6 February 2014, IEEE, 14253285, https://doi.org/10.1109/iraniancis.2014.6802550, 2014. 2. Babu, T. S., Ram, J. P., Sangeetha, K., Laudani, A., and Rajasekar, N: Parameter extraction of two diode solar PV model using Fireworks algorithm, Sol. Energy, 140, 265–276, https://doi.org/10.1016/j.solener.2016.10.044, 2016. 3. Deng, Z.-l., Cheng, F., and Zhang, M.: Design and optimization of arm structure for mobile medical service robot based on multi-target particle swarm optimization, International Journal of Mechatronics and Applied Mechanics, 2107, 115–121, https://doi.org/10.17683/ijomam/issue2.17, 2017. 4. Gan, Y., Wang, J., and Sun, F.: D–H parameter optimization design of 6R robot based on given workspace, China Mechanical Engineering, 25, 3003–3007, https://doi.org/10.3969/j.issn.1004-132X.2014.22.004, 2014. 5. Gao, Z., Lan, X., and Bian, Y.: Structural Dimension Optimization of Robotic Belt Grinding System for Grinding Work pieces with Complex Shaped Surfaces Based on Dexterity Grinding Space, Chinese Journal of Aeronautics, 24, 346–354, https://doi.org/10.1016/S1000-9361(11)60041-1, 2011.
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