Cluster optimization and algorithm design for machine vision in industrial robot control systems

被引：0
作者：
Guo, Linyang [1 ,2 ]
Yang, Runxian [1 ,2 ]
Tang, Mingjun [1 ]
Ma, Xiaoyan [2 ]
Wang, Lixia [3 ]
机构：
[1] College of Intelligent Manufacturing, Yangzhou Polytechnic Institute, Jiangsu, Yangzhou
[2] Jiangsu Prov. Engineering Research Center of Intelligient Application for Advanced Plastic Forming, Jiangsu, Yangzhou
[3] Jiangsu Shuguang Electro-Optics Co. LTD., Jiangsu, Yangzhou
来源：
Applied Mathematics and Nonlinear Sciences | 2024年 / 9卷 / 01期
关键词：
IEO algorithm; Machine vision; Mean Shift algorithm; Robot cluster optimization; Waypoint extraction;
D O I：
10.2478/amns-2024-2539
中图分类号：
学科分类号：
摘要：
Machine vision technology improves the ability to detect the environment of industrial robots, which contributes to the improvement of the collaboration efficiency of swarm robots. This paper proposes a roadmap extraction algorithm that utilizes the improved Mean Shift algorithm to extract the roadmap information from images acquired by binocular cameras. Subsequently, the IEO algorithm with K-Means++ is used to optimize the task allocation of the swarm robots. The experiments show that the average error of this paper's algorithm's road sign extraction is 0.025m, the ratio of full scene and homing reaches 90.6%, and the results of the scheduling algorithm under the three kinds of task volume are 59.89, 773.08, and 2704.67. The efficiency of scheduling task completion in dispensing experiments is 9.56% higher than that of the comparative algorithms. The experiment proves that the algorithm proposed in this paper has good performance and practical effects on optimizing the industrial robot control system. © 2024 Linyang Guo et al., published by Sciendo.
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