补偿(心理学)
空格(标点符号)
点(几何)
机器人
接头(建筑物)
网格
计算机科学
机器人航天器
控制理论(社会学)
拓扑(电路)
数学
几何学
人工智能
工程类
结构工程
组合数学
心理学
控制(管理)
精神分析
操作系统
作者
Yingjie Guo,Xuanhua Gao,Wei Yan Liang,Lei Miao,Shuo Zhao,Huiyue Dong
标识
DOI:10.1016/j.cirpj.2024.02.011
摘要
The poor absolute positioning accuracy of industrial robots has limited their application in fields such as aerospace manufacturing. To address this issue, the spatial grid compensation method has been proposed as an effective solution. In this paper, we propose a sampling method based on three-dimensional discrete point space circular fitting for grid points to significantly reduce the sampling workload and improve compensation accuracy compared to traditional joint space grid compensation methods. Additionally, we use the Kriging interpolation algorithm instead of the inverse distance weighting (IDW) algorithm for spatial interpolation prediction of pose error. Based on this, the proposed sampling and interpolation prediction method in this paper was verified on a Comau NJ500–2.7 manipulator equipped with a fiber-laying end effector. The experimental results demonstrate that using our proposed sampling method yields pose data of grid points that have only a small deviation from directly sampled results and are only slightly higher than the robot's repeat positioning accuracy. Moreover, our proposed method can significantly reduce the sampling workload by 60% under the experimental conditions of this study (sampling 10 groups of data within 90 degrees range), with increasing sampling range leading to more obvious efficiency improvements. Finally, we show that compared to the IDW algorithm, the Kriging interpolation algorithm yields better results and improves the mean absolute positioning accuracy of robots after compensation by 30%.
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