机器人
运动学
机器人校准
残余物
工业机器人
计算机视觉
校准
补偿(心理学)
职位(财务)
计算机科学
人工智能
机器人运动学
姿势
接头(建筑物)
算法
数学
移动机器人
工程类
物理
统计
建筑工程
心理学
财务
经典力学
精神分析
经济
作者
Yuankai Qiao,Yan Lu,Hongbo Hu,Chungang Zhuang
标识
DOI:10.1109/icrcv59470.2023.10329252
摘要
Robot performance metrics like their absolute positioning accuracy have a significant impact on their industrial applications. This research introduces a kinematic calibration approach for industrial robots based on the residual network that combines joint angles and robot pose. To compensate for the geometric error, a geometric parameter identification algorithm founded on the MDH model and error model is suggested. The residual network uses joint angles and robot pose as network inputs to compensate for non-geometric error together with the results of geometric parameter identification. Experimental validation is conducted on the Rokae XB7S robot, with a dataset constructed from joint angle changes and robot pose variations. The experimental results show that the robot’s position error decreases from $0.6549\mathrm{~mm}$ to $0.1011\mathrm{~mm}$ after compensation, verifying the suggested approach’s efficiency.
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