鉴定(生物学)
校准
工业机器人
度量(数据仓库)
机器人
工程类
控制理论(社会学)
机械手
计算机科学
控制工程
人工智能
数学
数据挖掘
控制(管理)
统计
植物
生物
作者
Alexandr Klimchik,Yier Wu,Stéphane Caro,Florent Laroche,Alexandr Klimchik
出处
期刊:Mechanisms and machine science
日期:2014-01-01
卷期号:: 73-81
被引量:12
标识
DOI:10.1007/978-3-319-07058-2_9
摘要
The paper is devoted to the accuracy improvement of robot-based milling by using an enhanced manipulator model that takes into account both geometric and elastostatic factors. Particular attention is paid to the model parameters identification accuracy. In contrast to other works, the proposed approach takes into account impact of the gravity compensator and link weights on the manipulator elastostatic properties. In order to improve the identification accuracy, the industry oriented performance measure is used to define optimal measurement configurations and an enhanced partial pose measurement method is applied for the identification of the model parameters. The advantages of the developed approach are confirmed by experimental results that deal with the elastostatic calibration of a heavy industrial robot used for milling. The achieved accuracy improvement factor is about 2.4.
科研通智能强力驱动
Strongly Powered by AbleSci AI