基于视觉引导的工业机器人示教编程系统

倪自强; 王田苗; 刘达

doi:10.13700/j.bh.1001-5965.2015.0218

基于视觉引导的工业机器人示教编程系统

doi: 10.13700/j.bh.1001-5965.2015.0218

北京航空航天大学机械工程与自动化学院, 北京 100083

基金项目: 国家"863"计划(2013AA041201);国家自然科学基金(61175104)

详细信息

作者简介:
倪自强男,博士研究生。主要研究方向:机器视觉。Tel.:010-82338271 E-mail:e-pi@163.com;王田苗男,博士,教授,博士生导师。主要研究方向:工业机器人。Tel.:010-82338271 E-mail:itm@buaa.edu.cn;刘达男,博士,副教授。主要研究方向:医疗机器人。Tel.:010-82338271 E-mail:drliuda@buaa.edu.cn

通讯作者:
王田苗,Tel.:010-82338271 E-mail:itm@buaa.edu.cn

中图分类号: V221⁺.3;TB553
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出版历程
- 收稿日期: 2015-04-13
- 网络出版日期: 2016-03-20

Vision guide based teaching programming for industrial robot

School of Mechanical Engineering and Automation, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

Funds: National High-tech Research and Development Program of China (2013AA041201);National Natural Science Foundation of China (61175104)

摘要

摘要: 针对目前工业机器人所采用的编程方式的局限性,提出了基于视觉引导的工业机器人示教编程方式。首先采用奇异值分解(SVD)法建立双目视觉系统与机器人的坐标映射关系,通过双目视觉系统识别和提取特定的示教工具末端位姿,以得到规划机器人运动所需的位姿数据。然后解析机器人控制器可执行文件的格式,将位姿数据转化为机器人可以执行的文件。最后通过试验验证了视觉引导示教方式的可行性,并对示教跟踪精度进行了分析,结果表明,跟踪误差最大为-1.18 mm,均方根误差为0.47 mm。
- 工业机器人 /
- 示教编程 /
- 视觉 /
- 焊接 /
- 奇异值分解(SVD)
Abstract: Most industrial robots used in manufacture are based on teaching programming and offline programming. The shortcomings of these two programming methods limit the further application of industrial robots. A vision guide based programming method was introduced to solve the problem. The singular value decomposition (SVD) algorithm was used to calculate the registration matrix between computer vision space and robot space. The positions and orientations of robot's end-actuator were obtained by measuring the teaching tool, which is the key process to realize vision guide programming. Analytical the format of executable file which running on robot controller, and then transform the positions and orientations' data into executable file. An experiment was introduced to verify the feasibility and reliability of the programming method. The results indicate that the maximum error of trajectory tracking is -1.18 mm, and the root mean square error is 0.47 mm.
- industrial robot /
- teaching programming /
- vision /
- weld /
- singular value decomposition (SVD)

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参考文献(15)

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