[1]赵立明,龙大周,徐晓东,等.工业机器人加工轨迹双目3D激光扫描成像修正方法[J].智能系统学报,2021,16(4):690-698.[doi:10.11992/tis.202008008]
ZHAO Liming,LONG Dazhou,XU Xiaodong,et al.Binocular 3D laser scanning imaging-based industrial robot machining trajectory correction method[J].CAAI Transactions on Intelligent Systems,2021,16(4):690-698.[doi:10.11992/tis.202008008]
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ZHAO Liming,LONG Dazhou,XU Xiaodong,et al.Binocular 3D laser scanning imaging-based industrial robot machining trajectory correction method[J].CAAI Transactions on Intelligent Systems,2021,16(4):690-698.[doi:10.11992/tis.202008008]
工业机器人加工轨迹双目3D激光扫描成像修正方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
16
期数:
2021年第4期
页码:
690-698
栏目:
学术论文—智能系统
出版日期:
2021-07-05
- Title:
- Binocular 3D laser scanning imaging-based industrial robot machining trajectory correction method
- Keywords:
- machine vision; image processing; robot; intelligent manufacturing; sensors; 3D imaging; laser scanning; binocular vision
- 分类号:
- TP242.2
- 摘要:
- 为了提高工业机器人作业柔性，本文提出了一种基于双目CCD激光扫描3D成像的“眼在外”(Eye-to-hand:ETH)工业机器人末端(tool center point, TCP)运动轨迹在线修正方法。以激光切割机器人视觉引导为研究背景，降低加工过程机器人对物理工装定位精度的依赖。首先，为提高机器人视觉控制精度，研究了目标工件双目3D激光扫描成像空间点云坐标精确提取方法；其次，融合ETH控制特点和扫描成像系统结构，建立了一种机器人TCP运动轨迹相对偏差在线补偿方法，并通过实验验证了所提方法的可行性，为工业应用奠定基础。
- Abstract:
- In this paper, to improve the flexibility of industrial robot operations, an eye-to-hand (ETH) industrial robot end-effector (tool center point (TCP)) trajectory on-line correction method is proposed based on binocular charge-coupled device laser scanning three-dimensional (3D) imaging. Under the research background on the visual guidance of laser cutting robots, the aim of this study is to reduce the dependence of robots on the positioning accuracy of physical tools in the machining process. First, to improve the vision control accuracy of robots, research was performed on a method of accurately extracting the spatial point cloud coordinates of target workpieces based on binocular 3D laser scanning imaging. Then, an online compensation method for the relative deviation of robot TCP trajectories was established by combining ETH control characteristics and the scanning imaging system structure. The feasibility of the proposed method was verified by experiments, which lays a foundation for industrial applications.
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备注/Memo
收稿日期:2020-08-10。
基金项目:国家自然科学基金项目(51604056)；重庆市基础与前沿研究计划项目(cstc2016jcyjA0537)；重庆市重点产业重点研发项目(cstc2017zdcy-zdyfX0025)
作者简介:赵立明，副教授，主要研究方向为多传感器信息融合处理、机器人视觉引导、医学图像处理与模式识别、机器视觉与深度学习。主持国家自然科学基金项目1项、省部级产业类重点项目1项、省部级自然科学基金项目2项。发表学术论文20余篇;龙大周，硕士研究生，主要研究方向为机器人视觉引导、图像处理与模式识别;徐晓东，讲师，主要研究方向为机器人视觉引导、机器视觉与深度学习
通讯作者:徐晓东.E-mail:xxd@cqupt.edu.cn
更新日期/Last Update:
1900-01-01