1.河北水利电力学院电气自动化系，河北 沧州 061001; 2. 苏州大学机电工程学院江苏省先进机器人技术重点实验室，江苏 苏州，215123; 3.河北省高校水利自动化与信息化应用技术研发中心，河北 沧州 061001
TP249
国家重点研发计划项目（2019YFB1310201）、沧州市科技计划自筹经费项目(204102006）、河北水利电力学院基本科研业务费研究项目-青年科研创新项目(SYKY2019）、河北省高校水利自动化与信息化应用技术研发中心资助
Wang Zhengbo
1. Hebei University of Water Resources and Electric Engineering Electrical automation Department, 061001, Cangzhou, Hebei, China；3.Research and Development Center of Water Conservancy Automation and Information Technology in Colleges and Universities of Hebei Province, HeBei Cangzhou 061001,ChinaTang Yong
1. Hebei University of Water Resources and Electric Engineering Electrical automation Department, 061001, Cangzhou, Hebei, China；3.Research and Development Center of Water Conservancy Automation and Information Technology in Colleges and Universities of Hebei Province, HeBei Cangzhou 061001,ChinaLiu Haibo
1. Hebei University of Water Resources and Electric Engineering Electrical automation Department, 061001, Cangzhou, Hebei, China；3.Research and Development Center of Water Conservancy Automation and Information Technology in Colleges and Universities of Hebei Province, HeBei Cangzhou 061001,China1.Hebei University of Water Resources and Electric Engineering Electrical automation Department, 061001, Cangzhou, Hebei, China; 2.Jiangsu Provincial Key Laboratory of Advanced Robotics, School of Mechanical and Electrical Engineering, Soochow University, JiangSu Suzhou 215123, China; 3.Research and Development Center of Water Conservancy Automation and Information Technology in Colleges and Universities of Hebei Province, HeBei Cangzhou 061001,China
针对复杂工业生产环境中机器人在杂乱的零件箱中进行分拣的问题，需要完成工件的空间定位、不同类型的工件识别以及机器人的抓取操作，现有的视觉技术不能满足随机分拣任务。故提出结合双目立体视觉、深度学习和UR5机器人组成一个智能的机器人分拣系统。提出立体视觉与投影结构光结合的三维视觉系统，重构立体匹配能量函数，完成工件的空间定位；利用基于深度学习的实例分割方法完成工件的精准识别；结合机器人手眼标定技术与工件空间定位和识别结果，实现基于双目结构光与深度学习的工件随机分拣系统。分析随机分拣过程中螺丝工件随机分拣成功率，得到不同数量的螺丝工件单次全部分拣成功的平均成功率为92.8%，按工件总数量的随机分拣成功率为98.8%，验证了系统的可行性。
In order to solve the problem of robot sorting in the disorderly parts box in the complex industrial production environment, it is necessary to complete the spatial positioning of the workpiece, the recognition of different types of the workpiece and the grasping operation of the robot. The existing vision technology can not meet the random sorting task.Therefore, an intelligent robot sorting system combining binocular stereo vision, deep learning and UR5 robot is proposed.A three-dimensional vision system combining stereoscopic vision and projection structured light was proposed. The energy function of stereoscopic matching was reconstructed to complete the spatial positioning of the workpiece.An instance segmentation method based on deep learning was used to accurately identify the workpiece.A random sorting system based on binocular structured light and deep learning was realized by combining robot hand-eye calibration technology with workpiece spatial positioning and recognition results. By analyzing the success rate of random sorting of screw workpiece in the process of random sorting, the average success rate of all single sorting of screw workpiece of different quantity is 92.8%, and the success rate of random sorting according to the total number of workpiece is 98.8%, which verifies the feasibility of the system.
王政博,唐勇,陈国栋,刘海波.基于双目结构光与深度学习的工件随机分拣技术研究[J].电子测量技术,2021,44(16):168-174
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