计算机应用 ›› 2020, Vol. 40 ›› Issue (8): 2434-2440.DOI: 10.11772/j.issn.1001-9081.2019122234

• 应用前沿、交叉与综合 • 上一篇    下一篇

基于改进单次多框检测算法的机器人抓取系统

韩鑫, 余永维, 杜柳青   

  1. 重庆理工大学 机械工程学院, 重庆 400054
  • 收稿日期:2020-01-07 修回日期:2020-02-28 出版日期:2020-08-10 发布日期:2020-05-13
  • 通讯作者: 韩鑫(1994-),男,重庆人,硕士研究生,主要研究方向:机器视觉、机器人,hxcqut@sina.com
  • 作者简介:余永维(1973-),男,重庆人,高级工程师,博士,主要研究方向:机器人、机器视觉、智能制造;杜柳青(1975-),女,重庆人,教授,博士,主要研究方向:智能制造、机床精度设计。
  • 基金资助:
    重庆市基础与前沿研究计划基金资助项目(cstc2017jcyjAX0344)。

Robotic grasping system based on improved single shot multibox detector algorithm

HAN Xin, YU Yongwei, DU Liuqing   

  1. College of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, China
  • Received:2020-01-07 Revised:2020-02-28 Online:2020-08-10 Published:2020-05-13
  • Supported by:
    This work is partially supported by the Chongqing Fundamental and Frontier Research Program Fund (cstc2017jcyjAX0344).

摘要: 针对汽车零部件回收工厂在实际复杂工况下的零件检测效果不佳导致不能实现精准抓取从而影响生产效率的问题,提出了一种基于改进单次多框检测(SSD)算法的机器人抓取系统,可实现零件检测、分类、定位及抓取任务。首先,通过改进SSD模型检测目标零件,得到零件位置和类别信息;其次,通过Kinect相机标定与手眼标定将像素坐标系转换到机器人世界坐标系,实现零件在机器人空间坐标系下的定位;然后,通过机器人正逆运动学建模与轨迹规划,完成目标零件抓取任务;最后,对整个集成抓取系统进行了零件识别分类、定位到抓取验证实验。实验结果表明:复杂工况下,所提系统的零件抓取平均成功率达到95%,满足零件抓取的实际生产需求。

关键词: 单次多框检测算法, 机器人, 零件检测, 定位, 抓取

Abstract: Concerning the problem that automobile part recycling factories cannot achieve accurate grasping and thus affects production efficiency due to poor part detection under actual complex working conditions, a robotic grasping system based on improved Single Shot multibox Detector (SSD) algorithm was proposed to realize the tasks of part detection, classification, location and grasping, including detection, location and grasping functions of the target parts. First, the target parts were detected by the improved SSD model, obtaining the part location and class information. Second, through Kinect camera calibration and hand-eye calibration, the pixel coordinate system was transferred into robot world coordinate system to realize the location of parts in robot spatial coordinate system. Third, the target part grasping task was completed by robot positive and inverse kinematic modeling and trajectory planning. Finally, the validation experiments of the whole integrated system on part detection, classification, location and grasping were carried out. Experimental results show that under complex working conditions, the average part grasping success rate of the proposed system reaches 95%, which meets the actual production demand of part grasping.

Key words: Single Shot multibox Detector (SSD) algorithm, robot, part detection, location, grasping

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