一种基于高斯过程混合模型的机械臂抓取方法

doi:10.13973/j.cnki.robot.180460

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (3484 KB) HTML ( ) 输出: BibTeX \| EndNote (RIS)
引用本文:
陈友东, 刘嘉蕾, 胡澜晓. 一种基于高斯过程混合模型的机械臂抓取方法[J]. 机器人, 2019, 41(3): 343-352.DOI: 10.13973/j.cnki.robot.180460. CHEN Youdong, LIU Jialei, HU Lanxiao. A Manipulator Grasping Method Based on Mixture of Gaussian Processes Model. ROBOT, 2019, 41(3): 343-352. DOI: 10.13973/j.cnki.robot.180460.

摘要为了避免现有的基于视觉的机械臂抓取方法中存在的标定繁琐和求逆困难的不足，提出一种基于高斯过程混合模型的机械臂抓取方法.在学习阶段，利用高斯过程混合模型直接构建目标物体的位姿到机械臂关节角度的映射.在抓取阶段，通过相机获取目标物体的位姿，分别计算各个高斯分量下该位姿的生成概率，选取后验概率最大的高斯分量对应的高斯过程回归计算相应的机械臂关节角度.定位容差为20 mm时，仿真抓取成功率达到93.3%，实际抓取成功率达到了88.3%，对于精度要求不高的抓取作业，该方法可以实现机械臂的快速部署和使用.

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	陈友东
	刘嘉蕾
	胡澜晓

关键词 ：机械臂抓取, 高斯过程混合模型, 机器视觉, 机器学习

Abstract：A manipulator grasping method based on the mixture of Gaussian process (MGP) model is proposed, in order to avoid the shortcomings of the commonly used vision-based method, such as the cumbersome visual calibration and the difficulties in the inverse kinematics solution. In the learning phase, the MGP model is used to directly construct the mapping from the pose of the target object to the joint angles of the manipulator. In the grasping phase, the pose of the target object is firstly captured by the camera. Secondly, the generating probability of the pose under each Gaussian component is calculated respectively. Finally, the Gaussian process regression is selected to calculate the corresponding joint angles, which is corresponding to the Gaussian component with the maximum posterior probability. When the positioning tolerance is 20 mm, the success rate of grasping simulation reaches 93.3%, and the success rate of actual grasping reaches 88.3%. For the grasping with low precision, this method can realize the rapid deployment and the use of the robot.

Key words： manipulator grasping mixture of Gaussian processes model machine vision machine learning

收稿日期: 2018-07-12 录用日期: 2018-10-05

TP242.6

基金资助:国家科技支撑计划（2015BAF01B04）；北京市科技计划（D161100003116002）.

通讯作者: 陈友东 E-mail: chenyd@buaa.edu.cn

作者简介: 陈友东(1973-),男,博士,副教授.研究领域:机器人控制,机器人协作.
刘嘉蕾(1993-),男,硕士生.研究领域:人机协作.
胡澜晓(1995-),男,硕士生.研究领域:机器人动力学与控制.

链接本文:

https://robot.sia.cn/CN/10.13973/j.cnki.robot.180460 或 https://robot.sia.cn/CN/Y2019/V41/I3/343

[1] Jeff L. Teaching a robot manipulation skills through demonstration[D]. Cambridge, USA:Massachusetts Institute of Technology, 2004.
[2] 宋薇,仇楠楠,沈林勇,等.面向工业零件的机器人单目立体匹配与抓取[J].机器人, 2018, 40(6):950-957. Song W, Qiu N N, Shen L Y, et al. The monocular stereo matching and grasping of robot for industrial parts[J]. Robot, 2018, 40(6):950-957.
[3] 杜学丹,蔡莹皓,鲁涛,等.一种基于深度学习的机械臂抓取方法[J].机器人, 2017, 39(6):820-828,837. Du X D, Cai Y H, Lu T, et al. A robotic grasping method based on deep learning[J]. Robot, 2017, 39(6):820-828,837.
[4] Zhang Z Y. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11):1330-1334.
[5] 张李俊,黄学祥,冯渭春,等.基于运动路径靶标的空间机器人视觉标定方法[J].机器人, 2016, 38(2):193-199. Zhang L J, Huang X X, Feng W C, et al. Space robot vision calibration with reference objects from motion trajectories[J]. Robot, 2016, 38(2):193-199.
[6] 雷金周,曾令斌,叶南.工业机器人单目视觉对准技术研究[J].光学精密工程, 2018, 26(3):733-741. Lei J Z, Zeng L B, Ye N. Research on industrial robot alignment technique with monocular vision[J]. Optics and Precision Engineering, 2018, 26(3):733-741.
[7] 贾鹏霄,汪沛,周越,等.一种基于直线基元的手眼系统结构光标定方法[J].光子学报, 2018, 47(1):188-195. Jia P X, Wang P, Zhou Y, et al. Calibration method of structured light for hand-eye system based on stripe line as primitive element[J]. Acta Photonica Sinica, 2018, 47(1):188-195.
[8] 张旭,魏鹏.针对机器人位姿测量立体标靶的单目视觉标定方法[J].红外与激光工程, 2017, 46(11):221-229. Zhang X, Wei P. Monocular vision calibration method of the stereo target for robot pose measurement[J]. Infrared and Laser Engineering, 2017, 46(11):221-229.
[9] 李兵,傅卫平,王雯,等.一种基于EIH的装配机器人标定方法[J].机械工程学报, 2018, 54(7):38-44. Li B, Fu W P, Wang W, et al. EIH based calibration method for assembly robot[J]. Journal of Mechanical Engineering, 2018, 54(7):38-44.
[10] Shimizu M, Kakuya H, Yoon W K, et al. Analytical inverse kinematic computation for 7-DOF redundant manipulators with joint limits and its application to redundancy resolution[J]. IEEE Transactions on Robotics, 2008, 24(5):1131-1142.
[11] 罗天洪,陈才,李富盈.基于时变萤火虫群算法的冗余机器人手臂逆解[J].计算机集成制造系统, 2016, 22(2):576-582. Luo T H, Chen C, Li F Y. Inverse solution of redundant robot arm based on glowworm swarm optimization algorithm of timevarying[J]. Computer Integrated Manufacturing Systems, 2016, 22(2):576-582.
[12] Nie L, Huang Q. Inverse kinematics for 6-DOF manipulator by the method of sequential retrieval[C]//Proceedings of the 1st International Conference on Mechanical Engineering and Material Science. Paris, France:Atlantis Press, 2012. DOI:10.2991/mems.2012.157.
[13] Shimizu M, Kakuya H, Yoon W K, et al. Analytical inverse kinematic computation for 7-DOF redundant manipulators with joint limits and its application to redundancy resolution[J]. IEEE Transactions on Robotics, 2008, 24(5):1131-1142.
[14] Luo R C, Lin T W, Tsai Y H. Analytical inverse kinematic solution for modularized 7-DoF redundant manipulators with offsets at shoulder and wrist[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA:IEEE, 2014:516-521.
[15] Ratliff N D, Silver D, Bagnell J A. Learning to search:Function al gradient techniques for imitation learning[J]. Autonomous Robots, 2009, 27(1):25-53.
[16] Kormushev P, Calinon S, Caldwell D G. Imitation learning of positional and force skills demonstrated via kinesthetic teaching and haptic input[J]. Advanced Robotics, 2011, 25(5):11722-11729.
[17] Mollard Y, Munzer T, Baisero A, et al. Robot programming from demonstration, feedback and transfer[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA:IEEE, 2015:1825-1831.
[18] Redmon J, Angelova A. Real-time grasp detection using convolutional neural networks[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA:IEEE, 2015:1316-1322.
[19] Argall B D, Chernova S, Veloso M, et al. A survey of robot learning from demonstration[J]. Robotics and Autonomous Systems, 2009, 57(5):469-483.
[20] Atkeson C G, Schaal S. Robot learning from demonstration[C]//Fourteenth International Conference on Machine Learning. Burlington, USA:Morgan Kaufmann Publishers Inc., 1997:12-20.
[21] 陈友东,郭佳鑫,陶永.基于高斯过程的机器人自适应抓取策略[J].北京航空航天大学学报, 2017, 43(9):1738-1745. Chen Y D, Guo J X, Tao Y. Adaptive grasping strategy of robot based on Gaussian process[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(9):1738-1745.
[22] 周亚同,赵翔宇,何峰,等.基于高斯过程混合模型的大气温湿度预测[J].农业工程学报, 2018, 34(5):219-226. Zhou Y T, Zhao X Y, He F, et al. Atmospheric temperature and humidity prediction of Gaussian process mixed model[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(5):219-226.
[23] 乔少杰,金琨,韩楠,等.一种基于高斯混合模型的轨迹预测算法[J].软件学报, 2015, 26(5):1048-1063. Qiao S J, Jin K, Han N, et al. Trajectory prediction algorithm based on Gaussian mixture model[J]. Journal of Software, 2015, 26(5):1048-1063.
[24] Tresp V. Mixtures of Gaussian processes[DB/OL].[2018-12-11]. https://www.researchgate.net/publication/2800503 Mix tures of Gaussian Processes.
[25] 周亚同,陈子一,马尽文.从高斯过程到高斯过程混合模型:研究与展望[J].信号处理, 2016, 32(8):960-972. Zhou Y T, Chen Z Y, Ma J W. From Gaussian processes to the mixture of Gaussian processes:A survey[J]. Journal of Signal Processing, 2016, 32(8):960-972.
[26] Nguyen T V, Bonila E V. Fast allocation of Gaussian process experts[C]//International Conference on Machine Learning. Berlin, Germany:Springer-Verlag, 2014:145-153.