中文

Journal of Shanghai University >

2016 , Vol. 22 >Issue 4: 421 - 431

DOI: https://doi.org/10.3969/j.issn.1007-2861.2016.04.008

Kinect-based object grasping by robot arm hand system

Expand
  • School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, China

Received date: 2015-03-25

  Online published: 2016-08-30

Fold

Abstract

To realize automatic object grasping by a robot arm hand system, Kinect is used for real-time detection of the object. The Zhang Zhenyou chessboard method is applied to calibrate the intrinsic and external parameters of the Kinect. Depth segmentation is done to filter out most of the background interference, and identification and location of object are achieved based on the color and shape features. The object’s 3D coordinates is sent to the manipulator console to locate the target position through TCP/IP communication. A changing integration PID algorithm is applied to achieve fast and accurate grasp by controlling pressure on the dexterous hand. An experiment system is developed to verify effectiveness of the proposed methods.

Key words：  changing integration PID;  depth segmentation;  dexterous hand; Kinect calibration

Cite this article

DING Meikun, XU Yulin, JIANG Caijun, RAN Peng . Kinect-based object grasping by robot arm hand system[J]. Journal of Shanghai University, 2016 , 22(4) : 421 -431 . DOI: 10.3969/j.issn.1007-2861.2016.04.008

References

[1] Sakagami Y, Watanabe R, Aoyama C, et al. The intelligent ASIMO: system overview and integration [C]// Proceeding of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2002: 2478-2483.
[2] Stückler J, Steffens R, Holz D, et al. Efficient 3D object perception and grasp planning for mobile manipulation in domestic environments [J]. Robotics & Autonomous Systems, 2012, 61(10): 1106-1115.
[3] 尚倩, 阮秋琦, 李小利. 双目立体视觉的目标识别与定位[J]. 智能系统学报, 2011, 6(4): 303-311.
[4] 项荣. 开放环境中番茄的双目立体视觉识别与定位[D]. 杭州: 浙江大学, 2013: 216.
[5] 徐昱琳, 杨永焕, 李昕, 等. 基于双目视觉的服务机器人仿人机械臂控制[J]. 上海大学学报(自然科学版), 2012, 18(5): 506-512.
[6] 韩峥, 刘华平, 黄文炳, 等. 基于Kinect 的机械臂目标抓取[J]. 智能系统学报, 2013, 8(2): 149-155.
[7] Huang Z Y, Huang J T, Hsu C M. A case study of object identification using a Kinect sensor [C]// 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2013: 1743-1747.
[8] 孙小凯. 基于RGB-D 信息的物体定位与识别[D]. 杭州: 浙江大学, 2014: 87.
[9] Parker M, Daniel H C, Echtler F, et al. Hacking the Kinect [M]. New York: Apress, 2012: 10-25.
[10] 李格格, 沈建强. 基于Kinect 的目标跟踪与避障[J]. 江南大学学报(自然科学版), 2014, 13(4): 427-432.
[11] Zhang Z Y. A flexible new technique for camera calibration [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334.
[12] Zhang Z Y. Flexible camera calibration by viewing a plane from unknown orientations [C]//The Proceedings of the Seventh IEEE International Conference on Computer Vision. 1999: 666-673.
[13] 于仕琪, 刘瑞祯. 学习OpenCV [M]. 北京: 清华大学出版社, 2009: 406-429.
[14] Jiang L, Koch A, Scherer S A, et al. Multi-class fruit classification using RGB-D data for indoor robots [C]// 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO). 2013: 587-592.
[15] Lai K, Bo L, Ren X, et al. A large-scale hierarchical multi-view RGB-D object dataset [C]//2011 IEEE International Conference on Robotics and Automation (ICRA). 2011: 1817-1824.
[16] 阮秋琦. 数字图像处理学[M]. 3 版. 北京: 电子工业出版社, 2007: 254-257.

Options
Outlines

/

〈 〉