Generation and Implementation of Grasping Rectangle Based on Hierarchical Shape Context and Kernel Density Estimation towards Robotic Grasping Unknown Objects

Abstract:

This paper focuses on the perception step of robotic grasping unknown objects in order to get a stable grasping hypothesis. At first, hierarchical shape context feature is proposed to depict the local and global shape character of a sample point along the edges of the object. Moreover a kind of random forests classifier is adopted to recognize the grasping candidates in the image from vision system so that a 2D grasping rectangle can be generated through kernel density estimation. Finally, by means of stereo matching, the grasping rectangle can be mapped into the 3D space. Thus, the center of the grasping rectangle can be applied as the center of the gripper. The approaching vector and the grasping rectangle direction can be employed to determine the pose of the gripper. Simulated experiments showed that a reasonable and stable grasping rectangle can be generated for various unknown objects.

• Periodical: Applied Mechanics and Materials (Volumes 433-435)
• Pages: 537-544
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.433-435.537
• Online since: October 2013
• Authors: Guo Liang Kang, Shi Yin Qin
• Keywords: Grasping Rectangle, Kernel Density Estimation, Robotic Grasping, Shape Context

References

[1] Popovic M, Kootstra G, Jorgensen J A, et al. Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on, 2011, pp.987-994.

[2] Kroemer O, Detry R, Piater J, et al. Intelligent Robots and Systems(IROS), 2009 IEEE/RSJ International Conference on, 2009, pp.2610-2615.

DOI: 10.1109/iros.2009.5354345

[3] Saxena A, Driemeyer J, Ng A Y. The International Journal of Robotics Research Vol. 27(2008), pp.157-173.

[4] Saxena A, Wong L, Quigley M, et al, in: Robotics Research, edited by Makoto Kaneko and Yoshihiko Nakamura, Springer, Berlin Heidelberg (2011).

[5] Le Q V, Kamm D, Kara A F, et al. Robotics and Automation (ICRA), 2010 IEEE International Conference on, 2010, pp.5062-5069.

[6] Jiang Y, Moseson S, Saxena. Robotics and Automation (ICRA), 2011 IEEE International Conference on, 2011, pp.3304-3311.

[7] Bohg J, Kragic D. Robotics and Autonomous Systems Vol. 58(2010), pp.362-377.

DOI: 10.1016/j.robot.2009.10.003

[8] Nguyen V D. Proceedings of 1986 ACM Fall joint computer conference. IEEE Computer Society Press, 1986, pp.129-137.

[9] Faverjon B, Ponce J. Robotics and Automation (ICRA), 1991 IEEE International Conference on, 1991, pp.424-429.

[10] Dizioğlu B. Acta mechanica Vol. 52(1984), pp.107-118.

[11] Stansfield S A. The International journal of robotics research Vol. 10(1991), pp.314-326.

[12] Bowers D L, Lumia R. Fuzzy Systems, IEEE Transactions on Vol. 11(2003), pp.320-330.

[13] Hsiao K, Kaelbling L P, Lozano-Perez T. Robotics and Automation (ICRA), 2007 IEEE International Conference on, 2007, pp.4685-4692.

DOI: 10.1109/robot.2007.364201

[14] Hsiao K, Lozano-Perez T. Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on, 2006: 5657-5662.

[15] Boularias A, Kroemer O, Peters J. Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on, 2011, pp.1548-1553.

DOI: 10.1109/iros.2011.6094888

[16] Belongie S, Malik J, Puzicha J. Pattern Analysis and Machine Intelligence, IEEE Transactions on Vol. 24(2002), pp.509-522.

DOI: 10.1109/34.993558

[17] Breiman L. Machine learning Vol. 45(2001), pp.5-32.

[18] Sheather S J, Jones M C. Journal of the Royal Statistical Society. Series B (Methodological), 1991, pp.683-690.