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SCIENCE CHINA Information Sciences, Volume 59, Issue 11: 112212(2016) https://doi.org/10.1007/s11432-015-0463-9

An effective method for grasp planning on objects with complex geometry combining human experience and analytical approach

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  • ReceivedNov 28, 2015
  • AcceptedMar 11, 2016
  • PublishedOct 17, 2016

Abstract

In this paper, an effective method for identifying the graspable components of objects with complex geometry is proposed for grasp planning based on human experience. Instead of focusing on individual objects, our method identifies graspable components on the category level under the assumption that geometrically alike objects share similar graspable components. Firstly, employing a modified SHOT descriptor, a fast KNN-based method is developed for object categorization. Then, the graspable components are identified by adopting a learning framework based on human experience. Afterwards, a fast analytical grasp planning method is proposed which comprises of contact points exaction and hand kinematics calculation. Finally, a regression model based on the extreme learning method (ELM) is built which inputs the desired contact points and the wrist orientation and outputs the wrist position. This approach is time-saving comparing with the optimization method. The simulations and experiments demonstrate the effectiveness of the proposed approach by realizing grasps on the graspable components of human choice for objects with complex geometry.


Funded by

National Natural Science Foundation of China(61327809)

National Natural Science Foundation of China(91520201)

National Natural Science Foundation of China(91420302)

National Natural Science Foundation of China(61210013)


Acknowledgment

Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant Nos. 61210013, 61327809, 91420302, 91520201).


References

[1] Lin G D, Li Z J, Liu L, et al. Development of multi-fingered dexterous hand for grasping manipulation. Sci China Inf Sci, 2014, 57: 120208 Google Scholar

[2] Li Z J, Deng S M, Su C Y, et al. Decentralized adaptive control of cooperating mobile manipulators with disturbance observers. IET Control Theory Appl, 2014, 8: 515-521 CrossRef Google Scholar

[3] Li Z J, Ge S S, Liu S B. Contact-force distribution optimization and control for quadruped robots using both gradient and adaptive neural networks. IEEE Trans Neural Netw Learn Syst, 2014, 25: 1460-1473 CrossRef Google Scholar

[4] Li Z J, Xiao S T, Ge S S, et al. Constrained multilegged robot system modeling and fuzzy control with uncertain kinematics and dynamics incorporating foot force. IEEE Trans Syst Man Cybern-Syst, 2015, 99: 1-14 Google Scholar

[5] El-Khoury S, Sahbani A. A new strategy combining empirical and analytical approaches for grasping unknown 3d objects. Robot Auton Syst, 2010, 58: 497-507 CrossRef Google Scholar

[6] Guo D, Sun F C, Liu C F. A system of robotic grasping with experience acquisition. Sci China Inf Sci, 2014, 57: 120202-507 Google Scholar

[7] Miller A T, Knoop S, Christensen H I, et al. Automatic grasp planning using shape primitives. In: Proceedings of IEEE International Conference on Robotics and Automation, Taipei, 2003. 1824--1829. Google Scholar

[8] Novotni M, Klein R. 3d zernike descriptors for content based shape retrieval. In: Proceedings of ACM Symposium on Solid Modeling and Applications, Washington, 2003. 216--225. Google Scholar

[9] Johnson A E, Hebert M. Using spin images for efficient object recognition in cluttered 3d scenes. IEEE Trans Patt Anal Mach Intell, 1999, 21: 433-449 CrossRef Google Scholar

[10] Körtgen M, Park G J, Novotni M, et al. 3d shape matching with 3d shape contexts. In: Proceedings of the 7th Central European Seminar on Computer Graphics, Budmerice, 2003. Google Scholar

[11] Bohg J, Kragic D. Learning grasping points with shape context. Robot Auton Syst, 2010, 58: 362-377 CrossRef Google Scholar

[12] Alexandre L A. 3D descriptors for object and category recognition: a comparative evaluation. In: Proceedings of IEEE Internatinal Conference on Intelligent Robots and Systems, Vilamoura, 2012. 1--6. Google Scholar

[13] Gori I, Pattacini U, Tikhanoff V, et al. Three-finger precision grasp on incomplete 3d point clouds. In: Proceedings of IEEE International Conference on Robotics and Automation, Hong Kong, 2014. 5366--5373. Google Scholar

[14] Pelossof R, Miller A, Allen P, et al. An SVM learning approach to robotic grasping. In: Proceedings of IEEE Internatinal Conference on Robotics and Automation, New Orleans, 2004. 3512--3518. Google Scholar

[15] Huang B D, El-Khoury S, Li M, et al. Learning a real time grasping strategy. In: Proceedings of IEEE International Conference on Robotics and Automation, Karlsruhe, 2013. 593--600. Google Scholar

[16] Aleotti J, Caselli S. A 3d shape segmentation approach for robot grasping by parts. Robot Auton Syst, 2012, 60: 358-366 CrossRef Google Scholar

[17] Hübner K, Kragic D. Grasping by parts: robot grasp generation from 3d box primitives. In: Proceedings of IEEE International Conference on Cognitive Systems, ETH Zurich, 2010. Google Scholar

[18] Tombari F, Salti S, Stefano L. Unique signatures of histograms for local surface description. In: Proceedings of European Conference on Computer Vision. Berlin: Springer, 2010. 356--369. Google Scholar

[19] Chen X, Ishwaran H. Random forests for genomic data analysis. Genomics, 2012, 99: 323-329 CrossRef Google Scholar

[20] Park C H, Kim S B. Sequential random k-nearest neighbor feature selection for high-dimensional data. Expert Syst Appl, 2015, 42: 2336-2342 CrossRef Google Scholar

[21] Nguyen V D. Constructing force-closure grasps. Int J Robot Res, 1988, 7: 240-245 Google Scholar

[22] Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of Internatinal Conference on Neural Networks, Perth, 1995. 1942--1948. Google Scholar

[23] Huang G B, Zhou H M, Ding X J, et al. Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B-Cybern, 2012, 42: 513-529 CrossRef Google Scholar

[24] Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: theory and applications. Neurocomputing, 2006, 70: 489-501 CrossRef Google Scholar

[25] Grana C, Davolio M, Cucchiara R. Similarity-based retrieval with mpeg-7 3d descriptors: performance evaluation on the princeton shape benchmark. In: Proceedings of the 1st International DELOS Conference, Pisa, 2007. 308--317. Google Scholar

[26] Lai K, Bo L F, Ren X F, et al. A large-scale hierarchical multi-view RGB-D object dataset. In: Proceedings of IEEE International Conference on Robotics and Automation, Shanghai, 2011. 1817--1824. Google Scholar

[27] Malvezzi M, Gioioso G, Salvietti G, et al. Syngrasp: a MATLAB toolbox for grasp analysis of human and robotic hands. In: Proceedings of IEEE International Conference on Robotics and Automation, Karlsruhe, 2013. 1088--1093. Google Scholar

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