基于非结构基本组成分析的自主抓取方法

doi:10.13973/j.cnki.robot.180762

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刘汉伟, 曹雏清, 王永娟. 基于非结构基本组成分析的自主抓取方法[J]. 机器人, 2019, 41(5): 583-590.DOI: 10.13973/j.cnki.robot.180762. LIU Hanwei, CAO Chuqing, WANG Yongjuan. Autonomous Grasping Method Based on Non-Structural Basic Composition Analysis. ROBOT, 2019, 41(5): 583-590. DOI: 10.13973/j.cnki.robot.180762.

摘要针对现实生活中的非结构化抓取环境，提出一种基于非规则物体基本形体组成的自主抓取方法．机器人自主抓取的关键不仅仅在于对物体类型的识别，更大一部分在于对物体形状（例如形状基元的组成）判断后的良好抓取．将不规则的复杂物体简化为一些简单物体的组合，利用基于特征点和核心提取的网格分割（MFC）算法将被抓取物体3D数据点分割为主体和分支部分，依据最优拟合算法将各部分拟合为球体、椭球体、圆柱体、平行六面体中的一种，并依据简化结果对抓取位姿进行约束，再对简化后物体进行抓取训练，获取最优抓取框，从而实现对未知物体的自主抓取．本文方法最终在Baxter机器人上实现了93.3%的抓取准确率．实验结果表明，该方法可应用于不同形状、不同位姿的未知非规则物体，鲁棒性较强．

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	刘汉伟
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关键词 ：非规则物体, 形状基元, 网格分割, 抓取训练

Abstract：Aiming at the unstructured grasping environment in real life, an autonomous grasping method based on the basic objects composition of irregular objects is proposed. The key to autonomous grasping of robots lies not only in the recognition of object types, but also in the good grasping of object's shapes (such as the composition of shape primitives). The irregular complex object is simplified as the composition of some simple objects. The 3D data points of the grasped object are segmented into the main body and the branch part based on the mesh segmentation using feature point and core extraction (MFC) algorithm, and the parts are fitted to a sphere, an ellipsoid, a cylinder or a parallelepiped according to an optimal fitting algorithm. Based on the simplified result, the grasping posture is constrained, and then the grasping training of the simplified object is completed to obtain the optimal grasping frame, thereby realizing the autonomous grasping of the unknown object. Finally, the method proposed realizes the grasping operation with an accuracy of 93.3% on the Baxter robot. The experimental results show that the method can be applied to unknown irregular objects of different shapes and postures with strong robustness.

Key words： irregular object shape primitive mesh segmentation grasping training

收稿日期: 2018-12-24 录用日期: 2019-04-09

TP391

基金资助:国家自然科学基金（61503186）

通讯作者: 曹雏清,caochuqing@njust.edu.cn E-mail: caochuqing@njust.edu.cn

作者简介: 刘汉伟(1996-),男,硕士生.研究领域:机器视觉,机器人控制.
曹雏清(1982-),男,博士,讲师.研究领域:智能化机器人,模式识别与人工智能,移动机器人自主导航及路径规划.
王永娟(1972-),女,博士,教授.研究领域:智能自动机技术,软体机器人技术,小型智能机器人.

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https://robot.sia.cn/CN/10.13973/j.cnki.robot.180762 或 https://robot.sia.cn/CN/Y2019/V41/I5/583

[1] Saxena A, Driemeyer J, Ng A Y. Robotic grasping of novel objects using vision[J]. International Journal of Robotics Research, 2008, 27(2):157-173.
[2] Hsiao K, Chitta S, Ciocarlie M, et al. Contact-reactive grasping of objects with partial shape information[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA:IEEE, 2010:1228-1235.
[3] 喻群超,尚伟伟,张驰.基于三级卷积神经网络的物体抓取检测[J].机器人,2018,40(5):762-768.Yu C Q, Shang W W, Zhang C. Object grasp detection based on three-level convolution neural network[J]. Robot, 2018, 40(5):762-768.
[4] Le Q V, Kamm D, Kara A F, et al. Learning to grasp objects with multiple contact points[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA:IEEE, 2010:5062-5069.
[5] Lenz I, Lee H, Saxena A. Deep learning for detecting roboticgrasps[J]. International Journal of Robotics Research, 2014, 34(4-5):705-724.
[6] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553):436-444.
[7] Suzuki K. Overview of deep learning in medical imaging[J]. Radiological Physics and Technology, 2017, 10(3):257-273.
[8] Huebner K, Ruthotto S, Kragic D. Minimum volume bounding box decomposition for shape approximation in robot grasping[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA:IEEE, 2008:1628-1633.
[9] George P L, Borouchaki H. Delaunay triangulation and meshing:Application to finite elements[M]. London, UK:Kogan Page, 1998.
[10] Katz S, Leifman G, Tal A. Mesh segmentation using feature point and core extraction[J]. Visual Computer, 2005, 21(8-10):649-658.
[11] Kurant M, Markopoulou A, Thiran P. On the bias of BFS[C]//22nd International Teletraffic Congress. 2010. DOI:10.1109/ITC.2010.5608727.
[12] Au O K C, Zheng Y Y, Chen M L, et al. Mesh segmentation with concavity-aware fields[J]. IEEE Transactions on Visualization and Computer Graphics, 2012, 18(7):1125-1134.
[13] Katz S, Tal A. Hierarchical mesh decomposition using fuzzy clustering and cuts[J]. ACM Transactions on Graphics, 2003, 22(3):954-961.
[14] Goldfeather J, Interrante V. A novel cubic-order algorithm for approximating principal direction vectors[J]. ACM Transac-tions on Graphics, 2004, 23(1):45-63.
[15] Fang Z G, Jiang J X, Xu J, et al. Efficient collision detection using bounding volume hierarchies of OBB-AABBs and its application[C]//International Conference on Computer Design & Applications. Piscataway, USA:IEEE, 2010:V5242-V5246.
[16] El-Khoury S, Sahbani A. A new strategy combining empirical and analytical approaches for grasping unknown 3D objects[J]. Robotics and Autonomous Systems, 2010, 58(5):497-507.
[17] Lian Z, Godil A, Bustos B, et al. SHREC'11 track:Shape retrieval on non-rigid 3D watertight meshes[DB/OL]. (2011-04-01)[2018-12-20]. https://www.itl.nist.gov/iad/vug/sharp/contest/2011/NonRigid/data.html.
[18] Cherkassky V, Ma Y Q. Practical selection of SVM parameters and noise estimation for SVM regression[J]. Neural Networks, 2004, 17(1):113-126.
[19] Johns E, Leutenegger S, Davison A J. Deep learning a graspfunction for grasping under gripper pose uncertainty[C]//IEEE/RSJ International Conference on Intelligent Robots and Sys-tems. Piscataway, USA:IEEE, 2016:4461-4468.
[20] Rusinkiewicz S, Levoy M. Efficient variants of the ICP algori-thm[C]//3rd International Conference on 3-D Digital Imaging and Modeling. Piscataway, USA:IEEE, 2001:145-152.
[21] Torr P H S, Zisserman A. MLESAC:A new robust estimator with application to estimating image geometry[J]. Computer Vision and Image Understanding, 2000, 78(1):138-156.