A 3D-grasp synthesis algorithm to grasp unknown objects based on graspable boundary and convex segments

This paper presents a three dimensional (3D) grasp synthesis algorithm to achieve distinguished grasps supporting both stability and human-like grasping. The algorithm, which is based on the concepts of a graspable boundary and convex segments, was developed to enable a two-fingered gripper to grasp any unknown object, regardless of its shape, texture, or concavity, given a single 3D image data from depth sensors. The proposed algorithm provides ways to grasp any object using boundary, envelope, and functional grasps. The algorithm is based on identifying graspable segments, analyzing them geometrically, and incorporating the memory of grasping experience. Unlike most grasp synthesis research that focuses on complete 3D contours, our algorithm concentrates only on the graspable boundary and convex segments and thereby achieves stable grasps with less computational complexity. The experimental results show that the proposed algorithm provides distinguished and stable grasps for various objects in various environments, and is suitable for robots to grasp the objects successfully.

[1]  Ashutosh Saxena,et al.  Robotic Grasping of Novel Objects using Vision , 2008, Int. J. Robotics Res..

[2]  Ying Li,et al.  Data-Driven Grasp Synthesis Using Shape Matching and Task-Based Pruning , 2007, IEEE Transactions on Visualization and Computer Graphics.

[3]  Eris Chinellato,et al.  Vision and Grasping: Humans vs. Robots , 2005, IWINAC.

[4]  Danica Kragic,et al.  Minimum volume bounding box decomposition for shape approximation in robot grasping , 2008, 2008 IEEE International Conference on Robotics and Automation.

[5]  Bernt Schiele,et al.  Functional Object Class Detection Based on Learned Affordance Cues , 2008, ICVS.

[6]  Antonio Morales Escrig Learning to predict grasp reliability with a multifinger robot hand by using visual features , 2004 .

[7]  김동환,et al.  Object Class Detection using Shape-Based Feature Descriptors , 2010 .

[8]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[9]  R. Howe,et al.  Human grasp choice and robotic grasp analysis , 1990 .

[10]  Quoc V. Le,et al.  Grasping novel objects with depth segmentation , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  David H. Douglas,et al.  ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE , 1973 .

[12]  Martial Hebert,et al.  Beyond Local Appearance: Category Recognition from Pairwise Interactions of Simple Features , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Anis Sahbani,et al.  Learning the natural grasping component of an unknown object , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Van-Duc Nguyen,et al.  Constructing Force- Closure Grasps , 1988, Int. J. Robotics Res..

[15]  Moonhong Baeg,et al.  A grasp strategy with the geometric centroid of a groped object shape derived from contact spots , 2012, 2012 IEEE International Conference on Robotics and Automation.

[16]  Henrik I. Christensen,et al.  Automatic grasp planning using shape primitives , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[17]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[18]  Danica Kragic,et al.  Model based techniques for robotic servoing and grasping , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Dmitry Berenson,et al.  Grasp planning in complex scenes , 2007, 2007 7th IEEE-RAS International Conference on Humanoid Robots.

[20]  ChangHwan Kim,et al.  Humanoid's dual arm object manipulation based on virtual dynamics model , 2012, 2012 IEEE International Conference on Robotics and Automation.

[21]  Niklas Bergström,et al.  Integration of Visual Cues for Robotic Grasping , 2009, ICVS.

[22]  Andrew Blake,et al.  Multiscale Categorical Object Recognition Using Contour Fragments , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Karun B. Shimoga,et al.  Robot Grasp Synthesis Algorithms: A Survey , 1996, Int. J. Robotics Res..

[24]  Antonio Morales,et al.  Vision-based grasp planning of 3D objects by extending 2D contour based algorithms , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[25]  Cordelia Schmid,et al.  Bandit Algorithms for Tree Search , 2007, UAI.

[26]  Peter K. Allen,et al.  Data-driven grasping , 2011, Auton. Robots.

[27]  Danica Kragic,et al.  Learning grasping points with shape context , 2010, Robotics Auton. Syst..

[28]  Jörg Stückler,et al.  Shape-Primitive Based Object Recognition and Grasping , 2012, ROBOTIK.

[29]  Antonio Morales,et al.  Vision-based three-finger grasp synthesis constrained by hand geometry , 2006, Robotics Auton. Syst..

[30]  Jimmy A. Jørgensen,et al.  Grasping unknown objects using an Early Cognitive Vision system for general scene understanding , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.