Analysis of shapes to measure surfaces: An approach for detection of deformations

This paper presents a method to analyse 3D planar surfaces and to measure variations on it. The method is oriented to the detection of deformations on the elastic object surfaces formed by flat faces. These deformations are usually caused when two bodies, a solid and another elastic object, come in contact and there are contact pressures among their faces. Our method describes a strategy to model the shape of deformation using a mathematical approach based on two concepts: Histogram and Map of curvature. In particular, we describe the algorithm for deformations in order to use it in visual control and inspection tasks for manipulation processes with robot hands. Several experiments and their results are shown to evaluate the validity and robustness of the method to detect and measure deformations in grasping tasks. To do it, some virtual scenarios were created to simulate contacts with fingers of a hand robot.

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

[2]  Pierre Payeur,et al.  Visual monitoring of surface deformations on objects manipulated with a robotic hand , 2010, 2010 IEEE International Workshop on Robotic and Sensors Environments.

[3]  José M. Sebastián,et al.  Improving detection of surface discontinuities in visual-force control systems , 2008, Image Vis. Comput..

[4]  D. Burschka,et al.  Vision based haptic multisensor for manipulation of soft, fragile objects , 2012, 2012 IEEE Sensors.

[5]  V. Kshirsagar,et al.  Face recognition using Eigenfaces , 2011, 2011 3rd International Conference on Computer Research and Development.

[6]  Shinichi Hirai,et al.  Robust grasping manipulation of deformable objects , 2001, Proceedings of the 2001 IEEE International Symposium on Assembly and Task Planning (ISATP2001). Assembly and Disassembly in the Twenty-first Century. (Cat. No.01TH8560).

[7]  Francesc Moreno-Noguer,et al.  FINDDD: A fast 3D descriptor to characterize textiles for robot manipulation , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Michael Garland,et al.  Efficient adaptive simplification of massive meshes , 2001, Proceedings Visualization, 2001. VIS '01..

[9]  Florentin Wörgötter,et al.  Voxel Cloud Connectivity Segmentation - Supervoxels for Point Clouds , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Dmitry Berenson,et al.  Manipulation of deformable objects without modeling and simulating deformation , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Pasu Boonvisut,et al.  Identification and active exploration of deformable object boundary constraints through robotic manipulation , 2014, Int. J. Robotics Res..

[12]  Ashutosh Saxena,et al.  Learning haptic representation for manipulating deformable food objects , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Oliver Brock,et al.  Manipulating articulated objects with interactive perception , 2008, 2008 IEEE International Conference on Robotics and Automation.

[14]  Markus H. Gross,et al.  Efficient simplification of point-sampled surfaces , 2002, IEEE Visualization, 2002. VIS 2002..

[15]  Pierre Payeur,et al.  Dexterous Robotic Manipulation of Deformable Objects with Multi-Sensory Feedback - a Review , 2010 .

[16]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  Nobuyuki Otsu,et al.  ATlreshold Selection Method fromGray-Level Histograms , 1979 .

[18]  Arvind K. Ramadorai,et al.  Vision based manipulation of non-rigid objects , 1996, Proceedings of IEEE International Conference on Robotics and Automation.