Recognition and Grasping of Target Position and Pose of Manipulator Based on Vision

In this paper, we mainly study the vision-based pose estimation and manipulator grasping control and demonstrate it through experiments. The attitude estimation is based on the geometric characteristics of the target object and the imaging principle of the camera. Based on the correspondence between two and three dimensions of the feature points, the pose estimation of the target object is performed. Then, under the premise of the known object's attitude, in view of how to grasp the target object, this paper designs a grasping strategy based on the latitude and longitude positioning method. Finally, the strategy is applied to the grasp of the manipulator, and the effectiveness of the strategy is verified by the grasping results.

[1]  Jonathan Bohren,et al.  The SMACH High-Level Executive [ROS News] , 2010 .

[2]  Vincent Lepetit,et al.  Monocular Model-Based 3D Tracking of Rigid Objects: A Survey , 2005, Found. Trends Comput. Graph. Vis..

[3]  Xiangyang Zhu,et al.  Planning force-closure grasps on 3-D objects , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[4]  Daniela Rus,et al.  Autonomous Object Manipulation Using a Soft Planar Grasping Manipulator , 2015, Soft robotics.

[5]  Gerd Hirzinger,et al.  Calculating hand configurations for precision and pinch grasps , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  John F. Canny,et al.  Planning optimal grasps , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[7]  Larry S. Davis,et al.  Model-based object pose in 25 lines of code , 1992, International Journal of Computer Vision.

[8]  Maxim Likhachev,et al.  Planning Single-Arm Manipulations with N-Arm Robots , 2014, SOCS.

[9]  Éric Marchand,et al.  Real-time Hybrid Tracking using Edge and Texture Information , 2007, Int. J. Robotics Res..

[10]  Elena Cabrio,et al.  Towards Lifelong Object Learning by Integrating Situated Robot Perception and Semantic Web Mining , 2016, ECAI.

[11]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[12]  Éric Marchand,et al.  Real-time markerless tracking for augmented reality: the virtual visual servoing framework , 2006, IEEE Transactions on Visualization and Computer Graphics.

[13]  Cui Hongxia,et al.  Position and Pose Estimation of Spherical Panoramic Image with Improved EPnP Algorithm , 2016 .

[14]  John F. Canny,et al.  Easily computable optimum grasps in 2-D and 3-D , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[15]  Marcello Pellicciari,et al.  A workcell calibration method for enhancing accuracy in robot machining of aerospace parts , 2016 .

[16]  Bolan Jiang.  Calibration-free Line-based Tracking for Video Augmentation , 2006, CGVR.

[17]  P. Ganesan,et al.  International Conference on Recent Trends in Computing 2015 ( ICRTC-2015 ) Comparative Study of Skin Color Detection and Segmentation in HSV and YCbCr Color Space , 2015 .

[18]  Sriparna Saha,et al.  EEG-Gesture Based Artificial Limb Movement for Rehabilitative Applications , 2018 .

[19]  Nassir Navab,et al.  A Unified Approach Combining Photometric and Geometric Information for Pose Estimation , 2008, BMVC.