Unknown object grasping using force balance exploration on a partial point cloud

Reducing the computing time for unknown object grasping while maintaining grasp stability is the goal of this paper. Inspired by the camera sensor distribution of the PR2 and Baxter robots, as well as active exploration for unknown object grasping, a novel unknown object grasping algorithm is proposed. This algorithm is based on two 3D sensors distributed like the PR2 and Baxter robots. Using the inputs from the two 3D sensors, a partial point cloud is constructed. Series of virtual viewpoints are allocated at intervals surround the principal component axis to build interval virtual object coordinate systems, from which force balance computation is carried out. The force balance is examined both in the XOY plane and the XOZ plane to guarantee the grasping stability. The hand configuration with the best force balance is returned as the final grasp configuration. Simulations based on a Universal robot arm and a Lacquey fetch gripper demonstrated favorable performance. Our algorithm can quickly process the partial point cloud and output the final grasp within 1 or 2 seconds (varying according to the point sets). The simulations demonstrated the effectiveness of our grasping algorithm.

[1]  Gary M. Bone,et al.  Automated modeling and robotic grasping of unknown three-dimensional objects , 2008, 2008 IEEE International Conference on Robotics and Automation.

[2]  Sang-Ryong Lee,et al.  3D optimal determination of grasping points with whole geometrical modeling for unknown objects , 2003 .

[3]  Kimitoshi Yamazaki,et al.  Picking up an Unknown Object through Autonomous Modeling and Grasp Planning by a Mobile Manipulator , 2007, FSR.

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

[5]  Matei T. Ciocarlie,et al.  Data-driven grasping with partial sensor data , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Matei T. Ciocarlie,et al.  Contact-reactive grasping of objects with partial shape information , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Matei T. Ciocarlie,et al.  The Columbia grasp database , 2009, 2009 IEEE International Conference on Robotics and Automation.

[8]  Ashutosh Saxena,et al.  Reactive grasping using optical proximity sensors , 2009, 2009 IEEE International Conference on Robotics and Automation.

[9]  Siddhartha S. Srinivasa,et al.  Object recognition and full pose registration from a single image for robotic manipulation , 2009, 2009 IEEE International Conference on Robotics and Automation.

[10]  Peter K. Allen,et al.  Semantic grasping: Planning robotic grasps functionally suitable for an object manipulation task , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Rachid Alami,et al.  A Grasp Planner Based On Inertial Properties , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[12]  Sukhan Lee,et al.  Compliant physical interaction based on external vision-force control and tactile-force combination , 2008, 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems.

[13]  Martijn Wisse,et al.  Grasping of unknown objects via curvature maximization using active vision , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Danica Kragic,et al.  Selection of robot pre-grasps using box-based shape approximation , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Robert Platt,et al.  Nullspace composition of control laws for grasping , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[16]  Javier Felip,et al.  Robust sensor-based grasp primitive for a three-finger robot hand , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  Martijn Wisse,et al.  Fast grasping of unknown objects using force balance optimization , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.