Dynamic regrasping by in-hand orienting of grasped objects using non-dexterous robotic grippers. (April 2018)

• Record Type: Journal Article
• Authors: Sintov, Avishai; Shapiro, Amir
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 50(2018)
• Page Start: 114
• Page End: 131
• Publication Date: 2018-04
• Subjects: Dynamic regrasping -- Motion planning -- Kinodynamic constraints -- Trajectory optimization; 00-01 -- 99-00; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2017.09.009
• Languages: English
• ISSNs: 0736-5845

Abstract

Highlights: A method for in-hand dynamic regrasping using a non-dexterous gripper. Stochastic motion planning algorithm with trajectory optimization. The algorithm optimizes the initial pose of the regrasp and control gains. Simulations and experiments validate the proposed algorithm. Abstract: Almost any task on an object requires regrasping of the object prior to performing an intended task by varying between grasp configurations. The human hand uses many methods to perform regrasping manipulations such as in-hand sliding, finger gaiting, juggling, picking and placing, etc. The most complex and inspiring approach is the in-hand orienting dynamic regrasping where an object is released into mid air and regrasped in a different orientation. This manipulation is useful in industrial robotics for rapid manufacturing and reducing the number of robotic arms in production lines. In this work, we present a novel approach for performing in-hand orienting regrasping using computed torque control. To maintain an efficient and low-cost approach, the regrasping is performed using a non-dexterous two-jaw gripper and by utilizing the robotic arm's dynamics. We focus on the motion planning for the motion and propose a novel stochastic algorithm for performing an optimal manipulation satisfying the kinematic and dynamic constraints. The algorithm optimizes the initial pose of the arm and the control gains. Statistical analysis shows the probability for the algorithm to find a solution if such exists. Simulations on a KUKA arm and demonstration on a planar 3R arm validate the feasibility of the proposed regrasping approach and planning algorithm.