A novel object slicing based grasp planner for 3D object grasping using underactuated robot gripper

Abstract

Robotic grasping of arbitrary objects even in completely known environments still remains a challenging problem. Most previously developed algorithms had focused on fingertip grasp, failing to solve the problem even for fully actuated hands/grippers during adaptive/wrapping type of grasps, where each finger makes contact with object at several points. Kinematic closed form solutions are not possible for such an articulated finger which simultaneously reaches several given goal points. This paper, presents a framework for computing best grasp for an underactuated robotic gripper, based on a novel object slicing method. The proposed method quickly finds contacts using an object slicing technique and uses grasp quality measure to find the best grasp from a pool of grasps. To validate the proposed method, implementation has been done on twenty-four household objects and toys using a two finger underactuated robot gripper on simulated environment. Unlike the many other existing approaches, the proposed approach has several advantages: it can handle objects with complex shapes and sizes; it does not require simplifying the objects into primitive geometric shapes; Most importantly, it can be applied on point clouds taken using depth sensors; it takes into account gripper kinematic constraints and generates feasible grasps for both adaptive/enveloping and fingertip types of grasps.