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 find contacts using an object slicing technique and use 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. 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 shape; Most importantly, it can be applied on point clouds taken using depth sensor; it takes into account gripper kinematic constraints and generates feasible grasps for both adaptive/enveloping and fingertip types of grasps.

A novel object slicing-based grasp planner for unknown 3D objects

“…This paper is an extension of our previous work [8]. The following are the major differences, improvements and extensions from the previous version.…”

“…The following are the major differences, improvements and extensions from the previous version. An automated object part-based pre-grasp generation has been introduced, where an object is decomposed into parts, and an appropriate type of grasp is applied on each part rather than on the object itself as in [8]. Unlike the previous version, this version can handle objects represented as point clouds as well as polygonal meshes.…”

Research on two-stage grasping planning method for multifingered dexterous hand

“…To generate the candidate grasping solutions, the dexterous hand workspace point clouds need to be distributed around the object to be grasped in a certain pose to obtain as many feasible grasping solutions as possible. The shape primitive method is an effective method for dexterous hand pose distribution (Miller et al , 2003; Sainul et al , 2019). To better cover as many grasping solutions as possible and reduce computational time, we directly adopt spherical distribution to place virtual dexterous hands of different poses.…”