Multiple-object Grasping Using a Multiple-suction-cup Vacuum Gripper in Cluttered Scenes

TL;DR

This paper introduces a novel grasp planner for vacuum grippers with multiple suction cups, enabling efficient multi-object grasping in cluttered scenes by using neural network inference and 3D convolution techniques.

Contribution

It proposes a new grasp planning method utilizing 3D convolution and cup ID encoding to determine optimal multi-suction-cup grasps, improving efficiency in cluttered scene picking.

Findings

Successful multi-object grasping in datasets and real robot experiments

Achieved 1.45x to 1.65x increased picking efficiency over single-cup grasping

Demonstrated generality and effectiveness of the proposed planner

Abstract

Multiple-suction-cup grasping can improve the efficiency of bin picking in cluttered scenes. In this paper, we propose a grasp planner for a vacuum gripper to use multiple suction cups to simultaneously grasp multiple objects or an object with a large surface. To take on the challenge of determining where to grasp and which cups to activate when grasping, we used 3D convolution to convolve the affordable areas inferred by neural network with the gripper kernel in order to find graspable positions of sampled gripper orientations. The kernel used for 3D convolution in this work was encoded including cup ID information, which helps to directly determine which cups to activate by decoding the convolution results. Furthermore, a sorting algorithm is proposed to find the optimal grasp among the candidates. Our planner exhibited good generality and successfully found multiple-cup grasps in previous affordance map datasets. Our planner also exhibited improved picking efficiency using multiple suction cups in physical robot picking experiments. Compared with single-object (single-cup) grasping, multiple-cup grasping contributed to 1.45x, 1.65x, and 1.16x increases in efficiency for picking boxes, fruits, and daily necessities, respectively.