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.

翻译：多吸盘抓取可以提高杂乱场景下物品识别和捡拾的效率。在本文中，我们提出了一种真空吸盘抓手的抓取计划器，利用多个吸盘同时抓取多个物体或一个具有大面积的物体。为了解决在抓取时需要确定抓取位置和激活哪些吸盘的挑战，我们使用三维卷积将神经网络推断出的可操作区域与抓手核进行卷积，以确定采样抓手方向的可抓取位置。本工作使用的3D卷积核包括吸盘ID信息的编码，可通过解码卷积结果来直接确定激活哪些吸盘。此外，提出了一种排序算法来在候选项中找到最优的抓取。我们的计划器表现出良好的普适性，成功地在先前的可操作性映射数据集中找到多吸盘的抓取。我们的计划器在物理机器人捡拾实验中表现出了使用多个吸盘提高拾取效率的特点。与单物体（单吸盘）抓取相比，多吸盘抓取分别为拾取箱子、水果和日用品的效率增加了1.45x、1.65x和1.16x。