  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.
