Multi-End Effector Selection by Depth-Aware Convolution

In this paper, we propose a method for selecting end-effectors based on depth images for a robot that performs picking tasks using multiple end-effectors. The proposed method evaluates the graspability of each end-effector in a scene by convolving a hand model, represented as a two-dimensional binary structure, with the depth image of the target scene. A key feature of the method is that it requires no pre-training and does not rely on object or environmental models, operating solely with simple models of the end-effectors. In picking experiments involving eight types of electronic components commonly used in factory automation, the proposed method effectively alternated between suction and two-finger grippers. Compared to other training-free end-effector selection methods and approaches using a single end-effector, the proposed method demonstrated an improvement of over 14% in grasp success rate compared to the second-best method.