Journal of Robotics and Mechatronics
Online ISSN : 1883-8049
Print ISSN : 0915-3942
ISSN-L : 0915-3942
Special Issue on Industrial Robotics and Systems
Multi-End Effector Selection by Depth-Aware Convolution
Yukiyasu Domae
Author information
JOURNAL OPEN ACCESS

2025 Volume 37 Issue 2 Pages 348-355

Details
Abstract

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.

Content from these authors

This article cannot obtain the latest cited-by information.

© 2025 Fuji Technology Press Ltd.

This article is licensed under a Creative Commons [Attribution-NoDerivatives 4.0 International] license (https://creativecommons.org/licenses/by-nd/4.0/).
The journal is fully Open Access under Creative Commons licenses and all articles are free to access at JRM official website.
https://www.fujipress.jp/jrobomech/rb-about/#https://creativecommons.org/licenses/by-nd
Previous article Next article
feedback
Top