3D Force Sensor-Based Multimodal Tactile Sensing for Underwater Robotic Adaptive Grasping
Yuchao Liu, Yibin Chen, Zijie Liu, Haihong Qin, Long Ren, Weipeng Li, Xuan Wu, Jiajie Guo
Abstract
Underwater tactile sensing is critical for marine robots to reliably manipulate objects. However, harsh underwater environments bring in serious disturbances to sensor techniques. Prior studies have typically been restricted to on-land scenarios, 1D force measurements, or qualitative object property analyses. To solve these limitations, this paper develops an underwater multimodal tactile sensing system, which can simultaneously capture 3D forces and quantitatively analyze three object properties: (1) Texture discrimination to determine grasping necessity; (2) Stiffness recognition to determine the maximum grasping force to avoid object excessive deformation; (3) Static friction coefficient quantification to derive the minimum anti-slip grasping force. The proposed method was rigorously validated by extensive underwater grasping experiments. By leveraging the tactile feed-back for closed-loop control, the system endows robotic grippers with non-destructive and anti-slip underwater grasping abilities, which is anticipated to promise benefits for marine robots.
BibTeX
@inproceedings{ral2026_3dforcesensorbas,
title = {3D Force Sensor-Based Multimodal Tactile Sensing for Underwater Robotic Adaptive Grasping},
author = {Yuchao Liu and Yibin Chen and Zijie Liu and Haihong Qin and Long Ren and Weipeng Li and Xuan Wu and Jiajie Guo},
booktitle = {RA-L 2026},
year = {2026}
}