A Deep-Sea Sensorized Soft Gripper With Force Estimation and Interactive Capabilities

Collecting biological specimens and cultural artifacts is a focal point in deep-sea exploration. However, these deepsea collection tasks require a soft touch with sensing capacity. In this article, we propose a design and force modeling method for nitinol-embedded soft fingers embedded with a flexible bending sensor. For modeling the soft fingers, we provide an analytical solution for the relationship between the angular displacement and the load of the soft fingers. Furthermore, we employed flex sensors to measure the curvature of the fingers and established the relationship between the deformation change of the finger and the resistance. In the pressure chamber, we observed that the deformation of the finger and the resistance of the flex sensor showed no significant changes with the increase in hydrostatic pressure. By balancing the internal and external pressure differences through flexible hoses, we achieved force estimation and visual feedback using an array of light emitting diodes (LEDs) mounted on the soft gripper system. The submersible pivot performed a series of operations in the deep sea (at a depth of 1384 m) using the soft gripper system with a force prediction resolution of 3N, including collecting porcelain samples, dropping sandbags, and biological sampling. The visual feedback from the LEDs strengthened the submarine crew’s judgment while interacting with deepsea soft-bodied animals and fragile cultural artifacts during the operation. Our research provides a new approach for designing future deep-sea soft robotic grippers with force feedback and interaction capacities.

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Publication Details

Journal
IEEE/ASME Transactions on Mechatronics
Published
2025-06-13
DOI
https://doi.org/10.1109/tmech.2025.3575248
Citations
1
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
1.21

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article

A Deep-Sea Sensorized Soft Gripper With Force Estimation and Interactive Capabilities

Li Wen, Xia He, Haoxuan Wang, Ke Zhang et al.
1 citations
IEEE/ASME Transactions on Mechatronics
Robot Manipulation and Learning
1.21
article

A Deep-Sea Sensorized Soft Gripper With Force Estimation and Interactive Capabilities

Li Wen, Xia He, Haoxuan Wang, Ke Zhang, Youning Duo, Zhuyin Shao, Zonghao Zuo, Jiaqi Liu, Q. Zhang
article en
1 citations

Abstract

Collecting biological specimens and cultural artifacts is a focal point in deep-sea exploration. However, these deepsea collection tasks require a soft touch with sensing capacity. In this article, we propose a design and force modeling method for nitinol-embedded soft fingers embedded with a flexible bending sensor. For modeling the soft fingers, we provide an analytical solution for the relationship between the angular displacement and the load of the soft fingers. Furthermore, we employed flex sensors to measure the curvature of the fingers and established the relationship between the deformation change of the finger and the resistance. In the pressure chamber, we observed that the deformation of the finger and the resistance of the flex sensor showed no significant changes with the increase in hydrostatic pressure. By balancing the internal and external pressure differences through flexible hoses, we achieved force estimation and visual feedback using an array of light emitting diodes (LEDs) mounted on the soft gripper system. The submersible pivot performed a series of operations in the deep sea (at a depth of 1384 m) using the soft gripper system with a force prediction resolution of 3N, including collecting porcelain samples, dropping sandbags, and biological sampling. The visual feedback from the LEDs strengthened the submarine crew’s judgment while interacting with deepsea soft-bodied animals and fragile cultural artifacts during the operation. Our research provides a new approach for designing future deep-sea soft robotic grippers with force feedback and interaction capacities.

IEEE/ASME Transactions on MechatronicsVol. 30(6)
Chinese Academy of Sciences (CN), Institute of Deep-Sea Science and Engineering (CN), Beihang University (CN)
Chinese Academy of Sciences
Life below water
Openalex Percentile: Top 20%
Robot Manipulation and Learning
1.21
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