A Deep-Sea Sensorized Soft Gripper with Force Estimation and Interactive Capabilities
IEEE/ASME Transactions on Mechatronics（2025）
School of Mechanical Engineering and Automation | Institute of Deep-sea Science and Engineering
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.
MoreTranslated text
Key words
Deep-sea grasping,force estimation,interactive gripping,soft robotics
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper