通过指内视觉重建柔软机器人触感

IF 6.8 Q1 AUTOMATION & CONTROL SYSTEMS Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-10-15 DOI:10.1002/aisy.202470045

Ning Guo, Xudong Han, Shuqiao Zhong, Zhiyuan Zhou, Jian Lin, Fang Wan, Chaoyang Song

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摘要

通过指内视觉重建软体机器人触感万芳、宋朝阳及合作者的研究（见文章编号 2400022）介绍了一种基于视觉的方法，用于学习使用软体机器人超材料（SRM）的本体感觉交互。通过重建物理接触过程中的形状和触感，作者实现了在虚拟环境中对软手指网格变形的实时、精确估计。这一创新增强了三维互动的适应性，并为人机协作和基于触摸的数字孪生互动带来了广阔的应用前景，通过多模态软触摸在物理世界和虚拟世界之间架起了桥梁。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Reconstructing Soft Robotic Touch via In-Finger Vision

Reconstructing Soft Robotic Touch via In-Finger Vision

The research by Fang Wan, Chaoyang Song, and co-workers (see article number 2400022) introduces a vision-based approach for learning proprioceptive interactions using Soft Robotic Metamaterials (SRMs). By reconstructing shape and touch during physical engagements, the authors achieve real-time, precise estimations of the soft finger mesh deformation in virtual environments. This innovation enhances the adaptability in 3D interactions and suggests promising applications in human–robot collaboration and touch-based digital twin interactions, bridging the gap between physical and virtual worlds via a multi-modal soft touch.

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