Intelligent Soft Quasi‐Organism Equipped with Sensor‐Driven Integrated Tentacles

Author:

Liu Chang12ORCID,Luo Jinan12,Liu Haidong12,Fu Junxin12,Liu Houfang3,Tang Hao12,Deng Zhikang12,Wu Jingzhi12,Li Yuanfang12,Liu Chuting12,Peng Shiqi12,Hu Juxin12,Ren Tian‐Ling3ORCID,Zhou Jianhua12ORCID,Qiao Yancong12ORCID

Affiliation:

1. School of Biomedical Engineering Shenzhen Campus of Sun Yat‐sen University Shenzhen 518107 P. R. China

2. Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering Sun Yat‐sen University Guangzhou 510275 P. R. China

3. School of Integrated Circuits and Beijing National Research Center for Information Science and Technology (BNRist) Tsinghua University Beijing 100084 P. R. China

Abstract

AbstractIntegration of soft electronic system components is critical to create a closed‐loop system that seamlessly integrates sensing, driving, processing, and autonomous control capabilities. However, the existence of these components, particularly sensors and actuators, in isolated or discrete forms complicates the endeavor to achieve seamless interface matching and in situ integration, no more than the autonomic system. Here, an intelligent soft quasi‐organism (SQO) equipped with sensor‐driven integrated tentacles is demonstrated, featuring real‐time state recognition and autonomous object recognition inspired by the sea anemones. By employing a heterogeneous mechanisms homotopic integration (HMHI) strategy, the tentacles of the SQO possess the unique ability to simultaneously perceive state changes and flexibly drive motions, utilizing the same material and structure. The sea anemone‐shaped SQO exhibits real‐time autonomous state identification capabilities through the integration of machine learning and customized circuits, attaining 100% recognition accuracy across sixteen states. With a neuromuscular system that facilitates active autonomous perception, the anemone‐shaped SQO can recognize and intelligently grasp static objects with an accuracy of 80.7%, surpassing that of a human hand (74.7%). The SQO provides a promising approach for realizing artificial neuromuscular systems, with great potential for applications in crucial areas such as intelligent soft robotics and in vivo therapy.

Funder

National Natural Science Foundation of China

Basic and Applied Basic Research Foundation of Guangdong Province

Shenzhen Science and Technology Innovation Program

Beijing National Research Center For Information Science And Technology

Publisher

Wiley

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