基于MXene和pan的碳纤维增强双峰摩擦电传感器用于机械臂感知与控制。

IF 12.1 2区 材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Small Pub Date : 2025-06-16 DOI:10.1002/smll.202503393
Fangyang Dong, Qinghe Peng, Guang-An Yu, Hengxu Du, Weilu Sha, Peishuo Li, Yilin Liu, Hu Cai, Taili Du, Minyi Xu
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引用次数: 0

摘要

赋予机器人类似人类的感知和思维以适应日益增长的智能化仍然是一个挑战。本文提出了一种基于MXene和聚丙烯腈(PAN)的碳纤维增强双峰摩擦电传感器(MPBS),该传感器与商业机械臂集成,建立了一种新的感知和控制范式。通过掺杂MXene纳米片的功能层和pan基碳纤维电极,进一步提高了非接触式和触觉感知性能。当MXene增加2 wt.%时,MPBS的电输出增加了100%,实现了200 cm的非接触式传感范围和3.65 V cm -2的峰值输出比。将mpbs集成到灵活的手指中,开发了一种具有双峰感知能力的软爪。非接触式信号提供了对材料成分的宝贵见解,而触觉模式可以实现精确的形状识别,准确率达到99.4%。进一步集成的机械臂利用非接触式传感来自主探索物体,并在发生意外事件时运行控制动作。使用融合非接触式和触觉数据的卷积神经网络(CNN),以98.7%的准确率识别出10种物体的材料和形状。通过启用人工智能的机械臂系统,成功创建了用于物体检测、智能分拣和管道检查的多任务应用演示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

MXene and PAN-Based Carbon Fiber Enhanced Bimodal Triboelectric Sensor for Robotic Arm Perception and Control

MXene and PAN-Based Carbon Fiber Enhanced Bimodal Triboelectric Sensor for Robotic Arm Perception and Control

Endowing robots with human-like perception and thinking to match the growing intelligentization remains a challenge. Here, an MXene and polyacrylonitrile (PAN) based carbon fiber enhanced bimodal triboelectric sensor (MPBS) is proposed to integrate with a commercial robotic arm, establishing a novel paradigm for perception and control. The touchless and tactile perception performance are further improved by a functional layer doped with MXene nanosheets and electrodes composed of PAN-based carbon fibers. With 2 wt.% MXene, the MPBS electrical output increases by 100%, achieving a touchless sensing range of 200 cm and a peak output ratio of 3.65 V cm −2. Integrating MPBSs into flexible fingers, a soft gripper with bimodal perception capabilities is developed. The touchless signals provide valuable insights into material composition, whereas the tactile mode enables precise shape recognition with an accuracy of 99.4%. The further integrated robotic arm utilizes touchless sensing to autonomously explore objects and run control actions when unexpected events occur. 10 types of object materials and shapes are identified with 98.7% accuracy using a convolutional neural network (CNN) that fuses touchless and tactile data. Demonstration of multitask applications, through the AI-enabled robotic arm system, is successfully created for object detection, intelligent sorting, and pipeline inspection.

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来源期刊
Small
Small 工程技术-材料科学:综合
CiteScore
17.70
自引率
3.80%
发文量
1830
审稿时长
2.1 months
期刊介绍: Small serves as an exceptional platform for both experimental and theoretical studies in fundamental and applied interdisciplinary research at the nano- and microscale. The journal offers a compelling mix of peer-reviewed Research Articles, Reviews, Perspectives, and Comments. With a remarkable 2022 Journal Impact Factor of 13.3 (Journal Citation Reports from Clarivate Analytics, 2023), Small remains among the top multidisciplinary journals, covering a wide range of topics at the interface of materials science, chemistry, physics, engineering, medicine, and biology. Small's readership includes biochemists, biologists, biomedical scientists, chemists, engineers, information technologists, materials scientists, physicists, and theoreticians alike.
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