Xue Yuhang, Duan Jun, Liu Wenjing, Jin Zihan, Deng Shenhao, Huang Liang, Qian Jingui
Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Optoelectronics Engineering, Hefei University of Technology, Hefei 230009, China.
ACS Appl Mater Interfaces. 2024 Aug 14;16(32):42242-42253. doi: 10.1021/acsami.4c08624. Epub 2024 Aug 5.
A multiple self-powered sensor-integrated mobile manipulator (MSIMM) system was proposed to address challenges in existing exploration devices, such as the need for a constant energy supply, limited variety of sensed information, and difficult human-computer interfaces. The MSIMM system integrates triboelectric nanogenerator (TENG)-based self-powered sensors, a bionic manipulator, and wireless gesture control, enhancing sensor data usability through machine learning. Specifically, the system includes a tracked vehicle platform carrying the manipulator and electronics, including a storage battery and a microcontroller unit (MCU). An integrated sensor glove and terminal application (APP) enable intuitive manipulator control, improving human-computer interaction. The system responds to and analyzes various environmental stimuli, including the droplet and fall height, temperature, pressure, material type, angles, angular velocity direction, and acceleration amplitude and direction. The manipulator, fabricated using 3D printing technology, integrates multiple sensors that generate electrical signals through the triboelectric effect of mechanical motion. These signals are classified using convolutional neural networks for accurate environmental monitoring. Our database shows signal recognition and classification accuracy exceeding 94%, with specific accuracies of 100% for pressure sensors, 99.55% for angle sensors, and 98.66, 95.91, 96.27, and 94.13% for material, droplet, temperature, and acceleration sensors, respectively.
为应对现有探测设备面临的挑战，如需要持续能源供应、传感信息种类有限以及人机界面困难等问题，提出了一种多自供电传感器集成移动操纵器（MSIMM）系统。该MSIMM系统集成了基于摩擦纳米发电机（TENG）的自供电传感器、仿生操纵器和无线手势控制，通过机器学习提高传感器数据的可用性。具体而言，该系统包括一个承载操纵器和电子设备的履带式车辆平台，电子设备包括一个蓄电池和一个微控制器单元（MCU）。集成的传感器手套和终端应用程序（APP）实现了直观的操纵器控制，改善了人机交互。该系统能够响应和分析各种环境刺激，包括液滴和下落高度、温度、压力、材料类型、角度、角速度方向以及加速度幅度和方向。使用3D打印技术制造的操纵器集成了多个传感器，这些传感器通过机械运动的摩擦电效应产生电信号。利用卷积神经网络对这些信号进行分类，以实现精确的环境监测。我们的数据库显示信号识别和分类准确率超过94%，其中压力传感器的准确率为100%，角度传感器为99.55%，材料、液滴、温度和加速度传感器的准确率分别为98.66%、95.91%、96.27%和94.13%。
