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Multiple Self-Powered Sensor-Integrated Mobile Manipulator for Intelligent Environment Detection.

Yuhang Xue1, Jun Duan1, Wenjing Liu1

  • 1Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Optoelectronics Engineering, Hefei University of Technology, Hefei 230009, China.

ACS Applied Materials & Interfaces
|August 5, 2024
PubMed
Summary

This study introduces a self-powered mobile manipulator with integrated sensors for exploration. It uses machine learning for accurate environmental sensing and intuitive gesture control, overcoming energy and interface limitations.

Keywords:
TENG-based self-powered sensorsenvironmental information collectionmachine learning-assisted monitoring systemmultiple-sensor systemwireless gesture control

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Area of Science:

  • Robotics and Automation
  • Sensor Technology
  • Energy Harvesting

Background:

  • Existing exploration devices face limitations in continuous power, diverse sensing, and user-friendly interfaces.
  • The need for autonomous and adaptable systems in challenging environments is increasing.

Purpose of the Study:

  • To develop a self-powered mobile manipulator system (MSIMM) that addresses limitations of current exploration devices.
  • To enhance sensor data usability and human-computer interaction for mobile exploration platforms.

Main Methods:

  • Integration of triboelectric nanogenerator (TENG)-based self-powered sensors with a bionic manipulator and wireless gesture control.
  • Utilizing a tracked vehicle platform, sensor glove, and mobile application for intuitive control and data acquisition.
  • Employing machine learning, specifically convolutional neural networks, for signal classification and environmental monitoring.

Main Results:

  • Achieved overall signal recognition and classification accuracy exceeding 94% for environmental stimuli.
  • Demonstrated high accuracy for individual sensors: 100% for pressure, 99.55% for angle, and over 94% for material, droplet, temperature, and acceleration sensors.
  • The MSIMM system effectively integrates self-powered sensing, robotic manipulation, and intuitive control for enhanced exploration capabilities.

Conclusions:

  • The proposed MSIMM system offers a viable solution for energy-independent and versatile environmental exploration.
  • The combination of TENG sensors, machine learning, and gesture control significantly improves the performance and usability of mobile exploration robots.
  • This technology has the potential to advance autonomous sensing and interaction in various fields, including environmental monitoring and hazardous site exploration.