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<p>In the wake of catastrophic earthquakes, rescue operations encounter significant obstacles in locating and reaching individuals trapped under debris. This study introduces the Smart Earthquake Rescue Robot (SERR) prototype, a cutting-edge solution designed to enhance the efficiency and effectiveness of earthquake rescue missions. The SERR is a mobile robot with advanced features, including live video streaming through an integrated camera, Grid-Eye temperature detection, and provisions for communication via built-in speakers and microphones, although these audio communication capabilities are pending implementation in the current prototype. It can be remotely controlled using a smartphone application, offering a safer and more efficient method for conducting rescue operations. Unlike recent advances discussed in the literature, SERR uniquely combines visual, thermal, and audio data with a multi-modal Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) model (RescueNet), achieving high accuracy (0.94), precision (0.90), recall (0.96), and F1-score (0.92) in detecting survivors, as validated by MATLAB simulations using USGS-PAGER data. The SERR’s rapid runtime (35&#xa0;ms) highlights its promise as a tool to improve earthquake rescue outcomes.</p>

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Design and implementation of a smart earthquake rescue robot to enhance rescue operations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月27日

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  • Omar Khattab,
  • B. Saravana Balaji,
  • M. Omar Al-Kadri,
  • Amal Alsaeed,
  • Asmaa Alofaisan,
  • Joud Aboutamar,
  • Hooreyah Alharbi,
  • Abdullatif Shikfa

摘要

In the wake of catastrophic earthquakes, rescue operations encounter significant obstacles in locating and reaching individuals trapped under debris. This study introduces the Smart Earthquake Rescue Robot (SERR) prototype, a cutting-edge solution designed to enhance the efficiency and effectiveness of earthquake rescue missions. The SERR is a mobile robot with advanced features, including live video streaming through an integrated camera, Grid-Eye temperature detection, and provisions for communication via built-in speakers and microphones, although these audio communication capabilities are pending implementation in the current prototype. It can be remotely controlled using a smartphone application, offering a safer and more efficient method for conducting rescue operations. Unlike recent advances discussed in the literature, SERR uniquely combines visual, thermal, and audio data with a multi-modal Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) model (RescueNet), achieving high accuracy (0.94), precision (0.90), recall (0.96), and F1-score (0.92) in detecting survivors, as validated by MATLAB simulations using USGS-PAGER data. The SERR’s rapid runtime (35 ms) highlights its promise as a tool to improve earthquake rescue outcomes.