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Related Concept Videos

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Related Experiment Video

Updated: May 2, 2026

Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect
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Particulate matter source localization in dynamic indoor environments: Bridging simulation-experimentation gaps with

Hongyi Mao1, Xun Guo2, Jiamin Qiu1

  • 1Department of HVAC, College of Urban Construction, Nanjing Tech University, Nanjing 210009, PR China.

Journal of Hazardous Materials
|February 7, 2025
PubMed
Summary

This study developed a multi-robot system for detecting indoor particulate matter (PM) in dynamic environments. The improved whale optimization algorithm (IWOA_3D) effectively localized PM2.5 sources with a 73.3% success rate.

Keywords:
3D source localizationDynamic indoor environmentMulti-robot olfaction methodParticulate matter (PM)

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Area of Science:

  • Robotics and Environmental Monitoring
  • Air Quality and Pollution Control
  • Computational Intelligence

Background:

  • Indoor particulate matter (PM) poses significant health, product quality, and safety risks.
  • Dynamic indoor environments with unpredictable airflow complicate PM source localization.
  • Accurate PM source localization is crucial for effective mitigation strategies.

Purpose of the Study:

  • To develop and evaluate a multi-robot system for 3D indoor PM concentration detection.
  • To assess the performance of the improved whale optimization algorithm (IWOA) in dynamic environments.
  • To investigate the adaptability of IWOA_3D to various environmental and PM source parameters.

Main Methods:

  • A self-developed multi-robot system for 3D concentration detection was utilized.
  • 225 experiments were conducted across 15 cases in a dynamic ventilation environment.
  • The improved whale optimization algorithm (IWOA_3D) was compared against 2D scenarios and improved particle swarm optimization (IPSO).

Main Results:

  • IWOA_3D demonstrated strong adaptability to variations in PM size, release rate, source location, and accuracy standards.
  • The algorithm achieved a 73.3% success rate in localizing PM2.5 at 1.05 µm, even with varying source heights.
  • Performance was evaluated using success rate as the primary criterion in dynamic conditions.

Conclusions:

  • The developed multi-robot system effectively bridges the gap between simulation and real-world PM detection.
  • IWOA_3D proves practical and effective for localizing PM sources in challenging dynamic indoor environments.
  • Optimizing sensor readings and localization strategies is vital for indoor air quality management.