Resumen
Mine emergencies demand rapid and informed decision-making under extreme conditions, often placing personnel in life-threatening situations. Robotic assistance offers the potential to reduce unnecessary human exposure during such operations. This study examines the specific informational needs and communication preferences of mine rescue personnel for designing robotic systems for underground emergency response. A semi-structured interview was developed and conducted with ten mine rescue personnel and subject matter experts (SMEs). Responses were analyzed using thematic analysis and compared with established cognitive models to derive key design recommendations. Drawing on both field experience and hypothetical rescue scenarios, participants provided insights into key functional aspects of robotic systems, including mapping and navigation, gas detection and environmental monitoring, communication capabilities, system reliability, control, and the robot’s specific roles during operations. The qualitative data was transcribed and analyzed to identify recurring themes and critical user guidelines. The findings revealed insights into the informational and interface recommendations of rescue teams, particularly the need for real-time situational data and customizable human–robot interfaces tailored to emergency scenarios. These results expose key deficiencies in the current human–robot interaction systems and offer actionable guidance for designing robotic technologies that better align with the operational needs of experienced responders. The outcomes of this study can serve as practical guidelines for developing effective interfaces to support underground mine rescue missions.
| Idioma original | English |
|---|---|
| Número de artículo | 1698570 |
| Número de páginas | 17 |
| Publicación | Frontiers in Robotics and AI |
| Volumen | 13 |
| DOI | |
| Estado | Published - 2026 |
Nota bibliográfica
Publisher Copyright:Copyright © 2026 Bakzadeh, Alhaj-Bedar, Wilson, Androulakis, Khaniani, Shao, Hassanalian and Roghanchi.
Financiación
The author(s) declared that financial support was received for this work and/or its publication. This study was funded by the National Institute for Occupational Safety and Health (NIOSH), United States, under the award #U60OH012351. The views, opinions, and recommendations expressed herein are solely those of the authors and do not necessarily reflect the views of NIOSH. Mentions of trade names, commercial products, or organizations do not imply endorsement by the authors nor the funding organization.
| Financiadores | Número del financiador |
|---|---|
| National Institute for Occupational Safety and Health | U60OH012351 |
ASJC Scopus subject areas
- Computer Science Applications
- Artificial Intelligence
Huella
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