Integrated Air–Ground Robotic System for Autonomous Post-Blast Operations in GNSS-Denied Tunnels

Post-blast operations in tunnel construction represent a critical bottleneck due to mandatory downtime and hazardous environmental conditions. This study addresses these challenges by developing and validating an integrated cyber–physical architecture that coordinates an autonomous Unmanned Aerial Vehicle (UAV) and an Autonomous Wheel Loader (AWL) under the supervision of a Digital Twin acting as central operational digital interface. Specifically, this technology was designed to access the tunnel, evaluate post-blasting conditions, and initiate operations during mandatory exclusion periods for personnel. The system was validated in a realistic, Global Navigation Satellite System (GNSS)-denied tunnel environment emulating post-detonation visibility constraints. The results demonstrate that the aerial agent successfully navigated and mapped the excavation front in less than 8 min, establishing a shared coordinate system for the ground machinery. Through this collaborative workflow, the autonomous deployment enabled operations to commence 50% to 80% earlier than conventional manual procedures. Furthermore, the system reduced daily operational time by approximately 8%, with an estimated return on financial investment between one and seven months. Overall, the proposed framework eliminates human exposure during high-risk inspections and transforms the fragmented excavation cycle into a continuous, data-driven process.

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Publication Details

Journal
Remote Sensing
Published
2026-04-10
DOI
https://doi.org/10.3390/rs18081133
Primary Topic
Occupational Health and Safety Research
Type
article
Field-Weighted Citation Impact
0.00

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article

Integrated Air–Ground Robotic System for Autonomous Post-Blast Operations in GNSS-Denied Tunnels

J. German Fernández, Francisco J. Pérez-Grau, Antonio Alonso-Cepeda, María T. Lázaro et al.
Remote Sensing
Occupational Health and Safety Research
article

Integrated Air–Ground Robotic System for Autonomous Post-Blast Operations in GNSS-Denied Tunnels

J. German Fernández, Francisco J. Pérez-Grau, Antonio Alonso-Cepeda, María T. Lázaro, Rafael Herguedas, Goretti Arias-Ferreiro, Sergio Noriega-del-Rivero, Amaia Castelruiz-Aguirre, Marco A. Montes-Grova, Mustafa Karahan
article en

Abstract

Post-blast operations in tunnel construction represent a critical bottleneck due to mandatory downtime and hazardous environmental conditions. This study addresses these challenges by developing and validating an integrated cyber–physical architecture that coordinates an autonomous Unmanned Aerial Vehicle (UAV) and an Autonomous Wheel Loader (AWL) under the supervision of a Digital Twin acting as central operational digital interface. Specifically, this technology was designed to access the tunnel, evaluate post-blasting conditions, and initiate operations during mandatory exclusion periods for personnel. The system was validated in a realistic, Global Navigation Satellite System (GNSS)-denied tunnel environment emulating post-detonation visibility constraints. The results demonstrate that the aerial agent successfully navigated and mapped the excavation front in less than 8 min, establishing a shared coordinate system for the ground machinery. Through this collaborative workflow, the autonomous deployment enabled operations to commence 50% to 80% earlier than conventional manual procedures. Furthermore, the system reduced daily operational time by approximately 8%, with an estimated return on financial investment between one and seven months. Overall, the proposed framework eliminates human exposure during high-risk inspections and transforms the fragmented excavation cycle into a continuous, data-driven process.

Remote SensingVol. 18(8)
Small and Medium Enterprises Development Organization (TR), Fundación Agustín de Betancourt (ES), Tecnalia (ES), Center for Advanced Aerospace Technologies (ES), Instituto Tecnológico de Aragón (ES), Acciona (Spain) (ES)
European Health and Digital Executive Agency
Openalex Percentile: Top 26%
Occupational Health and Safety Research
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