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primary topic: End-to-end emergency response protocol for tunnel accidents augmentation with reinforcement learning

End-to-end emergency response protocol for tunnel accidents augmentation with reinforcement learning
type: AcademicArticle
type: Article
abstract: Autonomous unmanned aerial vehicles (UAVs) offer cost-effective and flexible solutions for a wide range of real-world applications, particularly in hazardous and time-critical environments. Their ability to navigate autonomously, communicate rapidly, and avoid collisions makes UAVs well suited for emergency response scenarios. However, real-time path planning in dynamic and unpredictable environments remains a major challenge, especially in confined tunnel infrastructures where accidents may trigger fires, smoke propagation, debris, and rapid environmental changes. In such conditions, conventional preplanned or model-based navigation approaches often fail due to limited visibility, narrow passages, and the absence of reliable localization signals. To address these challenges, this work proposes an end-to-end emergency response framework for tunnel accidents based on Multi-Agent Reinforcement Learning (MARL). Each UAV operates as an independent learning agent using an Independent Q-Learning paradigm, enabling real-time decision-making under limited computational resources. To mitigate premature convergence and local optima during exploration, Grey Wolf Optimization (GWO) is integrated as a policy-guidance mechanism within the reinforcement learning (RL) framework. A customized reward function is designed to prioritize victim discovery, penalize unsafe behavior, and explicitly discourage redundant exploration among agents. The proposed approach is evaluated using a frontier-based exploration simulator under both single-agent and multi-agent settings with multiple goals. Extensive simulation results demonstrate that the proposed framework achieves faster goal discovery, improved map coverage, and reduced rescue time compared to state-of-the-art GWO-based exploration and random search algorithms. These results highlight the effectiveness of lightweight MARL-based coordination for autonomous UAV-assisted tunnel emergency response.
date: 2026-01
is part of: Scientific Reports
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: End-to-end emergency response protocol for tunnel accidents augmentation with reinforcement learning (Texto)
has document: End-to-end emergency response protocol for tunnel accidents augmentation with reinforcement learning (Otro)
subject: Engineering

End-to-end emergency response protocol for tunnel accidents augmentation with reinforcement learning (Texto)
type: Document
has file: s41598-026-37191-w_reference.pdf
has part: s41598-026-37191-w_reference.pdf

Scientific Reports
type: Collection
issn: 20452322

Yini Airet Miró Vera
type: Person
given name: Yini Airet
family name: Miró Vera

Roberto Marcelo Álvarez
type: Person
given name: Roberto Marcelo
family name: Álvarez

M. Junaid Gul
type: Person
given name: M. Junaid
family name: Gul

Muhammad Zeeshan Jhandir
type: Person
given name: Muhammad Zeeshan
family name: Jhandir

Rabbiya Younas
type: Person
given name: Rabbiya
family name: Younas

Hafiz Muhammad Raza ur Rehman
type: Person
given name: Hafiz Muhammad Raza
family name: ur Rehman

Imran Ashraf
type: Person
given name: Imran
family name: Ashraf

End-to-end emergency response protocol for tunnel accidents augmentation with reinforcement learning (Otro)
type: Document
is version of: End-to-end emergency response protocol for tunnel accidents augmentation with reinforcement learning (Texto)
is volatile version of: End-to-end emergency response protocol for tunnel accidents augmentation with reinforcement learning (Texto)
is index codes version of: End-to-end emergency response protocol for tunnel accidents augmentation with reinforcement learning (Texto)
has file: indexcodes.txt
has part: indexcodes.txt

HTML Summary of #27154 End-to-end emergency response protocol for tunnel accidents augmentation with reinforcement learning
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primary topic: End-to-end emergency response protocol for tunnel accidents augmentation with reinforcement learning

Engineering
type: Concept
preferred label: Ingeniería @es