1 Motivation for the research topic
Hazards are expected to occur with increasing frequency, severity, or both (). Climate change is amplifying the occurrence of extreme events such as floods and wildfires. For instance, severe river floods are projected to more than triple in some regions by the end of the century (), while rising thermal anomalies are expanding wildfire-prone areas (). At the same time, the increasing exposure of urban areas and wildland-urban interfaces is significantly elevating risks to human life, critical infrastructure, and emergency accessibility (; ; ). In addition, non-climate-related disasters, such as earthquakes, volcanic eruptions, and industrial accidents, pose growing threats due to rising population density in vulnerable regions (; ). These developments underline the urgent need to strengthen disaster resilience, not only in terms of anticipation and prevention, but also in response to unavoidable events. In such scenarios, rapid and effective disaster response is critical to minimizing loss of life and limiting damage to essential infrastructure.
1.1 Unmanned systems for disaster resilience
Several previous publications have highlighted how unmanned systems can be used for disaster management. point out clearly the three stages in disaster management where Unmanned Aerial Vehicles (UAVs) can be of essential support in: 1) early warning through sensor-based remote sensing, 2) disaster assessment with real-time monitoring of the disaster area, and 3) disaster response by being the communication nodes in a Wireless Sensor Network (WSN) or by transporting first aid to impervious areas. report on a real-world deployment of a remote-controlled ground robot for risk assessment during fire at Notre Dame Cathedral. A more recent scoping review by focuses on field reports of real-world deployments of UAVs, analyzing studies and experiments conducted using real-world data. In most scenarios, UAVs were used for disaster assessment by delivering images or by mapping the disaster area, including also three-dimensional mapping. Damage assessment in impervious areas was identified as the main advantage of UAVs in disaster management. Besides, UAVs can locate missing person(s) in Search and Rescue (SAR) operations faster, especially in snow-covered terrains.
1.2 AI for disaster resilience
Alongside the increasing risks associated with disasters, advances in artificial intelligence (AI) are creating new opportunities to enhance disaster resilience. Beyond enabling robots to act as sensor payload carriers for situational awareness (), the integration of AI facilitates increased autonomy in decision-making. For example, prior work has demonstrated automated planning for complex rescue missions using unmanned autonomous platforms (; ). Such capabilities enable more effective operations, thereby improving operational efficiency without requiring additional human involvement in the decision-making loop.
Recent advancements in Large Language Models (LLMs) have also enable automated context extractions for disaster management (; ), but also for seamless disaster response, in which operators command unmanned assets in natural language through LLM-powered interfaces ().
2 Objectives of the special session
While robotics and AI were often Research Topic of scientific publications, targeted applications often vary. Often, the application in disaster relief plays only a marginal role, for example, being one of many example use cases for performance benchmarking. This special session addresses exactly recent advancements in robotics and AI in view of their use for increasing disaster resilience. Research Topic of interest include.
Novel sensor techniques and sensor fusion algorithms to be integrated on unmanned vehicles deployed for disaster response;
AI algorithms, frameworks, and systems for automated planning, sequential decision-making, multi-agent coordination etc. of unmanned vehicles in disaster areas;
Algorithms and methods for motion control of robots to be deployed in disaster areas (to overcome the physically challenging environment at a disaster site, e.g., uneven grounds due to debris for ground vehicles, stormy weather for aerial vehicles, etc.);
Collaborative capabilities for improved interaction of humans and unmanned vehicles in shared spaces;
Reporting on field validation tests for unmanned technologies in realistic environments and review articles.
2.1 Submissions
Accepted publications cover a wide spectrum of Research Topic. Yamauchi et al. focus on the design and development of an innovative robot demonstrator, namely, the 3.6 m long Dragon Firefighter (DFF), capable of extinguishing fire with onboard nozzles. The DFF has achieved stable manual flight, at the time the publication was submitted. In contrast, Tamura and Kamegawa addresses the control of snake robots on soft surfaces, which are highly relevant in disaster scenarios where terrain surface can vary significantly with respect to hardness. The developed control loop considers tactile feedback from different surface conditions, and uses a Central Pattern Generator (CPG) network to optimize coordination of the joints during locomotion.
Focusing on a more conventional UGV platform, Zafar et al. extend beyond isolated control loop by integrating hand-gesture-based tele-operation and YOLO-based victim detection for more intuitive human-robot interaction in the operational pipeline for search-and-rescue missions. Additionally, Moosavi et al.; Döschl et al. address multi-robot operations. Moosavi et al. investigate path planning for multiple snake robots in rescue scenarios and demonstrates a functional coordination in a simulation environment. Meanwhile, Döschl et al. focus on symbolic planning for multiple aerial robots, providing validation in photorealistic simulation environments and outlining a pathway toward integration with real robotic hardware.
2.2 Outlook
This special session brings together a Research Topic of recent state-of-the-art research contributions aimed at enhancing disaster resilience through the integration of AI and unmanned platforms. With the rapid advancement of AI, particularly in LLM-driven applications enabling increasingly sophisticated reasoning and decision-making capabilities (), as well as recent progress in humanoid robotics (), the field is entering a phase of accelerated innovation. These developments are expected to continuously unlock new opportunities and research breakthroughs in the application of intelligent systems for disaster resilience.
Statements
Author contributions
JK: Conceptualization, Writing – original draft, Writing – review and editing. AD: Conceptualization, Writing – review and editing. RA: Conceptualization, Writing – review and editing. AW: Conceptualization, Writing – review and editing. YA: Conceptualization, Writing – review and editing.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Conflict of interest
Author RA was employed by The MITRE Corporation.
The remaining author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was used in the creation of this manuscript. Generative AI was use to check for spelling and grammar mistakes in the manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1
AlfieriL.BurekP.FeyenL.ForzieriG. (2015). Global warming increases the frequency of river floods in Europe. Hydrology Earth Syst. Sci.19, 2247–2260. 10.5194/hess-19-2247-2015
2
Bit-MonnotA.Bailon-RuizR.LacroixS. (2018). A local search approach to observation planning with multiple uavs. Proc. Int. Conf. Automated Plan. Sched.28, 437–445. 10.1609/icaps.v28i1.13924
3
ChenZ.Asadi ShamsabadiE.JiangS.ShenL.Dias-da CostaD. (2026). Integration of large vision language models for efficient post-disaster damage assessment and reporting. Nat. Commun.17, 1481. 10.1038/s41467-025-68216-z
4
DöschlB.KiamJ. J. (2025). “Say’n’fly: an llm-modulo online planning framework to automate uav command and control,” in 2025 34th IEEE international conference on robot and human interactive communication (RO-MAN), 1693–1698.
5
DottoriF.MentaschiL.BianchiA.AlfieriL.FeyenL. (2023). Cost-effective adaptation strategies to rising river flood risk in Europe. Nat. Clim. Change13, 196–202. 10.1038/s41558-022-01540-0
6
ErdeljM.NatalizioE. (2016). “Uav-assisted disaster management: applications and open issues,” in 2016 international conference on computing, networking and communications (ICNC), 1–5.
7
[Dataset] European Commission (2023). European union disaster resilience goals: acting together to deal with future emergencies
8
GuanW.LiuQ.DongC. (2022). Risk assessment method for industrial accident consequences and human vulnerability in urban areas. J. Loss Prev. Process Industries76, 104745. 10.1016/j.jlp.2022.104745
9
GuibaudA.MindeguiaJ.-C.AlbuerneA.ParentT.ToreroJ. (2024). Notre-dame de paris as a validation case to improve fire safety modelling in historic buildings. J. Cult. Herit.65, 145–154. 10.1016/j.culher.2023.05.008
10
HanJ.XieW.ZhengJ.ShiJ.ZhangW.XiaoT.et al (2025). Kungfubot2: learning versatile motion skills for humanoid whole-body control. arXiv:250916638. 10.48550/arXiv.2509.16638
11
Jacome Felix OomD.De RigoD.PfeifferH.BrancoA.FerrariD.GrecchiR.et al (2022). Pan-European wildfire risk assessment. Publications Office of the European Union. 10.2760/9429
12
MerzB.KuhlickeC.KunzM.PittoreM.BabeykoA.BreschD. N.et al (2020). Impact forecasting to support emergency management of natural hazards. Rev. Geophys.58, e2020RG000704. 10.1029/2020rg000704
13
Mohd DaudS. M. S.Mohd YusofM. Y. P.HeoC. C.KhooL. S.Chainchel SinghM. K.MahmoodM. S.et al (2022). Applications of drone in disaster management: a scoping review. Sci. and Justice62, 30–42. 10.1016/j.scijus.2021.11.002
14
PatraS.GhallabM.NauD.TraversoP. (2019). Acting and planning using operational models. Proc. AAAI Conf. Artif. Intell.33, 7691–7698. 10.1609/aaai.v33i01.33017691
15
van GinkelK. C.KoksE. E.de GroenF.NguyenV. D.AlfieriL. (2022). Will river floods ‘tip’ european road networks? A robustness assessment. Transp. Res. Part D Transp. Environ.108, 103332. 10.1016/j.trd.2022.103332
16
VerykokouS.IoannidisC.AthanasiouG.DoulamisN.AmditisA. (2018). 3D reconstruction of disaster scenes for urban search and rescue. Multimedia Tools Appl.77 (8), 9691–9717. 10.1007/s11042-017-5450-y
17
WebbT.MondalS. S.MomennejadI. (2025). A brain-inspired agentic architecture to improve planning with LLMs. Nat. Commun.16, 8633. 10.1038/s41467-025-63804-5
18
XuF.MaJ.LiN.ChengJ. C. (2025). Large language model applications in disaster management: an interdisciplinary review. Int. J. Disaster Risk Reduct.127, 105642. 10.1016/j.ijdrr.2025.105642
Summary
Keywords
articial intelligence, disaster resilience (DR), rescue, robotic, unmanned systems
Citation
Kiam JJ, Dimou A, Alford R, Wedler A and Ambe Y (2026) Editorial: AI and robotics for increasing disaster resilience in modern societies. Front. Robot. AI 13:1847221. doi: 10.3389/frobt.2026.1847221
Received
03 April 2026
Accepted
01 May 2026
Published
15 May 2026
Volume
13 - 2026
Edited and reviewed by
Chenguang Yang, The Hong Kong Polytechnic University, Hong Kong SAR, China
Updates
Copyright
© 2026 Kiam, Dimou, Alford, Wedler and Ambe.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Jane Jean Kiam, jane.kiam@unibw.de
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.