A POMDP Approach to Map Victims in Disaster Scenarios

Pedro Gabriel Villani () and Paulo Sergio Cugnasca
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Pedro Gabriel Villani: Safety Analysis Group (GAS), Department of Computer Engineering and Digital Systems (PCS), Escola Politécnica (Poli), Universidade de São Paulo (USP), São Paulo 05508-010, SP, Brazil
Paulo Sergio Cugnasca: Safety Analysis Group (GAS), Department of Computer Engineering and Digital Systems (PCS), Escola Politécnica (Poli), Universidade de São Paulo (USP), São Paulo 05508-010, SP, Brazil

Logistics, 2024, vol. 8, issue 4, 1-25

Abstract: Background : The rise in natural and man-made disasters has increased the need for effective search-and-rescue tools, particularly in resource-limited areas. Unmanned Aerial Vehicles (UAVs) are increasingly used for this purpose due to their flexibility and lower operational costs. However, finding the most efficient paths for these UAVs remains a challenge, as it is essential to maximize victim location and minimize mission time. Methods : This study presents an autonomous UAV-based approach for identifying victims, prioritizing high-risk areas and those needing urgent medical attention. Unlike other methods focused solely on minimizing mission time, this approach emphasizes high-risk zones and potential secondary disaster areas. Using a partially observable Markov decision process, it simulates victim detection through an image classification algorithm, enabling efficient and independent operation. Results : Experiments with real data indicate that this approach reduces risk by 66% during the mission’s first half while autonomously identifying victims without human intervention. Conclusions : This study demonstrates the capability of autonomous UAV systems to improve search-and-rescue efforts in disaster-prone, resource-constrained regions by effectively prioritizing high-risk areas, thereby reducing mission risk and improving response efficiency.

Keywords: humanitarian logistics; drone; unmanned aerial vehicle; search and rescue; map victims (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2024
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