A POMDP approach to map victims in disaster scenarios

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.