A POMDP Approach to Map Victims in Disaster Scenarios
<i>Background</i>: 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....
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| Format: | Article |
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MDPI AG
2024-11-01
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| Series: | Logistics |
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| Online Access: | https://www.mdpi.com/2305-6290/8/4/113 |
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| author | Pedro Gabriel Villani Paulo Sergio Cugnasca |
| author_facet | Pedro Gabriel Villani Paulo Sergio Cugnasca |
| author_sort | Pedro Gabriel Villani |
| collection | DOAJ |
| description | <i>Background</i>: 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. <i>Methods</i>: 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. <i>Results</i>: 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. <i>Conclusions</i>: 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. |
| format | Article |
| id | doaj-art-e7c0daf3f60e473ab8d988da84d32435 |
| institution | DOAJ |
| issn | 2305-6290 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Logistics |
| spelling | doaj-art-e7c0daf3f60e473ab8d988da84d324352025-08-20T02:50:59ZengMDPI AGLogistics2305-62902024-11-018411310.3390/logistics8040113A POMDP Approach to Map Victims in Disaster ScenariosPedro Gabriel Villani0Paulo Sergio Cugnasca1Safety Analysis Group (GAS), Department of Computer Engineering and Digital Systems (PCS), Escola Politécnica (Poli), Universidade de São Paulo (USP), São Paulo 05508-010, SP, BrazilSafety 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<i>Background</i>: 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. <i>Methods</i>: 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. <i>Results</i>: 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. <i>Conclusions</i>: 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.https://www.mdpi.com/2305-6290/8/4/113humanitarian logisticsdroneunmanned aerial vehiclesearch and rescuemap victims |
| spellingShingle | Pedro Gabriel Villani Paulo Sergio Cugnasca A POMDP Approach to Map Victims in Disaster Scenarios Logistics humanitarian logistics drone unmanned aerial vehicle search and rescue map victims |
| title | A POMDP Approach to Map Victims in Disaster Scenarios |
| title_full | A POMDP Approach to Map Victims in Disaster Scenarios |
| title_fullStr | A POMDP Approach to Map Victims in Disaster Scenarios |
| title_full_unstemmed | A POMDP Approach to Map Victims in Disaster Scenarios |
| title_short | A POMDP Approach to Map Victims in Disaster Scenarios |
| title_sort | pomdp approach to map victims in disaster scenarios |
| topic | humanitarian logistics drone unmanned aerial vehicle search and rescue map victims |
| url | https://www.mdpi.com/2305-6290/8/4/113 |
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