Real-Time FTM-Based Victim Positioning System Using Heterogeneous Robots in Remote and Outdoor Scenarios

Accurate victim localization in Remote, Outdoor, Unstructured, and Disaster (ROUD) scenarios remains a significant challenge due to limited infrastructure, complex terrains, and time constraints inherent in Search and Rescue (SAR) operations. This article introduces an innovative real-time positioning system that leverages Fine Time Measurement (FTM), a feature of the IEEE 802.11mc amendment, to detect WiFi-enabled devices typically carried by potential victims. The system integrates a Hybrid Wireless Sensor Network (H-WSN) composed of static and mobile anchors mounted on Uncrewed Ground and Aerial Vehicles (UGVs and UAVs), within a Robot Operating System (ROS)-based architecture. A centralized Feedback Information System (FIS) processes real-time RTT data from the field, executes a multilateration algorithm, and provides live geolocation updates to SAR coordinators. The system was validated during a large-scale SAR drill, successfully locating two types of victims—a semi-hidden surface victim and a buried victim—within a 2000m2 area in under 7 minutes, without any filtering or post-processing. The maximum positioning error was 22.87m for the buried victim and 17.14m for the surface one. The complete system, including source code, dataset, and a containerized environment, is openly available to support reproducibility and further research, highlighting the potential of robotic and wireless technologies to enhance disaster response through accurate, real-time localization in complex environments.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3570203
Citations
1
Primary Topic
Robotics and Automated Systems
Type
article
Field-Weighted Citation Impact
1.21
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article

Real-Time FTM-Based Victim Positioning System Using Heterogeneous Robots in Remote and Outdoor Scenarios

Javier Serón, Emil J. Khatib, Raquel Barco, Carlos S. Álvarez‐Merino et al.
1 citations
IEEE Access
Robotics and Automated Systems
1.21
article

Real-Time FTM-Based Victim Positioning System Using Heterogeneous Robots in Remote and Outdoor Scenarios

Javier Serón, Emil J. Khatib, Raquel Barco, Carlos S. Álvarez‐Merino, Alfonso García-Cerezo, Manuel Toscano-Moreno, J. J. Fernández-Lozano, Juan Bravo-Arrabal, José Antonio Gómez‐Ruiz
article en
1 citations

Abstract

Accurate victim localization in Remote, Outdoor, Unstructured, and Disaster (ROUD) scenarios remains a significant challenge due to limited infrastructure, complex terrains, and time constraints inherent in Search and Rescue (SAR) operations. This article introduces an innovative real-time positioning system that leverages Fine Time Measurement (FTM), a feature of the IEEE 802.11mc amendment, to detect WiFi-enabled devices typically carried by potential victims. The system integrates a Hybrid Wireless Sensor Network (H-WSN) composed of static and mobile anchors mounted on Uncrewed Ground and Aerial Vehicles (UGVs and UAVs), within a Robot Operating System (ROS)-based architecture. A centralized Feedback Information System (FIS) processes real-time RTT data from the field, executes a multilateration algorithm, and provides live geolocation updates to SAR coordinators. The system was validated during a large-scale SAR drill, successfully locating two types of victims—a semi-hidden surface victim and a buried victim—within a 2000m2 area in under 7 minutes, without any filtering or post-processing. The maximum positioning error was 22.87m for the buried victim and 17.14m for the surface one. The complete system, including source code, dataset, and a containerized environment, is openly available to support reproducibility and further research, highlighting the potential of robotic and wireless technologies to enhance disaster response through accurate, real-time localization in complex environments.

IEEE AccessVol. 13
Robotics Research (United States) (US), European Telecommunications Standards Institute (FR), Universidad de Málaga (ES)
Peace, Justice and strong institutions
Openalex Percentile: Top 22%
Robotics and Automated Systems
1.21
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