Detection systems have recently received considerable attention because of the importance of tracking infected people during SARS-CoV-2 pandemic. Such implementations can be very useful for finding potential victims in the context of emergency response, especially in situations where GPS is not available for inspection by imaging is not practical. Radio signals come into play, and specifically from devices that transmit periodically and with low power consumption. With the rise of Internet of Things and the plethora of wearable devices used in everyday life, like a smartphone, Bluetooth Low Energy (BLE) can provide considerable assistance in locating lost people. This work presents a system for detecting victims in a non-structured environment, by means of a search and rescue (SAR) robot. A real implementation of a close detection robotic platform based on BLE for SAR interventions is laid out. In order to estimate the distance between a robotic agent and potential victims within an experimental area, a Log-distance path loss model is presented, which has been tuned to detect beacons with reasonable accuracy within a range of 25 meters in rugged environments. The proposed scheme has been tested in realistic scenarios during SAR exercises.Los autores quieren agradecer la colaboración de la Cátedra de Seguridad, Emergencias y Catástrofes de la Universidad de Málaga, dirigida por el profesor Jesús Miranda, así como a Javier Serón Barba por su apoyo durante los experimentos. Este trabajo ha sido parcialmente subvencionado por el proyecto RTI2018-093421-B-I00. Se ha realizado en el cuadro del proyecto Horizonte 2020 proyecto LOCUS (ICT-871249) recibiendo fondos de la UE. Este trabajo también ha sido parcialmente subvencionado por la Junta de Andalucía y el ERDF (Consejería de Transformación Económica, Industria, Conocimiento y Universidades, Proyecto de Excelencia PENTA, P18-FR-4647). Este trabajo ha sido parcialmente financiado por la Junta de Andalucía y el ERDF en el marco del proyecto 5G-SCARF - 5G Smart Communications for the AiRport of the Future (Ref. UMA-CEIATECH-17, “Proyecto singular de actuaciones de transferencia del conocimiento Campus Excelencia Internacional Andalucía TECH. Ecosistema innovador con inteligencia artificial para Andalucía 2025”). Finalmente, al I Plan Propio de Investigación y Transferencia de Universidad de Málaga.
Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
