| Přispěvatelé: |
Universidad Pública de Navarra. Departamento de Estadística, Informática y Matemáticas, Nafarroako Unibertsitate Publikoa. Estatistika, Informatika eta Matematika Saila, Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa. Institute of Smart Cities - ISC, Gobierno de Navarra / Nafarroako Gobernua |
| Popis: |
New Unmanned Aerial Systems (UAS) applications will increase air traffic densities in metropolitan regions. Collision avoidance systems (CAS) are a key component in integrating a high number of UAS into the airspace in a safe way. This paper presents a distributed, autonomous, and knowledge-based CAS, called Dronetology System (DroS), for UASs. The CAS proposed here is managed using a novel ontology, called Dronetology-cas, which allows to make autonomous decisions according to the knowledge inferred from the data gathered by the UAS. DroS is deployed as part of the payload of the UAS. So, it is designed to run in an embedded platform with limited processing capacity and low battery consumption. DroS collects data from sensors and collaborative elements to make smart decisions using knowledge obtained from collaborative UASs, adapting the maneuvers of the aerial vehicles to their original flight plans, their kind of vehicle, and the collision scenario. DroS accountability involves recording its internal operation to assist with reconstructing the circumstances surrounding an autonomous maneuver or the details previous to a collision. DroS has been verified using the hardware in the loop (HIL) technique with a UAS traffic environment simulator. Results obtained show a significant improvement in terms of safety by avoiding collisions. This work has been supported in part by the Ministerio de Ciencia e Innovación (Spain) under the research grant RTI2018-095499-B-C31 IoTrain; in part by Agencia Estatal de Investigación (AEI) and European Union NextGeneration EU/PRTR PLEC2021-007997: Holistic power lines predictive maintenance system; and in part by the Government of Navarre (Departamento de Desarrollo Económico) under the research grants 0011-1411-2021-000021 EMERAL: Emergency UAVs for long range operations, 0011-1365-2020-000078 DIVA, and 0011-1411-2021-000025 MOSIC: Plataforma logística de largo alcance, eléctrica y conectada. |
