2026-05-28T13:45:51Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/276562026-02-03T12:11:15Zcom_10630_2254col_10630_37959

00925njm 22002777a 4500 dc Vilchez Campillejo, Enrique author Troya-Castilla, Javier author Cámara-Moreno, Javier author 2023 Wildfires have evolved significantly over the last decades, burning increasingly large forest areas every year. Smart cyber-physical systems like small Unmanned Air Vehicles (UAVs) can help to monitor, predict, and mitigate wildfires. In this paper, we present an approach to build control software for UAVs that allows autonomous monitoring of wildfires. Our proposal is underpinned by an ensemble of artificial intelligence techniques that include: (i) Recurrent Neural Networks (RNNs) to make local UAV predictions about how the fire will spread over its surrounding area; and (ii) Deep Reinforcement Learning (DRL) to learn policies that will optimize the operation of the UAV team. Vílchez, E., Troya, J., Cámara, J.: Towards Self-Adaptive Software for Wildfire Monitoring with Unmanned Air Vehicles. In: Durán Toro, A. (ed.) Actas de las XXVII Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2023). Sistedes (2023). https://hdl.handle.net/11705/JISBD/2023/8176 https://hdl.handle.net/10630/27656 Inteligencia artificial Aviones sin piloto Incendios forestales Towards Self-Adaptive Software for Wildfire Monitoring with Unmanned Air Vehicles.