RT Conference Proceedings
T1 Towards Self-Adaptive Software for Wildfire Monitoring with Unmanned Air Vehicles.
A1 Vilchez Campillejo, Enrique
A1 Troya-Castilla, Javier
A1 Cámara-Moreno, Javier
K1 Inteligencia artificial
K1 Aviones sin piloto
K1 Incendios forestales
AB 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.
YR 2023
FD 2023
LK https://hdl.handle.net/10630/27656
UL https://hdl.handle.net/10630/27656
LA eng
NO 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
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026