AADL Model for Temporal Tuning of the Failsafe Triggering Mechanism from a Fire Fighting Drone - Becker, L. B.; Pinto, M. L.; Bodeveix, J. -P.; Filali, M. (Anais do Simpósio Brasileiro de Engenharia de Sistemas Computacionais (SBESC), 2025)
The use of drones in firefighting has increased in recent years. In such applications, monitoring external temperatures is essential to ensure aircraft safety, particularly to prevent operation above a predefined threshold (70°C). This is critical when the drone approaches the fire, since the measured temperature reflects a past position, not the current one. Additionally, the drone takes some time to execute the maneuver and move away from the fire. While this delay depends on mechanical factors, the sensing latency is influenced by the embedded system architecture and is the focus of this paper. We present a method to estimate the temperature variation (ΔT) during this interval, based on an AADL model of the system. This model includes the drone’s temperature sensors and supports calculating their respective ΔT, helping to define when failsafe mechanisms should be triggered.