A Framework for Analyzing Adaptive Autonomous Aerial Vehicles

Unmanned aerial vehicles (UAVs), a.k.a. drones, are becoming increasingly popular due to great advancements in their control mechanisms and price reduction. UAVs are being used in applications such as package delivery, plantation and railroad track monitoring, where UAVs carry out tasks in an automated fashion. Devising how UAVs achieve a task is challenging as the environment where UAVs are deployed is normally unpredictable, for example, due to winds. Formal methods can help engineers to specify flight strategies and to evaluate how well UAVs are going to perform to achieve a task. This paper proposes a formal framework where engineers can raise the confidence in their UAV specification by using symbolic, simulation and statistical and model checking methods. Our framework is constructed over three main components: the behavior of UAVs and the environment are specified in a formal executable language; the UAV's physical model is specified by a simulator; and statistical model checking algorithms are used for the analysis of system behaviors. We demonstrate the effectiveness of our framework by means of several scenarios involving multiple drones.