Artificial Intelligence of Things-Based Modular Avionics System Solution for Swarm Unmanned Combat Aerial Vehicles (SUCAVs)

Abstract

In this study, a modular avionics system solution is presented for fixed-wing fully autonomous controlled swarm unmanned aerial vehicles that can successfully detect within-visual and beyond-visual targets in the mission area as many times as possible thanks to deep learning, lock on to the target for the desired period of time and destroy it, evade the locks of other armed unmanned aerial vehicles with maneuvers and continuously transfer the information it receives to ground stations. Armed unmanned aerial vehicles provide reconnaissance, surveillance and intelligence opportunities as well as detection and confirmation of within-visual and beyond-visual targets in risky, complex conflict zones. Avionics system architecture consists of three units: the air platform consisting of swarm unmanned aerial vehicles, the ground station where swarm unmanned aerial vehicles are tracked throughout the mission flight and the IoT platform. Swarm armed unmanned aerial vehicles can take off autonomously and calculate the orientation and direction information of all aircraft in the airspace. Accordingly, the locking process is performed via the high-resolution/wide-angle imaging system by focusing on the appropriate aircraft that can be tracked. With the artificial intelligence (AI) algorithms to be developed for the continuity of the locking process, it is ensured that the swarm armed unmanned aerial vehicles can perform appropriate maneuvers. Aircraft equipped with appropriate systems for autonomous flight have a mission computer to run the algorithms related to the task. A joint study is carried out with the ground station and IoT platform to ensure the controllability and tracking of the system during the tracking of the swarm armed unmanned aerial vehicles. In order for these specified design points to be successfully performed during the mission, the system has a stable and agile flight control.