Design and Implementation of a mission planner for Mutiple UCAVs in a SEAD mission

Abstract

In modern warfare, Unmanned Aerial Vehicles (UAVs) prove to be a highly valuable asset for long and dangerous missions. Although UAVs are mostly used for Surveillance, Reconnaissance and Target Acquisition missions, so-called unmanned combat aerial vehicles (UCAVs) are under development, carrying missiles as payload. Recent evolutions in computer technology and artiflcial intelligence (AI) enable UAVs to operate more autonomously. This reduces the UAV operator workload, enables covert operations and may, in the long term, reduce the number of operators on the ground. This paper is based on an MSc study on advanced UAV autonomy. It presents the results of the application of a greedy heuristic approach to enable a swarm of UCAVs to completely autonomously execute a Suppression of Enemy Air Defence (SEAD) mission. Trajectory generation was done using visibility graphs. For task assignment, Network Flow Programming was used. The algorithms are computationally e‐cient, which enables the swarm to update the mission plan, during the mission. Although the resulting mission plans are sub-optimal, test runs have shown that approach successfully generates feasible missions that meet fuel and time constraints.