Taxonomy of Mission Performance for Diverse and Homogenous UAV Flocks

Abstract

Groups of unmanned aerial vehicles (UAVs) are believed to be useful in wide-area search and destroy (WASD) missions. While many researches focus on developing better algorithms that govern the mission capabilities of a group of UAVs (∞ock), we take a novel approach and examine the efiect of ∞ock properties on mission execution performance. Using Dudek’s taxonomy we investigate the performance of a group of autonomous Unmanned Aerial Vehicles (UAVs) cooperating in mission execution against a group of enemy agents acting in an unknown environment. We show that increasing the number of UAVs in the group proves to be beneflcial as it allows the group to react to more enemy events. We also show that using communication helps creating better cooperation between the ∞ock members; however, using inflnite communication range or inflnite communication bandwidth results in considerable computational complexity. We conclude that it may be su‐cient to use flnite-bandwidth communication, keeping the computational complexity constant with the number of UAVs in the group, thus allowing the group to be scalable to large numbers of UAVs. We conclude that using ∞ocking behavior improves the group performance only if the group is capable of mission task collaboration. Finally, we show that using heterogeneous ∞ocks, comprised of identifying UAVs and shooter UAVs, provide better mission performance, especially in civilian-rich environment.