Decentralized Task Assignment for Networked Unmanned Aerial Vehicles

Competing with the technical advancements of near-peer adversaries, the Navy is shifting towards increased use of Unmanned Aerial Vehicles (UAVs) to accomplish missions. Formations of networked UAVs are susceptible to single-point failure when assigned tasks centrally, leading to the need for decentralization. This paper investigates the optimal allocation of tasks among networked UAVs by means of distributed algorithms to avoid the single-point vulnerability. Furthermore, it addresses the issue of incomplete information about assignment costs, the result of limited direct connections within the formation. The research synthesizes the Hungarian and consensus classes of algorithms to achieve decentralized task allocation subjected to unknown assignment costs and communications being restricted to immediate neighbors. Within the scope of this paper, the number of tasks and agents are equal and agents know their own costs. Assignment costs of other agent-task pairs are not globally known. The success of the proposed approach is established by demonstrating that the synthesized algorithm reaches the same task assignment compared to centralized optimization using the global cost matrix.