Task allocation for UAV swarms under communication attacks: An approach based on game theory and negotiation mechanism

Abstract

This research delves into the method of task allocation for Unmanned Aerial Vehicle (UAV) swarms utilizing game theory and negotiation mechanisms in the face of communication network disruptions. Initially, a UAV swarm topology network characterized by a scale-free structure is established. Within this network, specific nodes undergo a single, deliberate batch attack. To address this, an edge-compensation strategy is formulated for network restoration, aiming to reinstate network connectivity to an optimal level. Subsequently, an algorithm is developed, integrating a bi-directional selection negotiation mechanism and coalition game theory, to determine the initial coalition for UAV task allocation. Following this, a traversal algorithm is employed to optimize the initial coalition, ultimately yielding the final task allocation coalition result. This methodology proves effective in repairing the topology network of UAV swarms during attack conditions, leading to the derivation of an optimal UAV allocation scheme that enhances the success rate of task allocations.