Distributed Attacker Selection in UAV Swarm via Potential Game Inspired by Hawk Flock's Cooperative Hunting

During cooperative hunting, hawk flock rapidly selects a lead hawk to carry out attacks while other members assist in encircling the prey. Inspired by this, this paper proposes a method for distributed attacker selecting in unmanned aerial vehicle (UAV) swarm. First, incorporating the traits observed in hawk flock, the fully locally computable situation indicator, individual and group interception indicators, and a feedback-based willingness indicator are designed. Then, the individual utility function and the feasible strategy set are constrained, thereby establishing a distributed selection model that satisfies the conditions of a potential game. Furthermore, a Distributed Strictly-Better Updating (DSU) algorithm is proposed by strictlybetter set and capture marginal gain, which guarantees convergence within finite steps.