Cooperative mission planning based on game theory for UAVs and USVs heterogeneous system in dynamic scenario

Purpose: The purpose of this paper is to present and implement a task allocation method based on game theory for reconnaissance mission planning of UAVs and USVs system. Design/methodology/approach: In this paper, the decision-making framework via game theory of mission planning is constructed. The mission planning of UAVs–USVs is transformed into a potential game optimization problem by introducing a minimum weight vertex cover model. The modified population-based game-theoretic optimizer (MPGTO) is used to improve the efficiency of solving this complex multi-constraint assignment problem. Findings: Several simulations are carried out to exhibit that the proposed algorithm obtains the superiority on quality and efficiency of mission planning solutions to some existing approaches. Research limitations/implications: Several simulations are carried out to exhibit that the proposed algorithm obtains the superiority on quality and efficiency of mission planning solutions to some existing approaches. Practical implications: The proposed framework and algorithm are expected to be applied to complex real scenarios with uncertain targets and heterogeneity. Originality/value: The decision framework via game theory is proposed for the mission planning problem of UAVs–USVs and a MPGTO with swarm evolution, and the adaptive iteration mechanism is presented for ensuring the efficiency and quality of the solution.