【摘要】 针对多个自主水面航行器(ASV)围捕单个主动逃逸的对抗性目标问题,利用微分博弈理论建立了多ASV协同围捕问题博弈模型,在含有距离项的支付函数中引入由逃逸角构成的合围项,从而降低目标中途逃逸的概率;然后将围捕问题转换为求解可实现策略的优化问题,利用粒子群优化(PSO)算法求解满足纳什均衡的最优策略,仿真和湖上试验结果均证明了基于PSO的微分博弈围捕算法的有效性。更多还原

【Abstract】 In the scenario where multi-autonomous surface vehicles(ASVs) round up an actively escaped adversarial target, a game model of the multi-ASV cooperative hunting problem was established using differential game theory. A surround term consisting of the escape angle was introduced into the payment function which included the distance cost so that the escape probability of the target was reduced. At the same time, the hunting problem was converted into an optimization problem for solving achievable strategies, and the particle swarm optimization(PSO) algorithm was used to solve the optimal strategy that satisfied the Nash equilibrium. The simulation and lake test results show the effectiveness of the hunting algorithm based on differential game and PSO.更多还原