针对双方无人机之间的动态对抗博弈问题,提出了动态人工势场法(dynamic artificial potential field method,DAPF)和精英蚁群优化(elite ant colony optimization,EACO)算法相结合的求解方法。首先,采用动态人工势场法,以敌我双方无人...针对双方无人机之间的动态对抗博弈问题,提出了动态人工势场法(dynamic artificial potential field method,DAPF)和精英蚁群优化(elite ant colony optimization,EACO)算法相结合的求解方法。首先,采用动态人工势场法,以敌我双方无人机作为博弈的局中人,构建双方无人机动态对抗博弈模型。其次,提出精英蚁群算法,计算双方博弈的纳什均衡策略。该算法引入对立学习和划分精英蚂蚁加快算法收敛速度,并引入遗传算法中的变异操作以避免局部最优值的问题。最后,仿真验证了所提方法的可行性和有效性。展开更多
Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Fir...Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Firstly,a normalized artificial potential field optimization is proposed by reconstructing a novel function with anisotropy in each dimension,which can make the flight speed of a fixed UAV swarm independent of the repulsive/attractive gain coefficient and avoid trapping into local optimization and local oscillation.Then,taking into account minimum velocity and turning angular velocity of fixed-wing UAV swarm,a strategy of decomposing target vector to avoid moving obstacles and pop-up threats is proposed.Finally,several simulations are carried out to illustrate superiority and effectiveness.展开更多
文摘针对双方无人机之间的动态对抗博弈问题,提出了动态人工势场法(dynamic artificial potential field method,DAPF)和精英蚁群优化(elite ant colony optimization,EACO)算法相结合的求解方法。首先,采用动态人工势场法,以敌我双方无人机作为博弈的局中人,构建双方无人机动态对抗博弈模型。其次,提出精英蚁群算法,计算双方博弈的纳什均衡策略。该算法引入对立学习和划分精英蚂蚁加快算法收敛速度,并引入遗传算法中的变异操作以避免局部最优值的问题。最后,仿真验证了所提方法的可行性和有效性。
文摘Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Firstly,a normalized artificial potential field optimization is proposed by reconstructing a novel function with anisotropy in each dimension,which can make the flight speed of a fixed UAV swarm independent of the repulsive/attractive gain coefficient and avoid trapping into local optimization and local oscillation.Then,taking into account minimum velocity and turning angular velocity of fixed-wing UAV swarm,a strategy of decomposing target vector to avoid moving obstacles and pop-up threats is proposed.Finally,several simulations are carried out to illustrate superiority and effectiveness.
