DAM-BBOPSO算法的Multi-UAV集群攻击任务规划  

Research on cluster attack mission planning of Multi-UAV based on DAM-BBOPSO algorithm

作　　者：李杰[1] 孙尧[1] 莫宏伟[1] 

机构地区：[1]哈尔滨工程大学自动化学院,黑龙江哈尔滨150001

出　　处：《哈尔滨工程大学学报》2013年第10期1242-1248,共7页Journal of Harbin Engineering University

基　　金：国家自然科学基金资助项目(61075113);中央高校基本科研业务费重大专项资助项目(HEUCFZ1029);黑龙江省杰出青年科学基金资助项目(JC201212)

摘　　要：现代防御技术的迅速发展使得无人驾驶飞行器的攻击效果大大下降,无人驾驶飞行器自主编队集群攻击技术已经成为未来战场的关键技术之一,多无人机之间的任务规划算法是保证无人机顺利、高效完成任务的关键.将无人机集群攻击任务规划问题看成是多约束的任务分配过程,建立任务规划模型,结合分布式拍卖机制和生物地理算法对粒子群优化算法的粒子初始化和寻优过程进行改进.根据实际约束条件生成初始粒子,保证了粒子的多样性;在算法优化过程中,利用生物地理算法与粒子群算法对粒子运动进行动态的控制,使得算法具有更好的适应性与稳定性.仿真结果表明运用分布式拍卖机制生物地理粒子群优化算法得到的方案不仅完全满无人机集群攻击任务的要求,而且比传统粒子群优化算法和生物地理粒子群优化算法具有更好的收敛性.The rapid development of modern defense technology lowers the UAVs＂ attacking effect greatly, the au- tonomous formation and cluster attack technique of Muhi-UAV has become one of the key technologies in future bat- tlefield, and the mission planning algorithm among Multi-UAV is the key to the smooth and effective completion of a task. By considering the cluster attack mission planning of UAVs as a multi-constrained task allocation process, a mission planning model is established. And in combination with the distributed auction mechanism and biogeogra- phy-based optimization （BBO） , the particle initialization and optimization process of PSO algorithm is improved. According to the actual constraint conditions, initial particles are generated, to assure the diversity of particles ; in the algorithm optimization process, the BB0 algorithm and the PSO algorithm are utilized to dynamically control the motion of particles, so as to assure better adaptability and stability of algorithm. The simulation results show that, the program attained by applying the distributed auction mechanism BBOPSO algorithm may fully meet the require- ments of Multi-UAV＇s cluster attack missions, and show better convergence than traditional PSO and BBOPSO algo- rithm.

关 键 词：多无人机 生物地理算法 分布式拍卖机制 粒子群优化 任务规划 

分 类 号：V279[航空宇航科学与技术—飞行器设计]