火力与指挥控制
火力與指揮控製
화력여지휘공제
Fire Control & Command Control
2015年
10期
31-36
,共6页
网络化战场%多无人机任务分配%遗传算法
網絡化戰場%多無人機任務分配%遺傳算法
망락화전장%다무인궤임무분배%유전산법
networked battlefield%multi-UAVs mission allocation%genetic algorithm
针对网络化战场下无人机（UAV）跨区飞行、信息共享等特点扩大了任务区域与规模而引发任务分配难题，对无人机运动及通信特性、任务时间窗约束和初始战场布局等因素建模，根据目标属性对其分组以降低问题复杂度，应用优化算法得到初始解，随后进行全局交换、删除及插入等调整得到最终调度方案，由此搭建快速求解多UAV任务调度的通用算法框架。最后应用遗传算法验证，仿真结果表明：该框架在解决多无人机大规模任务分配时具有较好的时效性和适应性。
針對網絡化戰場下無人機（UAV）跨區飛行、信息共享等特點擴大瞭任務區域與規模而引髮任務分配難題，對無人機運動及通信特性、任務時間窗約束和初始戰場佈跼等因素建模，根據目標屬性對其分組以降低問題複雜度，應用優化算法得到初始解，隨後進行全跼交換、刪除及插入等調整得到最終調度方案，由此搭建快速求解多UAV任務調度的通用算法框架。最後應用遺傳算法驗證，倣真結果錶明：該框架在解決多無人機大規模任務分配時具有較好的時效性和適應性。
침대망락화전장하무인궤（UAV）과구비행、신식공향등특점확대료임무구역여규모이인발임무분배난제，대무인궤운동급통신특성、임무시간창약속화초시전장포국등인소건모，근거목표속성대기분조이강저문제복잡도，응용우화산법득도초시해，수후진행전국교환、산제급삽입등조정득도최종조도방안，유차탑건쾌속구해다UAV임무조도적통용산법광가。최후응용유전산법험증，방진결과표명：해광가재해결다무인궤대규모임무분배시구유교호적시효성화괄응성。
In networked battlefield,UAVs can fly cross regions and share information to others, which expand the area and scale of tasks. In order to address the difficulties of mission allocation caused by that,this paper will first model UAVs motion and communication features,task time window, initial battlefield layout,etc.,group the targets by their characteristics in order to reduce the complexity,then apply optimization algorithm to obtain initial solution and adjust them through exchange,deletion or insert so as to obtain the final plan,and build a common algorithm framework for fast solution. Genetic algorithm will be employed for verification. The simulation results indicate that the framework may achieve higher timeliness and adaptability in large-scale multi-UAV mission allocation.
