空海协同任务中无人集群协同路径规划充电调度联合优化方法OA
Joint Optimization Algorithm for Cooperative Path Planning and Charging Scheduling of Unmanned Swarms in Air-Sea Coordinated Missions
面向海上封控任务中空海协同拦截对抗的需求,传统多无人机(Unmanned Aerial Ve-hicle,UAV)路径规划算法存在难以同时处理复杂路径规划和连续充电时间优化的问题,提出一种基于分层优化的两阶段路径规划与充电调度联合优化方法.一阶段在"动作-状态"空间中构建基于行为的有向无环图,将联合路径规划描述为带任务时间窗和电量约束的整数规划,生成各无人机的任务路径及粗粒度海上充电决策;二阶段在初始决策的基础上建立混合整数线性规划模型,在给定充电窗口内细化无人机在无人艇上充电持续时间,最大限度地减少了任务执行时间,并确保电池不会完全耗尽.数值仿真结果表明,在设置的海上对抗场景及不同敌方规模下,该方法能够实现对多数敌方无人艇的有效拦截.
To meet the requirements of air-sea cooperative interception in maritime blockade mis-sions,and to address the difficulty of conventional multi-unmanned aerial vehicle path planning in handling both complex path constraints and continuous energy limitations,a two-stage optimiza-tion-based method for path planning and charging scheduling is proposed.In the first stage,a be-havior-based directed acyclic graph is constructed in the action-state space,and the joint path plan-ning problem is formulated as an integer programming model with task time window and battery-energy constraints,thereby generating task routes and coarse sea-based charging decisions for each UAV.In the second stage,a mixed-integer linear programming model is built on this basis to re-fine the docking-and-charging duration within given charging windows,so as to reduce overall task execution time as much as possible while ensuring that the onboard batteries never become fully depleted.Numerical simulation results in the designed maritime confrontation scenarios with dif-ferent scales of hostile unmanned surface vessels(USVs)indicate that the proposed method can ef-fectively intercept most hostile USVs,demonstrating the effectiveness of the two-stage optimiza-tion framework for air-sea cooperative interception.
王小军;刘廷轩;文泽凌;季袁冬;宁召柯
海军装备部·北京·100080四川大学数学学院·成都·610065四川大学空天科学与工程学院·成都·610207四川大学空天科学与工程学院·成都·610207||空天多源信息智能融合四川省重点实验室·成都·610207四川大学空天科学与工程学院·成都·610207||空天多源信息智能融合四川省重点实验室·成都·610207
航空航天
空海协同拦截对抗海上封控任务最优路径规划充电优化两阶段优化
air-sea cooperative interceptionmaritime blockade missionoptimal path planningcharging optimizationtwo-stage optimization
《飞控与探测》 2026 (2)
31-41,11
四川省科技计划(2024NSFSC1485)
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