This article studies the cooperative search-attack mission problem with dynamic targets and threats, and presents a Distributed Intelligent Self-Organized Mission Planning(DISOMP)algorithm for multiple Unmanned Aerial...This article studies the cooperative search-attack mission problem with dynamic targets and threats, and presents a Distributed Intelligent Self-Organized Mission Planning(DISOMP)algorithm for multiple Unmanned Aerial Vehicles(multi-UAV). The DISOMP algorithm can be divided into four modules: a search module designed based on the distributed Ant Colony Optimization(ACO) algorithm, an attack module designed based on the Parallel Approach(PA)scheme, a threat avoidance module designed based on the Dubins Curve(DC) and a communication module designed for information exchange among the multi-UAV system and the dynamic environment. A series of simulations of multi-UAV searching and attacking the moving targets are carried out, in which the search-attack mission completeness, execution efficiency and system suitability of the DISOMP algorithm are analyzed. The simulation results exhibit that the DISOMP algorithm based on online distributed down-top strategy is characterized by good flexibility, scalability and adaptability, in the dynamic targets searching and attacking problem.展开更多
Multiple Uninhabited Aerial Vehicles (multi-UAVs) coordinated trajectory replanning is one of the most complicated global optimum problems in multi-UAVs coordinated control. Based on the construction of the basic mode...Multiple Uninhabited Aerial Vehicles (multi-UAVs) coordinated trajectory replanning is one of the most complicated global optimum problems in multi-UAVs coordinated control. Based on the construction of the basic model of multi-UAVs coordinated trajectory replanning, which includes problem description, threat modeling, constraint conditions, coordinated function and coordination mechanism, a novel Max-Min adaptive Ant Colony Optimization (ACO) approach is presented in detail. In view of the characteristics of multi-UAVs coordinated trajectory replanning in dynamic and uncertain environments, the minimum and maximum pheromone trails in ACO are set to enhance the searching capability, and the point pheromone is adopted to achieve the collision avoidance between UAVs at the trajectory planner layer. Considering the simultaneous arrival and the air-space collision avoidance, an Estimated Time of Arrival (ETA) is decided first. Then the trajectory and flight velocity of each UAV are determined. Simulation experiments are performed under the complicated combating environment containing some static threats and popup threats. The results demonstrate the feasibility and the effectiveness of the proposed approach.展开更多
静止无功补偿器(static var compensator,SVC)通常用来进行负荷补偿或系统补偿,在系统补偿时往往用于电压稳定控制,针对电压稳定控制的工况,文中提出一种采用蚁群算法优化PI控制器参数的方法,克服了常规PI控制对被控对象数学模型的依赖...静止无功补偿器(static var compensator,SVC)通常用来进行负荷补偿或系统补偿,在系统补偿时往往用于电压稳定控制,针对电压稳定控制的工况,文中提出一种采用蚁群算法优化PI控制器参数的方法,克服了常规PI控制对被控对象数学模型的依赖性,简单易于实现。蚁群优化算法中,以时间与误差绝对值乘积积分(integral of time-weighted absolute error,ITAE)准则作为寻优目标函数,对PI控制器的比例、积分参数进行调整、寻优,使SVC系统的响应过程达到最优。仿真和实验结果表明,该最优PI控制器能快速跟踪SVC系统的电压设定值,基于该PI控制器的SVC能迅速进行无功补偿,具有较强的适应性和较高的补偿精度。展开更多
提出了一种基于蚁群优化和粒子群优化的混合算法求解TSP(Traveling Salesm an Prob lem)问题。在应用蚁群算法对TSP问题的求解过程中,利用粒子群算法对蚁群系统的参数进行优化,其目的是提高蚁群系统的优化性能,使蚁群系统的参数不必靠...提出了一种基于蚁群优化和粒子群优化的混合算法求解TSP(Traveling Salesm an Prob lem)问题。在应用蚁群算法对TSP问题的求解过程中,利用粒子群算法对蚁群系统的参数进行优化,其目的是提高蚁群系统的优化性能,使蚁群系统的参数不必靠人工经验或反复试验选取,而是通过粒子搜索自适应选取。展开更多
Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance...Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance and urgency（e.g.,observation tasks orienting to the earthquake area and military conflict area）,have not been taken into account yet.Therefore,it is crucial to investigate the satellite integrated scheduling methods,which focus on meeting the requirements of emergency tasks while maximizing the profit of common tasks.Firstly,a pretreatment approach is proposed,which eliminates conflicts among emergency tasks and allocates all tasks with a potential time-window to related orbits of satellites.Secondly,a mathematical model and an acyclic directed graph model are constructed.Thirdly,a hybrid ant colony optimization method mixed with iteration local search（ACO-ILS） is established to solve the problem.Moreover,to guarantee all solutions satisfying the emergency task requirement constraints,a constraint repair method is presented.Extensive experimental simulations show that the proposed integrated scheduling method is superior to two-phased scheduling methods,the performance of ACO-ILS is greatly improved in both evolution speed and solution quality by iteration local search,and ACO-ILS outperforms both genetic algorithm and simulated annealing algorithm.展开更多
基金supported in part by National Natural Science Foundation of China (Nos. 61741313, 61673209, and 61533008)Jiangsu Six Peak of Talents Program, China (No. KTHY-027)Postgraduate Research & Practice Innovation Program of Jiangsu Province, China (No. KYCX18_0303)
文摘This article studies the cooperative search-attack mission problem with dynamic targets and threats, and presents a Distributed Intelligent Self-Organized Mission Planning(DISOMP)algorithm for multiple Unmanned Aerial Vehicles(multi-UAV). The DISOMP algorithm can be divided into four modules: a search module designed based on the distributed Ant Colony Optimization(ACO) algorithm, an attack module designed based on the Parallel Approach(PA)scheme, a threat avoidance module designed based on the Dubins Curve(DC) and a communication module designed for information exchange among the multi-UAV system and the dynamic environment. A series of simulations of multi-UAV searching and attacking the moving targets are carried out, in which the search-attack mission completeness, execution efficiency and system suitability of the DISOMP algorithm are analyzed. The simulation results exhibit that the DISOMP algorithm based on online distributed down-top strategy is characterized by good flexibility, scalability and adaptability, in the dynamic targets searching and attacking problem.
基金supported by the Natural Science Foundation of China (Grant no.60604009)Aeronautical Science Foundation of China (Grant no.2006ZC51039,Beijing NOVA Program Foundation of China (Grant no.2007A017)+1 种基金Open Fund of the Provincial Key Laboratory for Information Processing Technology,Suzhou University (Grant no KJS0821)"New Scientific Star in Blue Sky"Talent Program of Beihang University of China
文摘Multiple Uninhabited Aerial Vehicles (multi-UAVs) coordinated trajectory replanning is one of the most complicated global optimum problems in multi-UAVs coordinated control. Based on the construction of the basic model of multi-UAVs coordinated trajectory replanning, which includes problem description, threat modeling, constraint conditions, coordinated function and coordination mechanism, a novel Max-Min adaptive Ant Colony Optimization (ACO) approach is presented in detail. In view of the characteristics of multi-UAVs coordinated trajectory replanning in dynamic and uncertain environments, the minimum and maximum pheromone trails in ACO are set to enhance the searching capability, and the point pheromone is adopted to achieve the collision avoidance between UAVs at the trajectory planner layer. Considering the simultaneous arrival and the air-space collision avoidance, an Estimated Time of Arrival (ETA) is decided first. Then the trajectory and flight velocity of each UAV are determined. Simulation experiments are performed under the complicated combating environment containing some static threats and popup threats. The results demonstrate the feasibility and the effectiveness of the proposed approach.
文摘静止无功补偿器(static var compensator,SVC)通常用来进行负荷补偿或系统补偿,在系统补偿时往往用于电压稳定控制,针对电压稳定控制的工况,文中提出一种采用蚁群算法优化PI控制器参数的方法,克服了常规PI控制对被控对象数学模型的依赖性,简单易于实现。蚁群优化算法中,以时间与误差绝对值乘积积分(integral of time-weighted absolute error,ITAE)准则作为寻优目标函数,对PI控制器的比例、积分参数进行调整、寻优,使SVC系统的响应过程达到最优。仿真和实验结果表明,该最优PI控制器能快速跟踪SVC系统的电压设定值,基于该PI控制器的SVC能迅速进行无功补偿,具有较强的适应性和较高的补偿精度。
文摘提出了一种基于蚁群优化和粒子群优化的混合算法求解TSP(Traveling Salesm an Prob lem)问题。在应用蚁群算法对TSP问题的求解过程中,利用粒子群算法对蚁群系统的参数进行优化,其目的是提高蚁群系统的优化性能,使蚁群系统的参数不必靠人工经验或反复试验选取,而是通过粒子搜索自适应选取。
基金supported by the National Natural Science Foundation of China (61104180)the National Basic Research Program of China(973 Program) (97361361)
文摘Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance and urgency（e.g.,observation tasks orienting to the earthquake area and military conflict area）,have not been taken into account yet.Therefore,it is crucial to investigate the satellite integrated scheduling methods,which focus on meeting the requirements of emergency tasks while maximizing the profit of common tasks.Firstly,a pretreatment approach is proposed,which eliminates conflicts among emergency tasks and allocates all tasks with a potential time-window to related orbits of satellites.Secondly,a mathematical model and an acyclic directed graph model are constructed.Thirdly,a hybrid ant colony optimization method mixed with iteration local search（ACO-ILS） is established to solve the problem.Moreover,to guarantee all solutions satisfying the emergency task requirement constraints,a constraint repair method is presented.Extensive experimental simulations show that the proposed integrated scheduling method is superior to two-phased scheduling methods,the performance of ACO-ILS is greatly improved in both evolution speed and solution quality by iteration local search,and ACO-ILS outperforms both genetic algorithm and simulated annealing algorithm.
基金Supported by National Natural Science Foundation of China （60496322）, Natural Science Foundation of Beijing （4083034）, and Scientific Research Common Program of Beijing Municipal Commission.of Education （KM200610005020）_ _ _
