针对当前装备体系(system of systems,SoS)任务建模研究深入程度不足问题,提出装备SoS使命任务的概念模型和描述模型,在此基础上,首先对各层级任务的任务线程进行分析与规划,改进传统Petri网,提出一种基于层次确定与随机Petri网(hierarchical deterministic and stochastic Petri nets,HDSPN)的装备SoS任务线程建模方法,构建面向多层级使命任务的装备SoS任务线程模型。然后,结合基于可达性分析算法(reachability analysis algorithm,RAA)的装备SoS总体任务成功性仿真评估算法,启动仿真模型运行,实现对装备SoS总体任务成功性的有效评估,并通过案例分析,验证了模型的适用性。
Einstein Probe,an astronomical satellite designed for X-ray observation on astronomical events drastically evolving over time,was successfully sent into preset orbit by a Long March 2C rocket from China’s Xichang Satellite Launch Center located in Sichuan Province at 15:03 GMT+8 on January 9,2024.
对敌防空压制(suppression of enemy air defenses, SEAD)场景是多无人机协同的典型应用,针对该场景特点,在任务规划问题基础上将各类型无人机数量也作为决策变量,充分表征目标、任务和无人机的多种约束,建立异构无人机编队路径问题模型。设计了双层联合优化方法求解该模型:上层设计了任务衔接参数指标,精确评估各类型无人机需求,指导无人机配置调整;下层设计了改进遗传算法,高效处理多类型约束并能结合无人机数量变化对任务方案进行精细调整;双层相互协调获得满足需求的无人机配置和执行方案。仿真结果表明,该方法可以在避免遍历无人机配置组合的前提下获得合理的无人机配置方案和高效可行的执行方案。
It is important to calculate the reachable domain(RD)of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient databasegeneration method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit’s inclination and right ascension of ascending node(RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than 0.01°on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.
Combining multiple crop protection Unmanned Aerial Vehicles(UAVs)as a team for a scheduled spraying mission over farmland now is a common way to significantly increase efficiency.However,given some issues such as different configurations,irregular borders,and especially varying pesticide requirements,it is more important and more complex than other multi-Agent Systems(MASs)in common use.In this work,we focus on the mission arrangement of UAVs,which is the foundation of other high-level cooperations,systematically propose Efficiency-first Spraying Mission Arrangement Problem(ESMAP),and try to construct a united problem framework for the mission arrangement of crop protection UAVs.Besides,to characterise the differences in sub-areas,the varying pesticide requirement per unit is well considered based on Normalized Difference Vegetation Index(NDVI).Firstly,the mathematical model of multiple crop-protection UAVs is established and ESMAP is defined.Furthermore,an acquisition method of a farmland’s NDVI map is proposed,and the calculation method of pesticide volume based on NDVI is discussed.Secondly,an improved Genetic Algorithm(GA)is proposed to solve ESMAP,and a comparable combination algorithm is introduced.Numerical simulations for algorithm analysis are carried out within MATLAB,and it is determined that the proposed GA is more efficient and accurate than the latter.Finally,a mission arrangement tested with three UAVs was carried out to validate the effectiveness of the proposed GA in spraying operation.Test results illustrated that it performed well,which took only 90.6%of the operation time taken by the combination algorithm.
Yang LiYanqiang WuXinyu XueXuemei LiuYang XuXinghua Liu
