A Planning Method for Operational Test of UAV Swarm Based on Mission Reliability

The unmanned aerial vehicle(UAV)swarm plays an increasingly important role in the modern battlefield,and the UAV swarm operational test is a vital means to validate the combat effectiveness of the UAV swarm.Due to the high cost and long duration of operational tests,it is essential to plan the test in advance.To solve the problem of planning UAV swarm operational test,this study considers the multi-stage feature of a UAV swarm mission,composed of launch,flight and combat stages,and proposes a method to find test plans that can maximize mission reliability.Therefore,a multi-stage mission reliability model for a UAV swarm is proposed to ensure successful implementation of the mission.A multi-objective integer optimization method that considers both mission reliability and cost is then formulated to obtain the optimal test plans.This study first constructs a mission reliability model for the UAV swarm in the combat stage.Then,the launch stage and flight stage are integrated to develop a complete PMS(Phased Mission Systems)reliability model.Finally,the Binary Decision Diagrams(BDD)and Multi Objective Quantum Particle Swarm Optimization(MOQPSO)methods are proposed to solve the model.The optimal plans considering both reliability and cost are obtained.The proposed model supports the planning of UAV swarm operational tests and represents a meaningful exploration of UAV swarm test planning.

Research on agile space emergency launching mission planning simulation and verification method

Space emergency launching is to send a satellite into space by using a rapid responsive solid rocket in the bounded time to implement the emergency Earth observation mission.The key and difficult points mainly include the business process construction of launching mission planning,validation of the effectiveness of the launching scheme,etc.This paper pro-poses the agile space emergency launching mission planning simulation and verification method,which systematically con-structs the overall technical framework of space emergency launching mission planning with multi-field area,multi-platform and multi-task parallel under the constraint of resource schedul-ing for the first time.It supports flexible reconstruction of mis-sion planning processes such as launching target planning,tra-jectory planning,path planning,action planning and launching time analysis,and can realize on-demand assembly of operation links under different mission scenarios and different plan condi-tions,so as to quickly modify and generate launching schemes.It supports the fast solution of rocket trajectory data and the accurate analysis of multi-point salvo time window recheck and can realize the fast conflict resolution of launching missions in the dimensions of launching position and launching window sequence.It supports lightweight scenario design,modular flexi-ble simulation,based on launching style,launching platform,launching rules,etc.,can realize the independent mapping of mission planning results to two-dimensional and three-dimen-sional visual simulation models,so as to achieve a smooth con-nection between mission planning and simulation.

Joint mission and route planning of unmanned air vehicles via a learning-based heuristic

Unmanned air vehicles(UAVs) have been regularly employed in modern wars to conduct different missions. Instead of addressing mission planning and route planning separately,this study investigates the issue of joint mission and route planning for a fleet of UAVs. The mission planning determines the configuration of weapons in UAVs and the weapons to attack targets, while the route planning determines the UAV’s visiting sequence for the targets. The problem is formulated as an integer linear programming model. Due to the inefficiency of CPLEX on large scale optimization problems, an effective learningbased heuristic, namely, population based adaptive large neighborhood search(P-ALNS), is proposed to solve the model. In P-ALNS, seven neighborhood structures are designed and adaptively utilized in terms of their historical performance. The effectiveness and superiority of the proposed model and algorithm are demonstrated on test instances of small, medium and large sizes. In particular, P-ALNS achieves comparable solutions or as good as those of CPLEX on small-size(20 targets)instances in much shorter time.

Timeline based autonomous mission planning system for deep space exploration

In order to realize the explorer autonomy, the software architecture of autonomous mission management system (AMMS) is given for the deep space explorer, and the autonomous mission planning system, the kernel part of this architecture, is designed in detail. In order to describe the parallel activity, the state timeline is introduced to build the formal model of the planning system and based on this model, the temporal constraint satisfaction planning algorithm is proposed to produce the explorer’s activity sequence. With some key subsystems of the deep space explorer as examples, the autonomous mission planning simulation system is designed. The results show that this system can calculate the executable activity sequence with the given mission goals and initial state of the explorer.

Case Analysis for USV Integrated Mission Planning System

Advanced countries around the world are spurring the development of Unmanned Surface Vehicles (USVs) that can operate autonomously at marine environment. The key enabling technology for such USVs is the mission planning system (MPS) that can autonomously navigate through the harsh waters. The MPS not only has the functions for the navigation, but also has the capabilities, such as obstacle avoidance, malfunction corrections, dealing with unexpected events, return home functions, and many other eventualities that cannot be programmed in advance. The autonomy levels are increasingly moving higher and it is foreseeable that the trend will continue in the future. The main purpose of this paper is the analysis of the MPS onboard the USVs, in terms of the categories, functions, and technological details. Also, we analyze the case study of autonomous mission planning control systems in various fields and introduce the features that constitute the critical functionalities of the mission planning systems.

Remote Operation SW for USV: Part I. Integrated Mission Planning System

The integrated Mission Planning System?(MPS) of Unmanned Surface Vehicle?(USV) refers to the process which can recognize, decide, plan situations and carry out missions, such as human beings, for all incidental or complex events occurring at sea. In the actual operating environment, it is necessary to develop a simulation software environment and analyze, verify it in advance so as to make an appropriate mission plan considering equipment, sensor, fuel, and other available resources. The existing USV mission planning process methodology has several limitations in the analysis of USV missions because the scenario to be tested is limited and autonomy of USV is not considered sufficiently. To overcome these problems, we constructed a process that considers various missions and is more autonomous, and an integrated environment in which to experiment. In this study, we designed a multi-agent based USV Integrated Mission Planning System and modeled each component. In addition, we constructed the USV remote operation S/W based on M&S that user can experiment with the modeled process and verified the usefulness of the developed system through simulations.

Coordinated dynamic mission planning scheme for intelligent multi-agent systems

Mission planning was thoroughly studied in the areas of multiple intelligent agent systems,such as multiple unmanned air vehicles,and multiple processor systems.However,it still faces challenges due to the system complexity,the execution order constraints,and the dynamic environment uncertainty.To address it,a coordinated dynamic mission planning scheme is proposed utilizing the method of the weighted AND/OR tree and the AOE-Network.In the scheme,the mission is decomposed into a time-constraint weighted AND/OR tree,which is converted into an AOE-Network for mission planning.Then,a dynamic planning algorithm is designed which uses task subcontracting and dynamic re-decomposition to coordinate conflicts.The scheme can reduce the task complexity and its execution time by implementing real-time dynamic re-planning.The simulation proves the effectiveness of this approach.

GNN-based temporal knowledge reasoning for UAV mission planning systems

Unmanned aerial vehicles(UAVs)are increasingly applied in various mission scenarios for their versatility,scalability and cost-effectiveness.In UAV mission planning systems(UMPSs),an efficient mission planning strategy is essential to meet the requirements of UAV missions.However,rapidly changing environments and unforeseen threats pose challenges to UMPSs,making efficient mission planning difficult.To address these challenges,knowledge graph technology can be utilized to manage the complex relations and constraints among UAVs,missions,and environments.This paper investigates knowledge graph application in UMPSs,exploring its modeling,representation,and storage concepts and methodologies.Subsequently,the construction of a specialized knowledge graph for UMPS is detailed.Furthermore,the paper delves into knowledge reasoning within UMPSs,emphasizing its significance in timely updates in the dynamic environment.A graph neural network(GNN)-based approach is proposed for knowledge reasoning,leveraging GNNs to capture structural information and accurately predict missing entities or relations in the knowledge graph.For relation reasoning,path information is also incorporated to improve the accuracy of inference.To account for the temporal dynamics of the environment in UMPS,the influence of timestamps is captured through the attention mechanism.The effectiveness and applicability of the proposed knowledge reasoning method are verified via simulations.

Mission Planning and Action Planning for Agile Earth-Observing Satellite with Genetic Algorithm

This paper concerns the mission scheduling problem for an agile Earth-observing satellite. Mission planning and action planning for the satellite are both taking into account. Multiple mission types( including multi-strip area,real time download request,and stereoscopic request) and complex satellite actions,such as observe action and data download action,are considered in this paper. Through reasonable analysis of specialties and operational constraints of agile satellites in observing process,the mission scheduling model under multiple objective conditions is constructed. A genetic algorithm combined with heuristic rules is designed to solve problem. Genetic algorithm is designed to arrange user missions and heuristic rules are used to arrange satellite actions. Experiment results suggest that our algorithm works well for the agile Earth-observing satellite scheduling problem.

Agile Satellite Mission Planning via Task Clustering and Double-Layer Tabu Algorithm

Satellite observation schedule is investigated in this paper.A mission planning algorithm of task clustering is proposed to improve the observation efficiency of agile satellite.The newly developed method can make the satellite observe more targets and therefore save observation resources.First,for the densely distributed target points,a preprocessing scheme based on task clustering is proposed.The target points are clustered according to the distance condition.Second,the local observation path is generated by Tabu algorithm in the inner layer of cluster regions.Third,considering the scatter and cluster sets,the global observation path is obtained by adopting Tabu algorithm in the outer layer.Simulation results show that the algorithm can effectively reduce the task planning time of large-scale point targets while ensuring the optimal solution quality.

Self-organized search-attack mission planning for UAV swarm based on wolf pack hunting behavior

Cooperative search-attack is an important application of unmanned aerial vehicle(UAV)swarm in military field.The coupling between path planning and task allocation,the heterogeneity of UAVs,and the dynamic nature of task environment greatly increase the complexity and difficulty of the UAV swarm cooperative search-attack mission planning problem.Inspired by the collaborative hunting behavior of wolf pack,a distributed selforganizing method for UAV swarm search-attack mission planning is proposed.First,to solve the multi-target search problem in unknown environments,a wolf scouting behavior-inspired cooperative search algorithm for UAV swarm is designed.Second,a distributed self-organizing task allocation algorithm for UAV swarm cooperative attacking of targets is proposed by analyzing the flexible labor division behavior of wolves.By abstracting the UAV as a simple artificial wolf agent,the flexible motion planning and group task coordinating for UAV swarm can be realized by self-organizing.The effectiveness of the proposed method is verified by a set of simulation experiments,the stability and scalability are evaluated,and the integrated solution for the coupled path planning and task allocation problems for the UAV swarm cooperative search-attack task can be well performed.

Space station short-term mission planning using ontology modelling and time iteration

This paper studies the problem of the space station short-term mission planning, which aims to allocate the executing time of missions effectively, schedule the corresponding resources reasonably and arrange the time of the astronauts properly. A domain model is developed by using the ontology theory to describe the concepts, constraints and relations of the planning domain formally, abstractly and normatively. A method based on time iteration is adopted to solve the short-term planning problem. Meanwhile, the resolving strategies are proposed to resolve different kinds of conflicts induced by the constraints of power, heat, resource, astronaut and relationship. The proposed approach is evaluated in a test case with fifteen missions, thirteen resources and three astronauts. The results show that the developed domain ontology model is reasonable, and the time iteration method using the proposed resolving strategies can successfully obtain the plan satisfying all considered constraints.

A Sightseeing Planning Support System with Gamification

In recent years, as there has been a major change in the necessity and importance of sightseeing information, a platform to provide real-time sightseeing information according to the ever-changing circumstances is necessary. Additionally, it is effective to adopt gamification to increase users’ motivation to continuously utilize the system in order to provide them with more information. In the present study, in order to support users’ enjoyment in creating efficient and pleasant sightseeing plans, the system that incorporates gamification to increase motivation was developed combining with web-geographic information systems (Web-GIS) and sightseeing planning and sharing system. The system was operated over a period of 2 weeks in Chofu City, Tokyo Metropolis, Japan. Based on the results of a questionnaire survey for 51 users, though the operability of the 3 main functions incorporated with motivation by gamification was rated lower than those of the 2 basic functions, their usefulness was highly rated. Based on the results of the access log analysis, it was effective to design the system so that the same functions can be used regardless of the type of information terminal. Additionally, it was evident that the continuous utilization of the system could increase the number of sightseeing plans created by the users.

A multiple heterogeneous UAVs reconnaissance mission planning and re-planning algorithm

Reconnaissance mission planning of multiple unmanned aerial vehicles(UAVs)under an adversarial environment is a discrete combinatorial optimization problem which is proved to be a non-deterministic polynomial(NP)-complete problem.The purpose of this study is to research intelligent multiUAVs reconnaissance mission planning and online re-planning algorithm under various constraints in mission areas.For numerous targets scattered in the wide area,a reconnaissance mission planning and re-planning system is established,which includes five modules,including intelligence analysis,sub-mission area division,mission sequence planning,path smoothing,and online re-planning.The intelligence analysis module depicts the attribute of targets and the heterogeneous characteristic of UAVs and computes the number of sub-mission areas on consideration of voyage distance constraints.In the sub-mission area division module,an improved K-means clustering algorithm is designed to divide the reconnaissance mission area into several sub-mission areas,and each sub-mission is detected by the UAV loaded with various detective sensors.To control reconnaissance cost,the sampling and iteration algorithms are proposed in the mission sequence planning module,which are utilized to solve the optimal or approximately optimal reconnaissance sequence.In the path smoothing module,the Dubins curve is applied to smooth the flight path,which assure the availability of the planned path.Furthermore,an online re-planning algorithm is designed for the uncertain factor that the UAV is damaged.Finally,reconnaissance planning and re-planning experiment results show that the algorithm proposed in this paper are effective and the algorithms designed for sequence planning have a great advantage in solving efficiency and optimality.

Method of the Mission Planning for the Communication between the Small Satellite Clusters

Mission planning of space astronomical satellite is a complex optimization problem,which is to determine the communication activities needed by space astronomical and research the in-orbit plan.By abstracting the relevant elements of the mission planning problems of space astronomical satellite and establishing the mathematical model of mission planning of the space astronomical satellite,we introduce the Genetic Algorithm and design the single-objective Genetic Algorithm based on the communication mission window.In addition,based on the Genetic Algorithm,a multi-objective Genetic Algorithm based on the sequence of communication window is designed,which improves the coding ability of Genetic Algorithm and improves the flexibility and applicability of planning effect.From the results of planning simulation,this paper not only innovatively introduces Genetic Algorithm into mission planning of satellite and ground data in order to improve the efficiency of mission planning of space astronomical satellite,but also optimizes single-objective mission to multi-objective mission,which improves the applicability of mission planning of satellite communication and provides reference for other relevant researches in the future.

面向战区联合作战的空间需求响应任务规划

针对战区联合作战中的空间需求响应问题,描述了面向战区联合作战的空间需求响应任务规划的背景;从空间资源能力、空间需求响应机制、空间需求响应效益和代价等方面,分析了空间需求响应任务规划所需考虑的主要因素;定义并描述了任务规划模型,设计了任务规划计算方法,并结合战区联合作战实际进行了案例仿真与分析,验证了模型和方法的有效性,为战区联合作战中空间需求的有效响应提供了理论和方法支撑。

分段分策略调度的遥感卫星任务规划架构

随着遥感卫星对各类应用的支撑保障不断深入,卫星任务规划系统面临的外部环境复杂多变,如卫星轨道调整、在轨突发故障、任务随机到达、成像条件变化等,应急突发事件发生的随机性大大提高,传统的定期集中式任务规划架构逐渐无法满足高时效、高响应要求。通过分析系统外部环境种类及变化规律,设计分段分策略调度的任务规划架构,以及相应的调度策略和对象状态管理机制,实现对应急突发事件的高效响应能力。并在某遥感卫星任务管控系统项目中采用该技术架构并研制相应软件。系统实际运行结果表明,该分段分策略的任务规划技术架构基本上满足了外部环境动态变化引起的高适应和高响应要求,极大提升了系统的自动化程度和时效性指标。

面向月球科研站任务的地月准实时遥操作模拟验证系统设计与关键技术研究

针对月球科研站任务地月遥操作需求和特点,设计地月准实时遥操作模拟验证系统,对其中的关键技术海量多尺度遥感数据环境感知、空地协同高精度定位、基于混合现实的预测仿真等进行了研究。提出了基于海量多尺度遥感数据的联合处理方法,环境感知数据融合可处理米级至厘米级环境感知结果;设计基于空地多运动平台协同定位的原型软件,空地协同地标约束下,定位精度不低于轨道器影像1个像素;开发了带有力觉反馈的预测仿真验证平台,在2~3 s变时延约束下可在仿真环境下协同演示遥操作过程,真实还原从端的遥操作作业场景,提高遥操作的执行效率和安全性。为地面人员的方案设计、操作手训练、故障处置等提供实时支持,为我国未来实施月球科研站任务筑牢基础。

目标型政党:一种关于新型政党的理论分析与经验阐释

目标型政党是从马克思主义政党理论中提炼和发展出来的新型政党形态和理论话语。它是一种以马克思主义为指导,以实现人类解放为最终目标,通过构建多层次目标体系,确立多维度使命契约,实施中长期发展计划,接续推动目标实现的政党类型。作为一种目标型政党,中国共产党始终以目标引领国家发展与治理,其提出的奋斗目标具有人民性、系统性、渐进性、问题导向性和可实现性等内在特征。在革命、建设和改革的不同历史时期,中国共产党坚持以自我革命引领社会革命,不断推进政党领导体制和国家制度体系建设,持续构建和完善政府、市场和社会协同互动的社会主义国家治理体系,有目的、有计划、有步骤地推进中国式现代化发展,逐步实现多层次多维度多方面的目标任务。

新能源大基地风光储容量协调优化配置

大型新能源基地需要协调配置风光火储容量以满足外送要求,其中火电容量及外送通道的容量通常预先确定,从而风电、光伏、储能的容量配置成为影响新能源外送效果的重要因素。为此,针对大型新能源基地,研究了风光储容量优化配置问题,提出了基于新能源外送可靠性的容量配置方法。基于需求侧的负荷曲线,提出了一种聚类方法来优化直流通道的计划传输功率;基于直流计划功率,提出了衡量新能源外送可靠性的送电置信概率指标;基于送电置信概率等约束,构建了风光储容量优化配置的基本规划模型。为了进一步保证在不同的场景下新能源能可靠外送,在基本规划模型的基础上加入场景分布不确定性,提出了风光储容量配置的分布鲁棒规划模型。考虑到分布鲁棒约束包含整数变量,提出了一种迭代算法用于该分布鲁棒规划模型的求解。实际算例验证了所提方法的有效性。