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.

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.

Cooperative UAV search strategy based on DMPC-AACO algorithm in restricted communication scenarios

Improvement of integrated battlefield situational awareness in complex environments involving dynamic factors such as restricted communications and electromagnetic interference(EMI)has become a contentious research problem.In certain mission environments,due to the impact of many interference sources on real-time communication or mission requirements such as the need to implement communication regulations,the mission stages are represented as a dynamic combination of several communication-available and communication-unavailable stages.Furthermore,the data interaction between unmanned aerial vehicles(UAVs)can only be performed in specific communication-available stages.Traditional cooperative search algorithms cannot handle such situations well.To solve this problem,this study constructed a distributed model predictive control(DMPC)architecture for a collaborative control of UAVs and used the Voronoi diagram generation method to re-plan the search areas of all UAVs in real time to avoid repetition of search areas and UAV collisions while improving the search efficiency and safety factor.An attention mechanism ant-colony optimization(AACO)algorithm is proposed for UAV search-control decision planning.The search strategy is adaptively updated by introducing an attention mechanism for regular instruction information,a priori information,and emergent information of the mission to satisfy different search expectations to the maximum extent.Simulation results show that the proposed algorithm achieves better search performance than traditional algorithms in restricted communication constraint scenarios.

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.

Improved algorithms to plan missions for agile earth observation satellites

This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.

AC-PSO ALGORITHM FOR UAV MISSION PLANNING

Choosing the best path during unmanned air vehicle （UAV） flying is the target of the UAV mission planning problem. Because of its nearly constant flight height, the UAV mission planning problem can be treated as a 2-D （horizontal） path arrangement problem. By modeling the antiaircraft threat, the UAV mission planning can be mapped to the traveling seaman problem （TSP）. A new algorithm is presented to solve the TSP. The algorithm combines the traditional ant colony system （ACS） with particle swarm optimization （PSO）, thus being called the AC-PSO algorithm. It uses one by one tour building strategy like ACS to determine that the target point can be chosen like PSO. Experiments show that AC-PSO synthesizes both ACS and PSO and obtains excellent solution of the UAV mission planning with a higher accuracy.

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 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.

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.

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.

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。

基于分层多智能体的多星联合任务规划架构研究

随着卫星规模、种类的不断增长,现有卫星任务规划技术架构已很难满足多类型卫星联合运用的有效性及可扩展性要求。通过深入分析多星任务筹划、单星任务规划、数传资源规划等环节,建立了基于分层多智能体的多星联合任务规划技术架构,设计了与架构匹配的相关优化算法及智能体功能实现机制,实现了多星联合任务规划能力。仿真实验及分析结果表明,该架构对多类型卫星联合任务规划场景有良好的适用性。

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。