Optimal confrontation position selecting games model and its application to one-on-one air combat

In the air combat process,confrontation position is the critical factor to determine the confrontation situation,attack effect and escape probability of UAVs.Therefore,selecting the optimal confrontation position becomes the primary goal of maneuver decision-making.By taking the position as the UAV’s maneuver strategy,this paper constructs the optimal confrontation position selecting games(OCPSGs)model.In the OCPSGs model,the payoff function of each UAV is defined by the difference between the comprehensive advantages of both sides,and the strategy space of each UAV at every step is defined by its accessible space determined by the maneuverability.Then we design the limit approximation of mixed strategy Nash equilibrium(LAMSNQ)algorithm,which provides a method to determine the optimal probability distribution of positions in the strategy space.In the simulation phase,we assume the motions on three directions are independent and the strategy space is a cuboid to simplify the model.Several simulations are performed to verify the feasibility,effectiveness and stability of the algorithm.

An Air Route Planning Model of Unmanned Aerial Vehicles Under Constraints of Ground Safety

With the rapid growth of the number and flight time of unmanned aerial vehicles(UAVs),safety accidents caused by UAVs flight risk is increasing gradually.Safe air route planning is an effective means to reduce the operational risk of UAVs at the strategic level.The optimal air route planning model based on ground risk assessment is presented by considering the safety cost of UAV air route.Through the rasterization of the ground surface under the air route,the safety factor of each grid is defined with the probability of fatality on the ground per flight hour as the quantitative index.The air route safety cost function is constructed based on the safety factor of each grid.Then,the total cost function considering both air route safety and flight distance is established.The expected function of the ant colony algorithm is rebuilt and used as the algorithm to plan the air routes.The effectiveness of the new air route planning model is verified through the logistical distribution scenario on urban airspace.The results indicate that the new air route planning model considering safety factor can greatly improve the overall safety of air route under small increase of the total flight time.

Multi-UAVs Collaborative Path Planning in the Cramped Environment

Due to its flexibility and complementarity, the multiUAVs system is well adapted to complex and cramped workspaces, with great application potential in the search and rescue(SAR) and indoor goods delivery fields. However, safe and effective path planning of multiple unmanned aerial vehicles(UAVs)in the cramped environment is always challenging: conflicts with each other are frequent because of high-density flight paths, collision probability increases because of space constraints, and the search space increases significantly, including time scale, 3D scale and model scale. Thus, this paper proposes a hierarchical collaborative planning framework with a conflict avoidance module at the high level and a path generation module at the low level. The enhanced conflict-base search(ECBS) in our framework is improved to handle the conflicts in the global path planning and avoid the occurrence of local deadlock. And both the collision and kinematic models of UAVs are considered to improve path smoothness and flight safety. Moreover, we specifically designed and published the cramped environment test set containing various unique obstacles to evaluating our framework performance thoroughly. Experiments are carried out relying on Rviz, with multiple flight missions: random, opposite, and staggered, which showed that the proposed method can generate smooth cooperative paths without conflict for at least 60 UAVs in a few minutes.The benchmark and source code are released in https://github.com/inin-xingtian/multi-UAVs-path-planner.

A Multi-UCAV cooperative occupation method based on weapon engagement zones for beyond-visual-range air combat

Recent advances in on-board radar and missile capabilities,combined with individual payload limitations,have led to increased interest in the use of unmanned combat aerial vehicles(UCAVs)for cooperative occupation during beyond-visual-range(BVR)air combat.However,prior research on occupational decision-making in BVR air combat has mostly been limited to one-on-one scenarios.As such,this study presents a practical cooperative occupation decision-making methodology for use with multiple UCAVs.The weapon engagement zone(WEZ)and combat geometry were first used to develop an advantage function for situational assessment of one-on-one engagement.An encircling advantage function was then designed to represent the cooperation of UCAVs,thereby establishing a cooperative occupation model.The corresponding objective function was derived from the one-on-one engagement advantage function and the encircling advantage function.The resulting model exhibited similarities to a mixed-integer nonlinear programming(MINLP)problem.As such,an improved discrete particle swarm optimization(DPSO)algorithm was used to identify a solution.The occupation process was then converted into a formation switching task as part of the cooperative occupation model.A series of simulations were conducted to verify occupational solutions in varying situations,including two-on-two engagement.Simulated results showed these solutions varied with initial conditions and weighting coefficients.This occupation process,based on formation switching,effectively demonstrates the viability of the proposed technique.These cooperative occupation results could provide a theoretical framework for subsequent research in cooperative BVR air combat.

A Predator-prey Particle Swarm Optimization Approach to Multiple UCAV Air Combat Modeled by Dynamic Game Theory

Dynamic game theory has received considerable attention as a promising technique for formulating control actions for agents in an extended complex enterprise that involves an adversary. At each decision making step, each side seeks the best scheme with the purpose of maximizing its own objective function. In this paper, a game theoretic approach based on predatorprey particle swarm optimization (PP-PSO) is presented, and the dynamic task assignment problem for multiple unmanned combat aerial vehicles (UCAVs) in military operation is decomposed and modeled as a two-player game at each decision stage. The optimal assignment scheme of each stage is regarded as a mixed Nash equilibrium, which can be solved by using the PP-PSO. The effectiveness of our proposed methodology is verified by a typical example of an air military operation that involves two opposing forces: the attacking force Red and the defense force Blue. © 2014 Chinese Association of Automation.

基于通信信号管制的城市空中交叉口设计研究

为确保城市低空空域内无人机规模化运行安全,缓解低空交通运行瓶颈,本文借鉴地面交通较为成熟的研究成果,结合城市低空及无人机运行特色,提出一种基于通信信号管制的城市空中交叉口设计模型。首先,考虑航路构型设计与航路运行主体,构建无人机运行所需的城市低空航路。其次,从进出口道、冲突响应距离2方面对空中交叉口几何结构进行设计,再从进口航道划分、通信信号管制、无人机高度调整3方面对交通组织进行设计,实现对空中交叉口的基本模型设计。最后,设置仿真实验验证该模型的可行性,并分析不同参数组合下的交叉口综合通行效率。实验结果表明:当飞行速度为18 m/s,纵向间隔为50 m,管制信号周期时长为226 s时,综合通行效率达到最佳。本研究可为未来城市空中交通航路网络精细划设与管理提供支撑,增强低空无人机交通的实践性。

基于需求的城市低空无人机航路网络构建方法

针对城市低空无人机运行需求不断增加的问题,综合考虑城市区域交通需求、空域环境以及无人机性能等限制条件,以最小化网络节点、最大化覆盖需求点和需求量为目标,提出了一种城市低空无人机航路网络构建方法.建立网络节点生成模型和航路网络构建模型;通过改进的greedy算法生成网络节点;结合使用k近邻(k-Nearest Neighbor,K-NN)和Prims算法构建无人机航路网络;选取上海地区物流业务需求量数据进行仿真验证,并运用复杂网络相关指标分析所构建的航路网络性能.结果表明:本文提出的城市低空无人机航路网络构建方法可以同时实现干线和支线航路网络的构建,且干线航路网络实现了网络全连通,还可以结合无人机任务需求设置网络节点实现航路网络灵活规划.

无人机辅助智能边缘网络技术综述

简要介绍了无人机作为空基信息平台的主要应用场景,结合6G多接入网络和用户为中心网络架构,讨论了无人机在构建智能边缘网络中的重要作用。结合无人机平台在不同场景下的通、感、算能力特点,介绍无人机自组网、无人机通感一体化、无人机边缘计算等无人机辅助智能边缘网络关键技术。最后,围绕无人机在构建智能边缘网络过程中与无线接入网新技术结合时产生的新问题,探讨无人机通信信道建模、三维立体部署与路径规划、携能有限与续航问题、无人机网络安全、无人机集群异构网络融合等关键技术挑战和未来研究方向。

基于自由空间光的无人机通信网络关键技术与发展趋势

在当前电磁频谱拥堵和无线电干扰严重的情况下,基于自由空间光(FSO)的无人机(UAV)通信网络作为推进空天地一体化进程的重要一环,得到了学术界和工业界的广泛关注。与传统射频通信相比,FSO通信具有高数据传输速率、低时延和高安全性等优势。然而,FSO链路易受大气信道条件影响,同时UAV高移动性、网络高动态性以及机载资源的有限性给FSO的稳定连接与可靠通信带来了巨大挑战。因此,该文在介绍了FSO传输特性的基础上,着重分析了提升基于FSO的UAV通信网络稳定性与通信质量的关键技术,在此基础上,归纳出高可靠、强智能、长续航的发展趋势,以期为基于FSO的UAV通信网络发展提供参考与借鉴。

基于有限忍耐度鸽群优化的无人机近距空战机动决策

由于对抗双方态势的快速变化,无人机近距空战机动自主决策困难且复杂,是空中对抗的一个难点。对此,提出一种基于有限忍耐度鸽群优化(FTPIO)算法的无人机近距空战机动决策方法。该方法主要包括基于机动动作库的对手行动预测和基于FTPIO算法的机动控制量和执行时间优化求解两个部分。为提升基本鸽群优化(PIO)算法的全局探索能力,引入有限忍耐度策略,在鸽子个体几次迭代中没有找到更优解时对其属性进行一次重置,避免陷入局部最优陷阱。该方法采用的优化变量是无人机运动模型控制变量的增量,打破了机动库的限制。通过和极小极大方法、基本PIO算法和粒子群优化(PSO)算法的仿真对抗测试结果表明,所提出的机动决策方法能够在近距空战中有效击败对手,产生更为灵活的欺骗性机动行为。

基于多指标正交实验设计的UAV编队配系优化

以对抗条件下,无人机(unmanned aerial vehicle,UAV)编队执行对地攻击任务为作战背景,构建多UAV对地攻击模型,利用多指标正交实验设计原理,研究编队配系的优化方法。首先,以武器类型和目标类型为对象,建立对抗双方各自的目标分配模型;其次,以目标探测、识别、杀伤和评估为基本任务,建立编队的攻击过程模型;以任务执行总代价、目标毁伤程度和任务执行时间为评价指标,建立目标函数模型;最后,将UAV的种类和数量视为实验因素和水平,研究基于正交实验设计的编队最优配系方案选择方法。实验结果表明,针对特定的任务要求,该方法可以依据现有的资源条件,得出最优的编队编组策略。

基于改进能量谷优化的多无人机空战目标分配

针对多无人机空战目标分配问题,提出一种基于改进能量谷优化的多无人机空战目标分配方法。首先,建立无人机空战态势评估模型,对多无人机空战目标分配问题进行数学建模。然后,在能量谷优化算法的基础上,对算法中粒子衰变过程进行改进,使能量谷优化算法适用于离散型优化问题,并提出混沌映射初始化和二进制改进两种改进措施。最后,分别开展消融实验与有效性实验对所提方法的有效性进行验证。仿真结果表明,所提改进措施对于在无人机目标分配任务中能量谷优化算法的算法效果具有一定的提升,且改进能量谷优化算法能够适用于多无人机空战目标分配问题,在多无人机空战中具有一定的应用意义。

基于图卷积深度强化学习的协同空战机动决策方法

针对多无人机智能协同空战对抗决策问题,提出了一种基于长短期记忆与竞争图卷积深度强化学习的多机协同空战机动对抗决策方法.首先,对多机协同空战对抗问题进行描述;其次,在竞争Q网络中,引入长短期记忆网络用于处理带有强时序相关性的空战信息,接着,搭建图卷积网络作为多机之间的通信基础,提出基于长短期记忆与竞争图卷积深度强化学习算法的协同空战训练框架,并对协同空战决策训练算法进行了设计.二对一空战仿真结果验证了本文所提出的协同智能对抗决策方法的有效性,其具有决策速度快、学习过程稳定的特点以及适应空战环境快速变化下的协同策略学习能力.

基于正交实验设计的UAV编队配系优化方法

针对多无人机执行对地攻击作战任务的背景,建立了编队对地攻击模型,提出了基于正交实验设计的编队配系优化方法。在考虑多机协同作战因素下,构建了编队目标分配的基本模型;以探测、识别和杀伤为基本过程,构建了攻击过程模型;以空袭方付出代价、防空方毁伤程度和作战效率为依据,构建了目标函数模型,分析了模型中中间变量与目标函数之间的关系;利用正交实验设计原理,研究了编队配系优劣的评价方法。实例分析表明,该方法能够在已有的资源约束条件下,给出编队的最优配系方案。

Cooperative decision-making algorithm with efficient convergence for UCAV formation in beyond-visual-range air combat based on multi-agent reinforcement learning

Highly intelligent Unmanned Combat Aerial Vehicle(UCAV)formation is expected to bring out strengths in Beyond-Visual-Range(BVR)air combat.Although Multi-Agent Reinforcement Learning(MARL)shows outstanding performance in cooperative decision-making,it is challenging for existing MARL algorithms to quickly converge to an optimal strategy for UCAV formation in BVR air combat where confrontation is complicated and reward is extremely sparse and delayed.Aiming to solve this problem,this paper proposes an Advantage Highlight Multi-Agent Proximal Policy Optimization(AHMAPPO)algorithm.First,at every step,the AHMAPPO records the degree to which the best formation exceeds the average of formations in parallel environments and carries out additional advantage sampling according to it.Then,the sampling result is introduced into the updating process of the actor network to improve its optimization efficiency.Finally,the simulation results reveal that compared with some state-of-the-art MARL algorithms,the AHMAPPO can obtain a more excellent strategy utilizing fewer sample episodes in the UCAV formation BVR air combat simulation environment built in this paper,which can reflect the critical features of BVR air combat.The AHMAPPO can significantly increase the convergence efficiency of the strategy for UCAV formation in BVR air combat,with a maximum increase of 81.5%relative to other algorithms.

无人机辅助空中计算的轨迹和功率联合优化方法

无人机(UAV)辅助的空中计算(AirComp)系统为大范围、分布式数据的快速聚合提供了有效的解决方法。该文研究了通过无人机辅助空中计算系统联合轨迹规划与功率优化方法。UAV作为移动基站,通过调整其运动轨迹和地面传感器节点发射功率,实现AirComp系统聚合数据均方误差的最优化。在UAV轨迹、传感器功率限制下,联合优化UAV轨迹、去噪因子和传感器功率,使时间平均均方误差最小化。基于块坐标下降和逐次凸逼近方法,提出无人机飞行轨迹与功率联合优化算法,并通过仿真验证了所提算法的性能。

地空协同场景下加权模糊聚类用户簇划分方法

为了解决应急通信场景下使用无人机作为空中基站进行辅助通信时涉及的无人机基站部署策略中的用户簇划分问题,在兼顾无人机基站性能和用户体验的条件下,提出一种基于特征加权的模糊聚类(Improved FCM)算法。首先,根据每个无人机基站的信号覆盖范围和最大服务用户数量的性能约束,针对随机分布条件下的用户簇在划分过程中算法计算量大不易收敛的问题,提出一种基于距离加权的特征加权节点数据投影算法;其次,针对同一用户处于多个簇有效范围内时用户划分的有效性和无人机基站资源的最大化利用问题,提出一种基于用户位置和无人机基站负载均衡的价值加权算法。实验结果表明,所提方法充分满足无人机基站的服务性能约束,且与几何分形法(GFA)、谱聚类(Sp-C)等算法相比,特征加权模糊聚类算法获得的平均负载率和覆盖比是最优的,分别达到了0.774和0.0263,因此,该算法可为应急通信场景下的用户簇划分问题提供一种可行的解决方案。

无人机应急通信虚拟仿真实验平台建设

为解决通信专业实验课程体系独立且设备昂贵,难以线下进行综合性实验等问题,建设了无人机应急通信虚拟仿真实验平台。通过Unity3D建模、人机交互等技术设计了空天地一体化实验教学环境、多层次多课程贯通的实验教学内容,与实验内容相对应的多场景综合实验考核体系。平台整合了应急组网配置、无人机部署与控制、星-空-地链路传输控制、5G网络规划与配置、应急通信链路性能测试与对比等内容系统化展现专业基础知识在无人机应急通信场景中的综合应用。教学实践表明:学生在有效掌握实验原理与应用的同时,进一步实现对多门课程的知识整合与综合运用,达到培养具有综合通信实践能力的新工科专业人才的目标。

Intermediate carriers for UAV swarms:problem of fleet composition

This article introduces a fleet composition algorithm for a fleet of intermediate carriers, which should deliver a swarm of miniature unmanned aerial vehicles （mini-UAVs） to a mission area. The algorithm is based on the sequential solution of several knapsack problems with various constraints. The algorithm allows both to form an initial set of required types of intermediate carriers, and to generate a fleet of intermediate carriers. The formation of a fleet of intermediate carriers to solve a suppression of enemy air defense （SEAD） problem is presented to illustrate the proposed algorithm.

城市低空物流无人机飞行计划预先调配

针对城市环境下物流无人机(unmanned aerial vehicle,UAV)飞行计划调配问题,以UAV运输成本和延误成本最小为目标,建立多约束物流UAV飞行计划预先调配模型。考虑货物类型优先级、物流公司优先级和配送时间优先级提出综合优先级,设计了基于综合优先级的飞行计划预先调配算法求解。以校园配送为仿真环境进行验证,模型和算法能够生成无冲突的飞行时刻表,实现飞行计划预先差异化调配。实验结果表明,综合优先级调配与传统任务优先调配、先到先服务调配相比,每条飞行计划的成本分别降低了21.69%和26.58%;当综合优先级权重组合为0.2、0.4和0.4时,每条飞行计划的成本最低为5.42元,调配结果最佳。