无人机快速逃逸过程中障碍物自动识别仿真

在无人机快速逃逸过程中,为了保证逃逸成功,需要对追击者设置的突变障碍物进行准确的视觉识别.由于障碍物对无人机丽言,在出现的位置与出现的形式上,存在很强的突变性,传统的立体视觉模式识别方法中,障碍物在机器视觉需要建立高纬度模型表示障碍物的突变性和非线性,导致识别过程中的立体视差存在较大偏差,无法快速、准确识别出障碍物信息.提出了非参数核密度的无人机快速逃逸过程中障碍物自动识别模型,应用高斯核密度估计进行背景建模,对视频序列中的像素点进行概率密度分析,利用对像素点阈值进行设置,采集障碍物目标,采用连续视频序列中的多帧差分法,构建背景的自适应更新模型,克服光照、抖动等因素对障碍物目标背景重建的影响,实现无人机快速逃逸过程中障碍物自动识别.实验结果说明,采用所提方法可获取准确的障碍物自动识别效果.     

单个追击者与两目标追逃对抗建模及仿真

本文建立了单个追击者与两目标追逃对抗微分对策模型,给出了追击者可达区域覆盖逃避者可达区域的条件,构造了追击者与逃避者的最优位置策略和、被捕获的初始点集区域,最后给出了相应的计算机仿真实例。     

基于多agent系统的大规模无人机集群对抗

本文将多agent系统引入到大规模无人机集群对抗决策系统中,给出了基于多agent系统的大规模无人机集群对抗决策方法.将机群中的每个无人机视为一个独立agent,建立了无人机运动模型,为无人机设计了独立的个体行为集,并针对每种行为给出了决策方法.通过每个个体无人机对其邻域环境的作用,涌现出宏观的集群对抗(作战)效果.使用MATLAB仿真软件对所设计的大规模无人机集群对抗方法进行了仿真,验证了所设计的基于多agent系统的大规模无人机集群对抗决策方法的有效性.     

基于HLA的反辐射无人机系统仿真研究

基于HLA建立仿真应用系统是提高作战仿真活动效率、检验武器系统作战效能的有效途径.分析了反辐射无人机与雷达对抗仿真系统的组成和特点,介绍了基于HLA的反辐射无人机仿真系统的体系结构,开发了系统的FOM/SOM表,探讨了基于HLA的仿真应用系统开发的一般过程,给出了反辐射无人机与雷达对抗仿真系统的设计实例.     

仿鹰群智能的无人机集群协同对抗飞行验证

自主能力强且低成本的无人机集群协同对抗,是无人机集群对抗中打击敌方攻击防御体系和拦截敌方入侵机群的一个重要手段.哈里斯鹰是一种集群狩猎的猛禽,集群狩猎对于哈里斯鹰获取维持生命活动所需的能量具有重要意义.从无人机集群协同对抗任务与哈里斯鹰协同狩猎行为相似性出发,本文提出一种仿鹰群智能的无人机集群协同对抗方法.首先通过分析鹰群的集群狩猎行为,建立鹰群智能行为机制,并将其映射到无人机集群协同对抗行为中;在该模型的基础上,利用李雅普诺夫导航向量场控制无人机的运动状态,使得我方无人机能够以恒定的速度收敛到预定的轨迹上,完成对敌方无人机的对抗打击;最后,搭建无人机集群验证平台,对所设计的仿鹰群无人机集群协同对抗模型进行外场飞行验证,试验结果验证了本文所设计的模型在无人机对抗环境中的可行性与有效性.     

导弹攻击下带矢量推力无人战斗机逃逸概率分析

无人战斗机逃逸概率是无人机设计的重要指标之一,提高无人机飞行时的可用过载能够提高其逃逸概率,另外无人机的机动也对其逃逸概率有重要影响,如何评判在各种机动情况下无人机对导弹的平均逃逸概率与其过载的关系是无人机设计师和军方十分关注的问题;论文以带矢量推力无人战斗机(UCAVVT)作为研究对象,建立其飞行动力学及导弹导引飞行控制数学模型,通过预先假定无人机做典型机动的概率,由计算机仿真导弹与无人机的相对飞行轨迹,研究讨论在各种机动下UCAVVT在导弹攻击下的平均逃逸概率,并对计算结果进行了分析,得出了一些重要结论,对带矢量推力无人战斗机的总体设计具有一定的参考价值。     

基于终端诱导强化学习的航天器轨道追逃博弈

针对脉冲推力航天器轨道追逃博弈问题,提出一种基于强化学习的决策方法,实现追踪星在指定时刻抵近至逃逸星的特定区域,其中两星都具备自主博弈能力.首先,充分考虑追踪星和逃逸星的燃料约束、推力约束、决策周期约束、运动范围约束等实际约束条件,建立锥形安全接近区及追逃博弈过程的数学模型;其次,为了提升航天器面对不确定博弈对抗场景的自主决策能力,以近端策略优化(Proximal policy optimization, PPO)算法框架为基础,采用左右互搏的方式同时训练追踪星和逃逸星,交替提升两星的决策能力;在此基础上,为了在指定时刻完成追逃任务,提出一种终端诱导的奖励函数设计方法,基于CW (Clohessy Wiltshire)方程预测两星在终端时刻的相对误差,并将该预测误差引入奖励函数中,有效引导追踪星在指定时刻进入逃逸星的安全接近区.与现有基于当前误差设计奖励函数的方法相比,所提方法能够有效提高追击成功率.最后,通过与其他学习方法仿真对比,验证提出的训练方法和奖励函数设计方法的有效性和优越性.     

基于粒子群算法的无人机航路规划与建模仿真

研究无人机航路规划问题,解决基本粒子群算法易陷入局部最优、收敛速度慢长导致人机作航路规划效率低的难题.为了提高无人机航路规划效率,提出了一种基于改进粒子群算法的无人机航路规划方法.在无人机航路规划建模过程中,如果粒子失活,该算法对其进行相应的变异与微调,重新激活粒子,保证了粒子群体在进化过程中具有较强的活力,能够快速逃逸出局部极值点,这样就以较快收敛速度找到最优航路.最后用改进的粒子群算法对无人机任务航路进行了仿真,仿真结果表明,相对于基本粒子群算法,该方法避免了陷入局部最优,并缩短了搜索时间,航路规划效率明显提高.该算法是一种有效的无人机航路优化算法.     

无人机飞行仿真系统多智能体建模分析与应用研究

无人机飞行仿真系统包含多个功能模块,采用多智能体(multi-Agent)技术研究无人机飞行仿真系统,分析并建立基于多智能体的飞行仿真系统的层次结构和运行机制,进而利用智能体对无人机仿真模型的各子模块模型运行过程进行建模,最后在JADE环境下建立了多智能体无人机飞行仿真系统的实例模型.这种方法可以通过仿真模型各子系统智能体之间的交互来完成无人机飞行仿真系统中飞行仿真的主要功能,从而满足无人机模拟训练系统的设计需求,在无人机模拟训练系统研究中具有广阔的应用前景.     

规则耦合下的多异构子网络MADDPG博弈对抗算法

针对多无人机博弈对抗过程中无人机数量动态衰减问题和传统深度强化学习算法中的稀疏奖励问题及无效经验抽取频率过高问题,本文以攻防能力及通信范围受限条件下的多无人机博弈对抗任务为研究背景,构建了红、蓝两方无人机群的博弈对抗模型,在多智能体深度确定性策略梯度(multi-agent deep deterministic policy gradient, MADDPG)算法的Actor-Critic框架下,根据博弈环境的特点对原始的MADDPG算法进行改进。为了进一步提升算法对有效经验的探索和利用,本文构建了规则耦合模块以在无人机的决策过程中对Actor网络进行辅助。仿真实验表明,本文设计的算法在收敛速度、学习效率和稳定性方面都取了一定的提升,异构子网络的引入使算法更适用于无人机数量动态衰减的博弈场景;奖励势函数和重要性权重耦合的优先经验回放方法提升了经验差异的细化程度及优势经验利用率;规则耦合模块的引入实现了无人机决策网络对先验知识的有效利用。     

UAVs in Urban Operations: Target Interception and Containment

In future urban warfare scenarios, the expectations placed on unmanned aerial vehicles (UAVs) in terms of autonomy, reliability and cooperation will be significantly increased. In this paper, a novel algorithm is developed which enables a swarm UAVs to contain and intercept multiple evading targets. In particular, the algorithm allows a swarm of UAVs to intercept significantly faster evading targets if there are more UAVs than targets. Several numerical experiments are conducted, in which four pursuers attempt to intercept one or two faster evaders. The algorithm is very effective at containing the targets; however, as the number of evading targets increases the speed of the evaders must be reduced to ensure capture. Finally, a scenario in which there are more evading targets than pursuers is considered, and, given that the evaders were slower than the pursers, containment and capture was successfully accomplished.     

基于协同进化的平面追逃对策研究

建立了二维平面内动力学约束下迫逃运动的数学模型.首先为追捕者设计了基于比例制导算法和进化算法的混合追捕策略,以提高其追捕能力;然后利用协同进化算法对追捕者和逃跑者的追选策略进行进化.仿真结果表明,进化后的逃跑策略能有效规避比例制导的追捕者.逃跑者在协同进化过程中涌现出众多复杂多变的规避策略.     

Coordinated pursuer control using particle filters for autonomous search-and-capture

In a real-world pursuit-evasion (PE) game, the pursuers often have a limited field-of-view of the evaders and thus are required to search for and detect the evaders before capturing them. This paper presents a unified framework and control algorithm using particle filters (PFs) for the coordination of multiple pursuers to search for and capture multiple evaders given the ability of PF to estimate highly non-Gaussian densities prevalent in search problems. The pursuer control problem is formulated as a stochastic control problem where global objectives function of both searching and capturing are common. To take the evaders’ actions into account, an action measure (AM) is defined over the evaders’ PDs is used to represent the probability that the evader may transit each state in the PD. The global objective functions for search and capture are then decomposed into local objective functions for unification through objective priority weights. Coordination between the pursuers takes place through the multi-sensor update where the observation likelihoods of all pursuers are used in the PF update stage. The control actions of each pursuer are then determined individually, based on the updated PDs given the objective weights, action measures as well as evader importance weights in the case of multiple evaders. The proposed algorithm is tested in three scenarios for its effectiveness. In addition, a parametric study on the average capture time against the initial variances of the target state uncertainty is conducted to test for robustness. Results show that the pursuers are able to capture all the evaders in each case with the capture time for the second and last scenario differing by only 2.9% implying firstly that under the proposed algorithm, the capture time is not proportional to the increase in the number of evaders and also suggested robustness and potential scalability of the proposed algorithm.     

Policy Improvements for Probabilistic Pursuit-Evasion Game

This paper focuses on a pursuit-evasion game (PEG) which involves two teams: one side consists of pursuers trying to minimize the time required to capture evaders, and the other side consists of evaders trying to maximize the capture time by escaping the pursuers. In this paper, we propose a hybrid pursuit policy for a probabilistic PEG, which possesses the combined merits of local-max and global-max pursuit policies proposed in previous literature. A method to find optimal pursuit and evasion polices for two competitive parties of the pursuers and evaders is also proposed. For this, we employ an episodic parameter optimization (EPO) algorithm to learn good values for the weighting parameters of a hybrid pursuit policy and an intelligent evasion policy. The EPO algorithm is performed during the numerous repeated simulation runs of the PEG and the reward of each episode is updated using reinforcement learning, and the optimal weighting parameters are selected by using particle swarm optimization. We analyze the trend of the optimal parameter values with respect to the number of the pursuers and evaders. The proposed strategy is validated both in simulations and experiments with small ground robots.     

Soft capture of two coordinated evaders

Sufficient conditions for the soft capture of two evaders are obtained for a linear problem of pursuit of two evaders by a group of pursuers. It is assumed that the capabilities of all the participants are equal, and the same control is used by the evaders.     

A non-stationary problem of group pursuit

A linear non-stationary conflict-interaction problem for controlled objects with n pursuers and m evaders with equal dynamical potency of each participant is considered. The objective of the pursuers is to capture all the evaders; the objective of the evaders is to avoid overtaking of at least one of them. Sufficient conditions for the global evasion problem solvability are stated.     

Scalable and practical pursuit-evasion with networked robots

In this paper, we consider the design and implementation of practical pursuit-evasion games with networked robots, where a communication network provides sensing-at-a-distance as well as a communication backbone that enables tighter coordination between pursuers. We first develop, using the theory of zero-sum games, an algorithm that computes the minimal completion time strategy for pursuit-evasion when pursuers and evaders have same speed, and when all players make optimal decisions based on complete knowledge. Then, we extend this algorithm to when evader are significantly faster than pursuers. Unfortunately, these algorithms do not scale beyond a small number of robots. To overcome this problem, we design and implement a partition algorithm where pursuers capture evaders by decomposing the game into multiple multi-pursuer single-evader games. We show that the partition algorithm terminates, has bounded capture time, is robust, and is scalable in the number of robots. We then describe the design of a real-world mobile robot-based pursuit evasion game. We validate our algorithms by experiments in a moderate-scale testbed in a challenging office environment. Overall, our work illustrates an innovative interplay between robotics and communication.     

Problem of pursuit of a group of coordinated evaders in linear recurrent differential games

For a linear nonstationary problem of pursuit of a group of evaders by a group of pursuers under the condition that the fundamental matrix of the homogeneous system is a recurrent function and all the evaders use the same control, sufficient conditions for capturing at least one evader are found.     

基于输入约束一致性算法的多无人机编队控制

针对多无人机系统的编队控制问题,提出了一种基于级联系统理论及输入约束一致性算法的控制方法。首先,给出了垂直起降无人机系统的一般性模型。然后,基于级联系统理论将复杂的一般性模型化简成级联形式,针对级联形式模型,利用双曲正切函数的有界性质,在控制器设计时引入双曲正切函数设计了一致性控制算法。最后基于所设计的输入约束一致性控制算法,设计编队控制算法研究了多无人机编队控制问题。基于Matlab仿真平台对所提控制方法进行验证,仿真结果表明在所设计的一致性控制算法作用下,系统中所有的状态都能够趋于一致。基于所设计的输入约束一致性算法,所提编队控制算法可以实现空间中的无人机保持指定的编队队形飞行。     

基于元胞自动机的蜂群无人机故障影响模型

基于固定翼无人机飞行特性以及蜂群无人机控制策略,针对无人机控制器遭受恶意攻击的情形,采用时序网络与元胞自动机理论分析蜂群无人机故障影响机理.首先,通过时序网络分析蜂群无人机拓扑网络的变化情况,提出基于跳数的故障传播路径的确定方法;其次,考虑蜂群无人机状态信息,建立符合蜂群无人机特征的元胞对象,同时基于局部信息交互原则,确定元胞自动机的状态演变规则,并依据近邻信息对无人机控制律的影响,提出矢量投影法来确定故障影响权值,辨识出各无人机故障影响程度的动态变化情况;最后,建立仿真模型,利用预测与实际故障影响程度结果,基于DCG算法与模式距离验证所建故障影响模型的有效性.