Flocking for swarm systems with fixed topology in a changing environment

This paper is mainly devoted to the flocking of a class of swarm with fixed topology in a changing environment. Firstly, the controller for each agent is proposed by employing the error terms between the state of the agent and the average state of its neighbors. Secondly, a sufficient condition for the swarm to achieve flocking is presented under assumptions that the gradient of the environment is bounded and the initial position graph is connected. Thirdly, as the environment is a plane, it is further proved that the velocity of each agent finally converges to the velocity of the swarm center although not one agent knows where the center of the group is. Finally, numerical examples are included to illustrate the obtained results.

Revolutionary entrapment model of uniformly distributed swarm robots in morphogenetic formation

This study proposes a method for uniformly revolving swarm robots to entrap multiple targets,which is based on a gene regulatory network,an adaptive decision mechanism,and an improved Vicsek-model.Using the gene regulatory network method,the robots can generate entrapping patterns according to the environmental input,including the positions of the targets and obstacles.Next,an adaptive decision mechanism is proposed,allowing each robot to choose the most well-adapted capture point on the pattern,based on its environment.The robots employ an improved Vicsek-model to maneuver to the planned capture point smoothly,without colliding with other robots or obstacles.The proposed decision mechanism,combined with the improved Vicsek-model,can form a uniform entrapment shape and create a revolving effect around targets while entrapping them.This study also enables swarm robots,with an adaptive pattern formation,to entrap multiple targets in complex environments.Swarm robots can be deployed in the military field of unmanned aerial vehicles’(UAVs)entrapping multiple targets.Simulation experiments demonstrate the feasibility and superiority of the proposed gene regulatory network method.

关于flocking的综述

本文着重介绍了群及其聚结规则,由雷诺兹启发提出的控制规律确保所有个体的前进方向和速度以渐近线趋向于相同值,并且避免个体间的相互碰撞。阐述了国内外研究的历史和现状,以及对未来发展的预测。本文在总结国内外flocking研究发展的基础上,预测其在航空等方面发展的前途,为我们在新的领域中研究flocking提供了可能。

群机器人Flocking行为研究综述

主要回顾了群机器人自组织flocking行为的研究。首先,介绍群机器人学的含义及关键特征;然后,指出flocking行为在生物学上及机器人学上的意义,之后对目前的研究现状进行介绍。最后,按照以下的三个分类对群机器人flocking行为进行总结:行为控制,硬件设备及交流策略。

Discrimination Between Foreshocks and Earthquake Swarm from the Change of First Motion Sign

Based on the data of first motion of 11 earthquake sequences with ML ≥ 3.0 recorded by the Telemetric Seismic Network of Shanxi since the 1980s, the first motion characteristics of each earthquake sequence were studied. It is known that earthquake sequences of different types have different consistency characteristics of focal mechanism. The decrease and increase of the first motion contradictory sign ratios could be taken as an index to judge whether there would still be a larger earthquake to come after the earthquake sequence.

Multipoint Heave Motion Prediction Method for Ships Based on the PSO-TGCN Model

During ship operations,frequent heave movements can pose significant challenges to the overall safety of the ship and completion of cargo loading.The existing heave compensation systems suffer from issues such as dead zones and control system time lags,which necessitate the development of reasonable prediction models for ship heave movements.In this paper,a novel model based on a time graph convolutional neural network algorithm and particle swarm optimization algorithm(PSO-TGCN)is proposed for the first time to predict the multipoint heave movements of ships under different sea conditions.To enhance the dataset's suitability for training and reduce interference,various filter algorithms are employed to optimize the dataset.The training process utilizes simulated heave data under different sea conditions and measured heave data from multiple points.The results show that the PSO-TGCN model predicts the ship swaying motion in different sea states after 2 s with 84.7%accuracy,while predicting the swaying motion in three different positions.By performing a comparative study,it was also found that the present method achieves better performance that other popular methods.This model can provide technical support for intelligent ship control,improve the control accuracy of intelligent ships,and promote the development of intelligent ships.

Multi-Strategy-Driven Salp Swarm Algorithm for Global Optimization

In response to the shortcomings of the Salp Swarm Algorithm (SSA) such as low convergence accuracy and slow convergence speed, a Multi-Strategy-Driven Salp Swarm Algorithm (MSD-SSA) was proposed. First, food sources or random leaders were associated with the current bottle sea squirt at the beginning of the iteration, to which Levy flight random walk and crossover operators with small probability were added to improve the global search and ability to jump out of local optimum. Secondly, the position mean of the leader was used to establish a link with the followers, which effectively avoided the blind following of the followers and greatly improved the convergence speed of the algorithm. Finally, Brownian motion stochastic steps were introduced to improve the convergence accuracy of populations near food sources. The improved method switched under changes in the adaptive parameters, balancing the exploration and development of SSA. In the simulation experiments, the performance of the algorithm was examined using SSA and MSD-SSA on the commonly used CEC benchmark test functions and CEC2017-constrained optimization problems, and the effectiveness of MSD-SSA was verified by solving three real engineering problems. The results showed that MSD-SSA improved the convergence speed and convergence accuracy of the algorithm, and achieved good results in practical engineering problems.

基于鱼群涌现行为启发的集群机器人硬注意力强化模型

生物集群运动模型能使集群机器人涌现秩序,但是所形成的机器人自然集群秩序难以有效地被人工控制,为此提出鱼群硬注意力模型来解析实验鱼群数据中的交互行为。该模型通过编码器网络、图注意力网络、信息聚合网络、预解码网络以及最终解码网络等结构来获取焦点单体的重要邻居;再利用深度确定性策略梯度技术设计轨道强化网络与安全强化网络,以实现集群的人工控制。多智能体仿真与集群机器人实验结果表明:所提方法能够实现集群的人工轨道、安全控制,重要邻居信息为解决集群运动的强化学习难题提供了新思路,所提控制模型在无人机群空中协作、智慧农机集群作业、物流仓储多体搬运等领域具有较大的应用潜力。

基于改进粒子群算法的UWB雷达人体动作识别研究

针对雷达信号中的杂波干扰及样本数量对人体动作识别精度的限制,提出一种基于改进粒子群算法(particle swarm optimization,PSO)优化支持向量机(support vector machine,SVM)模型的超宽带(ultra-wideband,UWB)雷达人体动作识别算法。利用动态目标指示(moving target indication,MTI)与小波阈值滤波对接收到的UWB回波信号进行预处理,消除回波信号中的杂波和噪声对人体动作识别的影响;结合二维离散小波包分解(two dimensional discrete wavelet packet decomposition,2D-DWPD)与奇异值分解(singular value decomposition,SVD),对预处理后的雷达信号进行特征提取和降维;提出一种改进粒子群算法,优化SVM模型的相关参数进行识别和分类。实验结果表明,提出的算法准确率可达到96.25%,具有良好的识别性能。

基于人工鱼群算法的篮球跳投轨迹实时跟踪

篮球运动轨迹跟踪方法由于轨迹特征提取效果差,导致跟踪误差高,提出一种基于人工鱼群算法的篮球跳投轨迹实时跟踪方法。提取三维坐标系下图像中篮球边缘轮廓信息,通过滤波函数消除篮球跳投图像噪声,基于人工鱼群算法提取图像中篮球跳投轨迹特征,寻找篮球运动轨迹的最优解集,改进篮球实时跟踪匹配路径,形成轨迹跟踪函数。实验结果可知,该方法的平均轨迹跟踪误差为18.28mm,与常规方法相比降低了6.22mm以上。因此,该跟踪方法的篮球轨迹跟踪精度更高,并且实时跟踪轨迹与真实轨迹更吻合。

黎曼流形上的粒子群运动模型

自然界中生物群的协同运动模式多种多样,运动空间的复杂性对该多样性起着关键作用.为描述生物群的协调运动,人们基于欧氏空间提出了多种粒子群运动模型.然而,这些模型往往只能描述简单的运动模式.本文基于Morse势和黎曼流形提出了一个新模型,该模型可以描述任意运动空间中的群体运动,且易于工程实现.本文分别以球面、环面、Möbius带及类丘陵曲面为代表仿真分析了黎曼曲面的紧致性、亏格、可定向性及高斯曲率等性质对模型的影响.结果显示,紧致性导致粒子群聚合得更加紧密对称,非零亏格导致群体中粒子的运动速度趋于一致并阻止粒子群形成类涡流形态,不可定向性导致粒子群在任何条件下都趋于分散,而高斯曲率对粒子群运动行为的影响较小.此外,本文还研究了当曲面包含一个、两个及三个障碍物时模型的输出,结果显示,当势强度足够大时,粒子群能够趋向目标,也能包围目标或绕行障碍物.综上,相比已有模型,该模型能够描述更为丰富的运动模式.

风电变桨减速器振动监测及变桨动作识别

由于变桨减速器随轮毂做圆周运动且伴随间歇性、回转动作,导致变桨有效振动数据识别存在困难,针对此问题,提出了粒子群优化变分模态分解结合滑动中值滤波的变桨动作识别方法。首先,通过现场变桨减速器振动信号分析,将其划分为静态未变桨、静态变桨、动态变桨和动态未变桨4部分,提出基于包络信号的变桨动作识别思路;其次,针对减速器随轮毂旋转导致的正弦分量和趋势分量,利用优化后的变分模态分解进行去除;然后,提出基于信号包络的变桨动作识别思路,采用滑动中值滤波平滑包络信号消除结构激振产生的脉冲干扰;最后,利用静态未变桨数据和3σ准则计算阈值,将平滑后的包络信号曲线与该阈值比较实现变桨动作识别。现场应用表明,该方法可准确识别变桨动作,且与其他方法相比具有明显优势,为风电机组变桨振动识别与状态监测提供参考。

融合改进LM算法及动态时间规整算法的人体动作捕捉研究

人体动作捕捉是指通过技术手段捕捉人体的运动轨迹和姿态信息,广泛应用于娱乐、体育、医疗等领域.然而,现有的动作捕捉技术存在捕捉不准确、计算效率低等问题,影响了其在实时应用场景中的表现.针对这些问题,引入一种骨骼点坐标拟合优化Levenberg-Marquardt算法,并采用粒子群算法对其加以优化.同时采用动态时间规整算法进行人体动作捕捉及评估,以期实现人体动作的实时捕捉.结果显示,该算法对肩部侧平举这一动作的捕捉准确率最高达到了99.23%,明显优于其余对比算法.此外,该算法对深蹲动作的测试时间最短,仅为1.15 s,表明该算法在进行动作捕捉时具有显著的性能优势及很强的实际应用效果,为人体动作捕捉领域提供了新的解决方案.

动态拓扑下四旋翼无人机集群蜂拥控制

针对动态拓扑下无严格构型约束的无人机集群协同控制问题,基于四旋翼无人机(quadrotor unmanned aerial vehicle,QUAV)内外环串级控制思想,提出一种QUAV集群蜂拥控制策略。引入卡尔曼一致性滤波(Kalman-consensus filter,KCF)算法对带有噪声的通信数据进行融合,实现对变速度领导者状态的精确估计;考虑无人机集群拓扑的动态变化及可扩展性需求,设计基于KCF的蜂拥控制算法以实现无人机集群位置控制,利用李雅普诺夫稳定性定理证明算法的稳定性;基于大脑情感学习(brain emotional learning,BEL)模型设计姿态控制器,实现了QUAV的姿态控制。通过仿真实验验证了控制算法的有效性。

基于无界排斥的各向异性Swarm模型

为提高集群运动对环境的适应性,提出了一种基于无界排斥作用的各向异性Swarm模型.各向异性性质使个体之间的相互作用不仅取决于个体间的距离,还与所处势场环境等因素相关,使得集群运动更能适应环境的变化;通过采用无界排斥作用避免了个体之间发生碰撞,保证了各个个体位置的独立性.证明了在线性势场环境中集群中心的聚集运动和集群收敛的稳定性.仿真结果表明,基于此模型的Swarm系统可以聚集成群或列队行进,通过对各向异性参数的控制可以实现集群整体结构和队形的变化.

篮球投篮机器人手臂运动轨迹优化方法研究

篮球投篮机器人是对投篮动作进行规划和控制的机械装置,而手臂是机器人投篮过程中的关键部位。在投篮过程中,机器人手臂易发生非可抗振动,影响机器人投篮轨迹的参数起伏较大。提出了一种篮球投篮机器人手臂运动轨迹优化控制方法。该方法建立了篮球投篮机器人手臂的运动模型和反馈模型,获取投篮机器人在运动过程中的相关数据。以此为依据采用五次多项式插值方法,结合粒子群优化算法和迭代方法,优化控制篮球投篮机器人手臂运动轨迹。实验结果表明,所提方法能够有效优化控制机器人手臂运动轨迹,在运动过程中保持轨迹的角位移、角速度、角加速度和振动响应起伏稳定。

面向空地中继网络优化的无人机运动控制方法

本文提出一种基于模型的通信中继无人机运动控制方法,旨在提高地面车辆编队的网络连通性和通信性能。通过联合考虑未知多用户移动性、环境对信道特性的影响以及接收信号的不可用到达角信息来解决中继无人机运动控制问题。该方法主要由两部分构成:①利用图论中的最小生成树构建网络连接性并定义通信性能指标,该网络连接性同时考虑了地面节点与无人机的通信链路及地面节点与地面节点的通信链路;②针对移动节点的通信中继,提出一种改进粒子群优化(particle swarm optimization,PSO)和非线性模型预测控制(nonlinear model predictive control,NMPC)相结合的中继无人机运动控制策略,其中移动节点的未来位置由卡尔曼滤波器进行预测。在单一环境和复杂环境下的仿真结果表明,所提出的运动控制方法可以驱使无人机到达或跟踪最优中继位置的运动并提高网络性能,同时论证了考虑环境对信道的影响是有益的。

直流GIS/GIL中线形金属微粒群飞萤运动现象与危险程度评估

直流GIS/GIL中金属微粒群的存在严重威胁设备的绝缘性能,其中线形微粒会发生“飞萤运动”。为厘清线形微粒群飞萤运动机制,该文搭建飞萤运动观测平台,获得线形微粒群的运动特性。进一步,建立微粒群荷电运动模型,实现线形微粒群飞萤运动的动态模拟。研究表明,微粒群飞萤起始电压与微粒数量呈负相关,且随微粒直径的增加而增大,随微粒长度的增大而减小,叠放状态下微粒群的启举电压减小,但飞萤起始电压不受微粒初始状态的影响;相较于单微粒,微粒间库仑力、空间电荷与场强的变化使得线形微粒群存在一系列不规则悬浮与随机跨越运动。微粒群飞萤显著降低了设备耐电强度,基于微粒的气隙击穿效应对线形微粒群运动行为进行危险程度评估。具体结果表明,对于飞萤的线形微粒群,地电极单微粒飞萤的危险程度最低,高压电极单微粒飞萤与静止微粒飞萤时危险程度较高,微粒绕飞时危险程度最高。该研究在抑制微粒群飞萤运动方面为设备的主绝缘设计提供一定参考依据。

多项式插值改进的乒乓球拾球机械臂运动控制

乒乓球拾球机器人在工作过程中易受到场地、机械结构等因素的制约,不能精准拾取乒乓球。为此,提出多项式插值改进的乒乓球拾球机械臂运动控制。通过乒乓球捡拾机器人工作示意图及机械臂优化原理,优化设计拾球机械臂的结构,利用3-5-3多项插值法构建拾球机械臂轨迹的轨迹函数,并在此基础上利用混沌粒子群算法实现拾球机械臂的轨迹规划。实验结果表明,所提方法能够精确地对拾球机械臂的轨迹实施规划,并且其受随机扰动的影响较小,控制误差平均值为0.01,具有良好的控制效果。