Cooperative navigation and localization for multiple UUVs

The authors proposed a moving long baseline algorithm based on the extended Kalman filter （EKF） for cooperative navigation and localization of multi-unmanned underwater vehicles （UUVs）. Research on cooperative navigation and localization for multi-UUVs is important to solve navigation problems that restrict long and deep excursions. The authors investigated improvements in navigation accuracy. In the moving long base line （MLBL） structure, the master UUV is equipped with a high precision navigation system as a node of the moving long baseline, and the slave UUV is equipped with a low precision navigation system. They are both equipped with acoustic devices to measure relative location. Using traditional triangulation methods to calculate the position of the slave UUV may cause a faulty solution. An EKF was designed to solve this, combining the proprioceptive and exteroceptive sensors. Research results proved that the navigational accuracy is improved significantly with the MLBL method based on EKF.

Cooperative Navigation for Autonomous Underwater Vehicles Based on Estimation of Motion Radius Vectors

A cooperative navigation algorithm for a group of autonomous underwater vehicles is proposed on the basis of motion radius vector estimation.Combined the dead reckoning data with the mutual range data through an acoustic communication network among the group members, the relative positioning problem can be solved. A novel approach for solving the relative positioning is presented by using a recursive trigonometry technique and extended Kalman filter(EKF). Simulation results verify the correctness and effectiveness of this navigation method.

The Safe Navigation of Partial Motion Planning Based on “Cooperation” with Roadside Fixed Sensors in VANET

In recent years, many methods of safe vehicle navigation and partial motion planning (PMP) have been proposed in vehicular ad-hoc network (VANET) field. In order to improve the limitation of traditional PMP, this paper presents a novel effective way to plan motion with cooperation of roadside fixed sensors (RFSs). With their cooperation, the vehicles can get the surrounding information quickly and effectively, and give highly accurate projections about the near future conditions on road. After proposing our algorithm, the worst case is analyzed and methods are found to solve the problem. Finally we conduct one elemental contrast experiment, driver situation awareness, with or without the “cooperation” of RFSs in highway scenarios. The result shows that the vehicles can make a better PMP based on the forward conditions received from RFSs, and extend the warning distance obviously when emergency happens.

Laser tracking leader-follower automatic cooperative navigation system for UAVs

Currently,small payload and short endurance are the main problems of a single UAV in agricultural applications,especially in large-scale farmland.It is one of the important methods to solve the above problems of UAVs by improving operation efficiency through multi-UAV cooperative navigation.This study proposed a laser tracking leader-follower automatic cooperative navigation system for multi-UAVs.The leader in the cluster fires a laser beam to irradiate the follower,and the follower performs a visual tracking flight according to the light spot at the relative position of the laser tracking device.Based on the existing kernel correlation filter(KCF)tracking algorithm,an improved KCF real-time spot tracking method was proposed.Compared with the traditional KCF tracking algorithm,the recognition and tracking rate of the optimized algorithm was increased from 70%to 95%in indoor environment,and was increased from 20%to 90%in outdoor environment.The navigation control method was studied from two aspects:the distance coordinate transformation model based on micro-gyroscope and navigation control strategy.The error of spot position was reduced from the maximum(3.12,−3.66)cm to(0.14,0.12)cm by correcting the deviation distance of the spot at different angles through a coordinate correction algorithm.An image coordinate conversion model was established for a complementary metal-oxide-semiconductor(CMOS)camera and laser receiving device at different mounting distances.The laser receiving device was divided into four regions,S0-S3,and the speed of the four regions is calculated using an uncontrollable discrete Kalman filter.The outdoor flight experiments of two UAVs were carried out outdoors using this system.The experiment results show that the average flight error of the two UAVs on the X-axis is 5.2 cm,and the coefficient of variation is 0.0181.The average flight error on the Z-axis is 7.3 cm,and the coefficient of variation is 0.0414.This study demonstrated the possibility and adaptability of the developed system to achieve multi-UAVs cooperative navigation.

Consensus of multi-vehicle cooperative attack with stochastic multi-hop time-varying delay and actuator fault

A consensus-distributed fault-tolerant(CDFT)control law is proposed for a class of leader-following multi-vehicle cooperative attack(MVCA)systems in this paper.In particular,the switching communication topologies,stochastic multi-hop timevarying delays,and actuator faults are considered,which may lead to system performance degradation or on certain occasions even cause system instability.Firstly,the estimator of actuator faults for the following vehicle is designed to identify the actuator faults under a fixed topology.Then the CDFT control protocol and trajectory following error are derived by the relevant content of Lyapunov stability theory,the graph theory,and the matrix theory.The CDFT control protocol is proposed in the same manner,where a more realistic scenario is considered,in which the maximum trajectory following error and information on the switching topologies during the cooperative attack are available.Finally,numerical simulation are carried out to indicate that the proposed distributed fault-tolerant(DFT)control law is effective.

Cooperative Search and Task Allocation Among Heterogeneous Teams of UAVs

A cooperative control method of multi-class UAV(unmanned air vehicle) team is investigated.During the mission,the UAVs perform search,classification,attack and battle damage assessment(BDA) tasks at various locations,which involves a combination of the team intelligence type of decision making combined with control,estimate and real-time trajectory optimization.The search-theoretic approach based on rate of return(ROR) maps is developed to get the cooperative search strategy.Templates are developed and views are combined to maximize the probability of correct target identification over various aspect angles.Monte Carle simulation runs for the scenario to evaluate the performance of the approach with various decision parameters,UAVs distributions and UAV team characteristics.Simulation results show that the cooperative behavior can significantly improve the operational effectiveness of UAV team,and the cooperative control allows for near optimal solution of the correlative behavior of a group of UAVs in battlefield.

A Multi-Vehicle Cooperative Localization Method Based on Belief Propagation in Satellite Denied Environment

The global navigation satellite system(GNSS)is currently being used extensively in the navigation system of vehicles.However,the GNSS signal will be faded or blocked in complex road environments,which will lead to a decrease in positioning accuracy.Owing to the higher-precision synchronization provided in the sixth generation(6G)network,the errors of ranging-based positioning technologies can be effectively reduced.At the same time,the use of terahertz in 6G allows excellent resolution of range and angle,which offers unique opportunities for multi-vehicle cooperative localization in a GNSS denied environment.This paper introduces a multi-vehicle cooperative localization method.In the proposed method,the location estimations of vehicles are derived by utilizing inertial measurement and then corrected by exchanging the beliefs with adjacent vehicles and roadside units.The multi-vehicle cooperative localization problem is represented using a factor graph.An iterative algorithm based on belief propagation is applied to perform the inference over the factor graph.The results demonstrate that our proposed method can offer a considerable capability enhancement on localization accuracy.

无人机系统控制研究综述

无人机(UAV)作为集技术创新和应用场景多元化为一体的低成本、高效率的明星产品,正引领着当前社会中众多行业的变革。这一变革的核心在于无人机系统控制的不断研究与进步。系统控制是无人机技术中至关重要的一个环节,它涉及到飞行稳定性、导航精度、任务执行能力以及安全性等多个方面。对近些年来的相关文献进行了综述和分析,首先,阐述了无人机系统控制的发展需求,并对无人机系统控制发展及控制架构两部分进行了详细的介绍;接着,围绕无人机系统控制的关键技术问题,即导航定位、跟踪控制、自适应控制、集群协同控制,展开全面概述和深入剖析;最后,对无人机系统控制的发展趋势进行了展望。

基于标签伯努利滤波技术的多机器人随机组网协同导航

针对多机器人在间歇性观测或无绝对观测环境下的分布式协同导航问题,提出了一种基于标签伯努利滤波技术的多机器人随机组网协同导航算法(GS-EPF-LMB)。该算法通过时间更新、观测更新和显示通信3种状态更新策略,利用随机有限集对状态和观测进行建模,并生成标签化的多伯努利粒子。为了提高算法的一致性和定位精度,基于标签多伯努利粒子耦合相对观测和绝对观测,采用粒子滤波器优化带有标签的粒子状态,并利用历史信息对状态估计进行约束。此外,利用概率数据关联实现导航系统状态估计,并通过分层高斯模型和变分贝叶斯方法实现全局最优状态估计。实验结果表明,算法的定位精度达到0.11 m,相较于全局状态-协方差交(GS-CI)算法,定位状态协方差收敛性提高了48.6%,精度提高了11%。

多矢量姿态测量算法研究综述

将矢量测量转化为姿态测量,该问题在姿态估计中被称为Wahba问题。介绍了最小化Wahba问题的损失函数的不同算法,包括最优四元数估计器(ESOQ)、四元数估计器(QUEST)、快速最优姿态矩阵(FOAM)、矩阵的奇异值分解(SVD)等广泛使用的算法,以及基于快速线性四元数姿态估计器(FLAE)、黎曼流形等近年来提出的算法。给出了简单的计算原理与推导过程,并通过计算机仿真对照,归纳各类算法在计算旋转矩阵时的计算精度、鲁棒性。针对姿态测量算法应对当前应用场景的性能需求,简要介绍其在惯性测量单元(IMU)动态对准、协同集群视觉定姿、图像拼接等方面的适用可能和工作原理,并在此基础上简述了姿态测量算法当前的缺陷与未来的发展趋势。

基于构型优选的5G集群无人机协同导航方法

针对现有基于测距的集群UAV协同导航方法普遍忽略了空间构型对定位定能的影响,难以获得精确的导航定位结果,提出一种基于空间构型优选的5G集群UAV协同导航方法。构建了复杂环境下基于5G信号的UAV相对测距误差模型,基于最小几何精度因子(geometric dilution of precision,GDOP)准则建立了协同导航节点寻优策略,实现了协同导航空间构型的实时优选;设计了基于5G测距网络的协同导航滤波器,对UAV导航信息进行在线估计和实时补偿,提高集群UAV的协同定位精度。仿真结果表明:该方法从机定位精度平均提升了约42.05%,为集群UAV实现在卫星不可用条件下的自主导航提供了一种有效的新方法。

基于节点贡献度评估的集群无人机协同导航方法

针对集群无人机协同导航中传统构型优选效率低、实时性差等问题,提出了一种基于节点贡献度评估的集群无人机协同导航方法。推导了基于测距/测角信息的协同精度因子(CDOP)计算方法。并以CDOP为指标,提出了一种节点贡献度计算方法。采用模糊综合评价法,以贡献度和测距/测角误差为因素集,提出了节点贡献度综合评估方法。该方法综合考虑节点构型、量测误差等因素,实现了最优构型下的量测信息快速筛选。将筛选后的量测信息与INS位置估计融合,实现集群无人机的协同定位。仿真结果表明,相较于快速选星经验方法,所提出的算法定位精度明显提升,定位误差小于1 m的误差累积分布由15.4%提高到了49.1%。同时,在与改进粒子群优化算法精度相当的情况下,计算效率提升了2.96倍。

基于以即刻修复为目标的动态导航下无牙颌种植手术护理配合

回顾性分析基于以即刻修复为目标的动态导航下无牙颌种植手术的护理配合要点。方法收集2019年1月—2023年5月收治的在动态导航系统引导下完成无牙颌种植手术伴即刻固定修复的42例患者的临床资料,分析动态导航技术的应用情况及护理配合特点。结果42例患者经过术前设计、术中导航均实现精确的种植体植入,种植体就位率达100%,并获得良好的初期稳定性,完成即刻固定修复,术后6个月并发症发生率为1.59%。结论动态导航辅助下的无牙颌种植手术需要护理人员熟练掌握动态导航理论知识、导航器械的安装使用及软件操作等相关技能,做好充足的术前准备以及默契的术中护理配合,以确保手术顺利完成。

基于移动长基线和导航误差修正的USV/UUV协同导航算法

针对无人水下航行器长时间协同运动,惯性导航误差累积增长的问题,为减小USV/UUV之间相对导航误差,提高UUV导航精度,提出一种基于移动长基线和导航误差修正的无人水面艇(USV)/无人水下航行器(UUV)协同导航算法。该算法基于水声通信测距,首先由UUV收集其与USV间相对距离信息,计算相对导航误差修正量,接着将修正量添加到USV当前导航位置中,结合USV与UUV间的队形设置以及UUV自身低精度导航设备解算的位置,最终实现对UUV累积的惯导位置误差修正。仿真结果表明,在USV/UUV协同运动过程中,通过对修正后UUV位置信息以及UUV惯导解算得到的位置量进行融合,可显著提高UUV导航精度。

基于奇异谱分析的空间非合作目标无源机动检测方法

针对空间非合作目标无源预警任务中无先验信息条件下的目标轨道异常检测难题,提出了基于奇异谱分析的目标机动无源检测方法。首先,在追踪卫星轨道坐标系下建立了相对动力学模型及无源相对视线角测量模型。然后,通过奇异谱分析对视线角原始测量序列进行特征提取,设计了差异化评价指标及其阈值的计算方法并利用与、或门逻辑进行综合机动判断。最后,在GEO类型轨道应用场景下对所提算法进行了数值仿真验证与结果分析。仿真结果表明,当无源测角精度不低于3×10^(-4)rad时,相对距离1000 km以内目标执行的1 m/s量级以上的小脉冲机动可被有效地检测,检测成功率超过90%。

基于差分隐私的卫星互联网协同定位研究

卫星互联网基于GNSS为用户提供定位、导航和定时服务,但GNSS协同定位过程会泄露包含用户位置信息的数据从而对用户隐私安全造成威胁.针对当前GNSS协同定位隐私保护方面存在的问题,本文提出了一种基于差分隐私的卫星导航系统协同定位方法.该方法首先生成扰动位置,并在满足隐私预算的前提下随机选择扰动位置计算定位协同数据,防止用户位置信息在协作过程中泄露.实验结果表明:所提出的方法可以在保障定位准确率和实时性的前提下有效保护用户位置隐私,可为卫星互联网协同定位信息安全研究提供理论参考.

基于惯性导航与超宽带的行人协同定位研究

基于惯性导航与超宽带测距,文章研究了GNSS信号受干扰情况下的行人协同定位技术,采用了一种协同导航算法,其可以在行人导航解算的基础上,获得单人导航的位置信息,并在UWB测距得到的相对观测值的基础上,通过扩展卡尔曼滤波算法对各个行人的位置信息进行交互融合,最终得到目标的理想位置信息。行人导航实验表明,该方法能够提升系统定位精度,并进一步增强系统的稳定性与可靠性。

空地协同无人系统研究

空地协同无人机系统是当今无人集群研究领域的热点,系统在负载、速度、机动性等要素上高度优势互补,具有环境适应性强,应用场景多样,任务执行率高等特点。回顾了空地协同无人系统近年的研究发展现状,基于对系统内无人机与无人车功能互补性的分析,总结了4种常用的功能角色分工模式,详细梳理了空地协同无人系统在协同控制、任务规划、导航定位、协同着陆等关键技术领域的研究进展,分析了其研究发展方向和关键技术问题。

基于分布式网络的水面舰艇编队一体化导航方法

[目的]针对体系化作战中,水面舰艇编队导航面临设备损毁、导航干扰等复杂作战环境的挑战,提出一种基于分布式网络的水面舰艇编队一体化导航系统及方法。[方法]通过分布式协同导航提升编队应对复杂环境的可靠性和灵活性。编队一体化导航系统分为设备层、系统层和体系层,设备层输出原始的GNSS/INS导航数据,系统层输出节点独立导航数据,体系层融合生成编队协同导航数据。为实现体系层编队协同导航数据融合,采用Floyd算法,以基线传递路径最短的原则构建分布式编队协同导航网络,在节点独立导航数据的基础上通过动态基线传递生成节点协同导航数据;通过加权最小二乘法融合多个节点协同导航数据输出编队协同导航数据。[结果]仿真结果表明,应用该方法可成功构建分布式协同导航网络。通过导航支援,保障了受毁伤节点导航的不中断;通过精度支援,提升了受干扰节点的导航精度。[结论]分布式协同导航具有抗毁伤、抗干扰、可提高精度的优势,并可有效应对复杂的作战环境。

基于分布式信息滤波的集群导弹协同定位方法

针对领从式集群导弹中的从弹定位问题,考虑领弹动态地切入切出导航网络情况,提出了一种基于捷联惯性导航系统(INS)与到达时间(TOA)测距系统相融合的领从类分布式协同导航方法。该方法综合考虑了导航参数与相关传感器误差模型,基于INS误差系统建立线性状态空间模型,基于弹间数据链TOA测距系统建立非线性伪距量测模型,并基于扩展信息滤波器实现动态信息的分布式融合,以适应量测信息节点数目动态变化引起的协同导航网络结构变化。将其应用于集群导弹协同导航系统中,仿真结果表明所提算法可以快速且高精度地完成从弹导航参数的估计,可以有效消除领弹节点变化所带来的负担。该方法可以作为低成本从弹导航系统的补充手段。