A Lyapunov-based three-axis attitude intelligent control approach for unmanned aerial vehicle

A novel Lyapunov-based three-axis attitude intelligent control approach via allocation scheme is considered in the proposed research to deal with kinematics and dynamics regarding the unmanned aerial vehicle systems.There is a consensus among experts of this field that the new outcomes in the present complicated systems modeling and control are highly appreciated with respect to state-of-the-art.The control scheme presented here is organized in line with a new integration of the linear-nonlinear control approaches,as long as the angular velocities in the three axes of the system are accurately dealt with in the inner closed loop control.And the corresponding rotation angles are dealt with in the outer closed loop control.It should be noted that the linear control in the present outer loop is first designed through proportional based linear quadratic regulator(PD based LQR) approach under optimum coefficients,while the nonlinear control in the corresponding inner loop is then realized through Lyapunov-based approach in the presence of uncertainties and disturbances.In order to complete the inner closed loop control,there is a pulse-width pulse-frequency(PWPF) modulator to be able to handle on-off thrusters.Furthermore,the number of these on-off thrusters may be increased with respect to the investigated control efforts to provide the overall accurate performance of the system,where the control allocation scheme is realized in the proposed strategy.It may be shown that the dynamics and kinematics of the unmanned aerial vehicle systems have to be investigated through the quaternion matrix and its corresponding vector to avoid presenting singularity of the results.At the end,the investigated outcomes are presented in comparison with a number of potential benchmarks to verify the approach performance.

Automatic Carrier Landing Control for Unmanned Aerial Vehicles Based on Preview Control

For carrier-based unmanned aerial vehicles(UAVs),one of the important problems is the design of an automatic carrier landing system(ACLS)that would enable the UAVs to accomplish autolanding on the aircraft carrier.However,due to the movements of the flight deck with six degree-of-freedom,the autolanding becomes sophisticated.To solve this problem,an accurate and effective ACLS is developed,which is composed of an optimal preview control based flight control system and a Kalman filter based deck motion predictor.The preview control fuses the future information of the reference glide slope to improve landing precision.The reference glide slope is normally a straight line.However,the deck motion will change the position of the ideal landing point,and tracking the ideal straight glide slope may cause landing failure.Therefore,the predictive deck motion information from the deck motion predictor is used to correct the reference glide slope,which decreases the dispersion around the desired landing point.Finally,simulations are carried out to verify the performance of the designed ACLS based on a nonlinear UAV model.

Data-driven unsupervised anomaly detection and recovery of unmanned aerial vehicle flight data based on spatiotemporal correlation

Anomaly detection is crucial to the flight safety and maintenance of unmanned aerial vehicles(UAVs)and has attracted extensive attention from scholars.Knowledge-based approaches rely on prior knowledge,while model-based approaches are challenging for constructing accurate and complex physical models of unmanned aerial systems(UASs).Although data-driven methods do not require extensive prior knowledge and accurate physical UAS models,they often lack parameter selection and are limited by the cost of labeling anomalous data.Furthermore,flight data with random noise pose a significant challenge for anomaly detection.This work proposes a spatiotemporal correlation based on long short-term memory and autoencoder(STCLSTM-AE)neural network data-driven method for unsupervised anomaly detection and recovery of UAV flight data.First,UAV flight data are preprocessed by combining the Savitzky-Golay filter data processing technique to mitigate the effect of noise in the original historical flight data on the model.Correlation-based feature subset selection is subsequently performed to reduce the reliance on expert knowledge.Then,the extracted features are used as the input of the designed LSTM-AE model to achieve the anomaly detection and recovery of UAV flight data in an unsupervised manner.Finally,the method's effectiveness is validated on real UAV flight data.

FBCRI Based Real-time Path Planning for Unmanned Aerial Vehicles in Unknown Environments with Uncertainty

This paper presents an FBCRI(feedback based compositional rule of inference)based novel path planning method to satisfy the requirements of real-time navigation,smoothness optimization and probabilistic obstacle avoidance.With local path-searching behaviors in regional ranges and global goal-seeking behaviors in holistic ranges,the method infers behavior weights using fuzzy reasoning embedded with feedback,and then coordinates the behaviors to generate new reference waypoints.In view of the deterministic decisions and the uncertain states of a UAV(unmanned air vehicle),chance constraints are adopted to probabilistically guarantee the UAV’s safety at a required level.Simulation results in representative scenes prove that the method is able to rapidly generate convergent paths in obstacle-rich environments,as well as highly improve the path quality with respect to smoothness and probabilistic safety.

A New Approach to Estimate True Position of Unmanned Aerial Vehicles in an INS/GPS Integration System in GPS Spoofing Attack Conditions

This paper presents a new approach to estimate the true position of an unmanned aerial vehicle （UAV） in the conditions of spoofing attacks on global positioning system （GPS） receivers. This approach consists of two phases, the spoofing detection phase which is accomplished by hypothesis test and the trajectory estimation phase which is carried out by applying the adapted particle filters to the integrated inertial navigation system （INS） and GPS. Due to nonlinearity and unfavorable impacts of spoofing signals on GPS receivers, deviation in position calculation is modeled as a cumulative uniform error. This paper also presents a procedure of applying adapted particle swarm optimization filter （PSOF） to the INS/GPS integration system as an estimator to compensate spoofing attacks. Due to memory based nature of PSOF and benefits of each particle＇s experiences, application of PSOF algorithm in the INS/GPS integ- ration system leads to more precise positioning compared with general particle filter （PF） and adaptive unscented particle filer （AUPF） in the GPS spoofing attack scenarios. Simulation results show that the adapted PSOF algorithm is more reliable and accurate in estim- ating the true position of UAV in the condition of spoofing attacks. The validation of the proposed method is done by root mean square error （RMSE） test.

Distributed game strategy for unmanned aerial vehicle formation with external disturbances and obstacles

We investigate a distributed game strategy for unmanned aerial vehicle(UAV)formations with external disturbances and obstacles.The strategy is based on a distributed model predictive control(MPC)framework and Levy flight based pigeon inspired optimization(LFPIO).First,we propose a non-singular fast terminal sliding mode observer(NFTSMO)to estimate the influence of a disturbance,and prove that the observer converges in fixed time using a Lyapunov function.Second,we design an obstacle avoidance strategy based on topology reconstruction,by which the UAV can save energy and safely pass obstacles.Third,we establish a distributed MPC framework where each UAV exchanges messages only with its neighbors.Further,the cost function of each UAV is designed,by which the UAV formation problem is transformed into a game problem.Finally,we develop LFPIO and use it to solve the Nash equilibrium.Numerical simulations are conducted,and the efficiency of LFPIO based distributed MPC is verified through comparative simulations.

基于DPBBO算法的智慧云仓UAV盘库作业优化

针对智慧云仓货物信息量大、易出现账物不符等库存管理问题,迫切需要将无人机(unmanned aerial vehicle, UAV)和工业物联网(industrial Internet of things, IIoT)集成起来,为仓储精细化管理提供解决方案。首先,分析盘库作业数据采集与信息交互运行机制,以危险避障和数据采集为约束函数,考虑了UAV在加速、减速、匀速、转角等飞行条件下的能耗差异,并以能耗最低和时间最短为目标函数构造UAV盘库作业数学模型;然后,设计了差分迁移-分段变异生物地理学优化(differential migration-piecewise mutation-biogeography-based optimization, DPBBO)算法对上述模型进行优化解算;最后,进行了仿真实验验证。结果表明:DPBBO算法对解决该盘库作业问题的效果较优,可以提升库存抽检任务的时效性和库存管理的准确性。

航母舰载无人机全自动着舰技术特点分析

全自动着舰技术是无人机上舰必须解决的“使能”技术。2013年,美军已完成X-47B无人机验证机在航母上的全自动着舰试验验证,为其发展MQ-25A“黄貂鱼”航母舰载无人机装备奠定了良好的技术基础。在梳理飞机着陆与着舰差异的基础上,给出航母舰载无人机全自动着舰的通用流程;从地位作用、工作范围、系统构架、系统要求、实现手段以及关键技术支撑等角度,对比有人机重点分析了航母舰载无人机全自动着舰的特点,为后续关键技术研究和武器装备建设发展提供参考。

改进CBBA算法的风电场无人机群巡检任务分配

提出一种基于改进的共识捆绑算法(CBBA)的多无人机风电场巡检任务分配方法。首先,根据风电机组基础信息,采用K-均值算法确定无人机数量和安放位置,并构建风电机组巡检价值评估函数,更为合理地评估各风电机组的巡检收益;然后,针对无人机的归巢返程问题,提出一种基于CBBA算法的任务包双向构建策略;最后,为克服传统CBBA算法中存在的因个体“贪婪”导致的整体效益下降问题,设计一种基于类别的奖励制度,以提高任务分配的合理性。试验表明,所提方法可根据风速、风向的变化制定相应的巡检任务分配方案,可为无人机群自主巡检风电场的任务分配提供可参考的方案。

无人机双目视觉鲁棒定位方法

无人机(unmanned aerial vehicle,UAV)在全球定位系统(global positioning system,GPS)信号拒止环境中的应用受到限制,传统视觉同步定位与建图(simultaneous localization and mapping,SLAM)技术一定程度上解决了该问题,但在动态场景和弱纹理场景中定位精度较差。针对该问题提出一种基于双目视觉的多场景鲁棒SLAM方法,重点考虑了真实环境中的动态和弱纹理2类具有挑战性的场景,利用双目相机为UAV在动态和弱纹理场景中提供位姿信息。针对动态场景利用掩膜基于区域的卷积神经网络(mask region-based convolutional neural network,Mask R-CNN)分割潜在动态内容并剔除动态特征,通过计算稠密光流同步相邻帧的掩膜,减小了掩膜的计算成本。对于弱纹理场景,在传统SLAM算法使用的点特征基础上融合了线特征,充分利用了环境中的结构特征。数值模拟和仿真实验证明了本文算法具有更高的鲁棒性和精确性。

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。

低空无人机与地面移动式激光雷达的点云配准研究

激光雷达多平台数据的匹配融合是目前点云应用的研究热点。本文以无人机与地面移动式激光雷达的配准为例,通过采集建筑用地、稀疏林地、茂密林地、规则林地的点云数据,研究手动配准、ICP配准对不同场景的适用性。实验表明,配准精度上,建筑用地>稀疏林地>茂密林地>规则林地,可见数据几何特征越强,配准效果越好。配准方法上,手动配准后叠加ICP的方法优于单一方法,可更好实现林区的点云匹配。

一种无人机自组网中基于身份加密的认证方案

随着无线通信技术的日益发展,无人机自组网在救援、巡逻和侦查等领域都得到了广泛运用。然而,由于无人机自组网网络结构灵活多变且缺乏基础设施的支持,该网络面临严重的身份安全威胁。尽管当前存在各种无第三方安全方案,但这些方案存在性能低下和安全性不足等问题,难以在无人机自组网上部署。针对这些问题,该文提出一种基于IBE的认证方案,将实体身份信息作为公钥,实体无需存储额外的公钥信息,降低了密钥存储开销。同时,该方案支持批量认证,极大地提升了认证效率。此外,通过引入哈希链技术,有效地保留和复用无人机首次认证后的状态信息,降低了后续无人机的认证成本。通过形式化的安全分析,证明该方法能抵御身份伪造、中间人等恶意攻击。相较于已有方案,该方案的计算和存储开销更低。

无人机自组网中基于优先级和发送概率的低时延MAC协议设计

媒体接入控制(MAC)协议是满足数据传输质量服务需求的一项关键技术,其性能的优劣直接决定了网络的整体运行效率。相较于其他经典MAC协议,基于统计优先级的多址接入(SPMA)协议采用了多优先级接入、信道检测和流量控制等机制,因此它能更好地满足无人机自组网的低时延、区分服务、大规模组网等重要需求。其中,不同优先级阈值是影响该协议性能的关键指标之一,而现有研究中的阈值设置策略会导致无人机自组网吞吐量波动较大的问题。除此之外,在大规模节点组网的情况下,由于特殊的发送机制,该协议面临数据包冲突加剧的问题。针对以上问题,本文提出了一种新的阈值设置方法和低优先级数据概率性接入信道的方法。在大规模节点网络高业务量的情况下,该改进策略根据新的阈值和周期性负载统计的比较结果决定是否延迟或者拒绝相关数据的接入。与此同时,将SPMA协议的多优先级接入机制和排队论基本原理相结合,通过计算设定发送概率对不同低优先级数据进行接入控制。仿真结果表明,所提方法能够使得无人机自组网的吞吐量保持稳定,并为不同类型业务的差异性服务质量(QoS)需求提供良好支持,提高不同优先级数据的首发传输成功率的同时不会带来较大的额外时延损耗,性能优于现有的SPMA协议。

多策略鼠群优化算法的无人机三维航迹规划

针对在复杂电力检测环境中无人机三维航迹规划问题,提出一种基于跳跃式自适应小孔成像反向学习鼠群优化(JAPRSO)算法的无人机三维航迹规划方法。JAPRSO算法引入了Sobol序列初始化种群以增强种群多样性;引入了非线性自适应因子实现动态权衡局部开发和全局搜索能力;嵌入了跳跃式围捕猎物机制以避免算法陷入局部最优;同时,引入跳跃式自适应小孔成像反向学习追赶猎物机制以提高算法的全局寻优能力。仿真结果表明:所提出的路径规划方法寻优性能优于RSO算法、灰狼优化(GWO)算法,金枪鱼群优化(TSO)算法和海鸥优化(SOA)算法,能够有效地躲避威胁区,快速获得航迹代价最小的安全可行航迹,可适用于求解电力检测方面的无人机三维航迹规划问题。

基站对低空无人机通感算一体化应用组网研究

基站对低空无人机的通信感知算力一体化是6G通信系统的重要技术之一,其应用场景、基站组网、以及网络架构对工程落地至关重要。首先详细分析了通信感知一体化技术及在无人机上的应用及关键性能指标。其次,提出了针对无人机应用的四种多基站协同组网模式和相应的解决方案建议。最后,对核心网络架构功能、多站多模态多终端感知、以及移动边缘计算等关键技术进行了分析,为未来低空经济的规模发展和6G演进提供有益参考。

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。

物联网应用中基于位置优先级的UAV分配算法

针对无人机协助的物联网应用(UAV-IoT),提出基于位置优先级的UAV分配算法。在UAV-IoT应用中,UAV具有感知位置服务数据的能力,并将所感测的IoT数据传输至基站。构建最大化服务效率的优化问题。考虑优化问题的NP-难,采用启发式算法求解。计算用户所请求服务的位置优先级,再依据位置优先级排序,使UAV优先为位置优先级高的请求提供服务,进而提升服务效率和降低服务时延。仿真结果表明,相比于随机分配UAVs执行位置服务算法和时延感知的UAV分配算法,LPUA算法的平均服务时延分别下降了约23.4%和10.2%。

无线电地图辅助的多无人机部署与用户关联离线规划方法

构建基于无人机基站(Unmanned Aerial Vehicle Base Station, UBS)的空地网络是解决移动通信网络覆盖等问题的重要途径。区别于地面移动通信网络,空地网络需要对UBS位置和用户关联进行联合优化。针对上述问题,首先通过构建二进制无线电地图(Binary Radio Map, BRM)使得UBS能够有效获取整个任务区域中用户位置关联的信道知识,在此基础上提出基于BRM的离线多UBS部署与用户关联联合规划方法。该方法以最大化网络效用函数为目标,通过互嵌套的启发式UBS部署位置搜索和基于匹配博弈的UBS-用户匹配实现UBS位置和用户关联的离线优化。在复杂城市环境下,相比于参考方案,所提方法可使得用户和速率性能提升10%~40%。

面向无人自主空战的编队飞行控制方法综述

无人机集群在民用和军用领域的应用近年来受到广泛关注,其中一个关键问题就是无人机编队的飞行控制,为此本文对该领域内的相关文献进行了梳理。首先,介绍了无人机编队计算和通信结构的组织方式,即集中式、分布式和混合式;其次,对常用的一些无人机编队飞行控制方法进行了详细介绍,包括领从法、虚拟结构法、基于行为的方法和人工势场法等;再次,从编队生成、队形保持、避障、可实现性、鲁棒性、对计算资源和通信链路的需求等方面对比了这些方法的优缺点,以期为读者在编队控制领域的研究提供便利;最后,讨论了一些有待解决的难题和可能的研究方向。