Modeling and Trajectory Tracking Control for Flapping-Wing Micro Aerial Vehicles

This paper studies the trajectory tracking problem of flapping-wing micro aerial vehicles(FWMAVs)in the longitudinal plane.First of all,the kinematics and dynamics of the FWMAV are established,wherein the aerodynamic force and torque generated by flapping wings and the tail wing are explicitly formulated with respect to the flapping frequency of the wings and the degree of tail wing inclination.To achieve autonomous tracking,an adaptive control scheme is proposed under the hierarchical framework.Specifically,a bounded position controller with hyperbolic tangent functions is designed to produce the desired aerodynamic force,and a pitch command is extracted from the designed position controller.Next,an adaptive attitude controller is designed to track the extracted pitch command,where a radial basis function neural network is introduced to approximate the unknown aerodynamic perturbation torque.Finally,the flapping frequency of the wings and the degree of tail wing inclination are calculated from the designed position and attitude controllers,respectively.In terms of Lyapunov's direct method,it is shown that the tracking errors are bounded and ultimately converge to a small neighborhood around the origin.Simulations are carried out to verify the effectiveness of the proposed control scheme.

Multi-Object Tracking with Micro Aerial Vehicle

A simple yet efficient tracking framework is proposed for real-time multi-object tracking with micro aerial vehicles(MAVs). It's basic missions for MAVs to detect specific targets and then track them automatically. In our method, candidate regions are generated using the salient detection in each frame and then classified by an eural network. A kernelized correlation filter(KCF) is employed to track each target until it disappears or the peak-sidelobe ratio is lower than a threshold. Besides, we define the birth and death of each tracker for the targets. The tracker is recycled if its target disappears and can be assigned to a new target. The algorithm is evaluated on the PAFISS and UAV123 datasets. The results show a good performance on both the tracking accuracy and speed.

Implementation of VxWorks in autopilot for micro aerial vehicle based on PXA255 and FPGA

The overall hardware construction of autopilot within micro aerial vehicle is presented. The boot process of VxWorks real time operating system as well as the conception and function of board support package （BSP） is described. And the transplantation process of the VxWroks operat ing system into the hardware platform mentioned above is highlighted. It is shown from the final re sults that VxWorks has high stability and real time performance, ensuring accurate flight control and a smooth flight of the micro aerial vehicle.

Indoor versus outdoor scene recognition for navigation of a micro aerial vehicle using spatial color gist wavelet descriptors

In the context of improved navigation for micro aerial vehicles,a new scene recognition visual descriptor,called spatial color gist wavelet descriptor(SCGWD),is proposed.SCGWD was developed by combining proposed Ohta color-GIST wavelet descriptors with census transform histogram(CENTRIST)spatial pyramid representation descriptors for categorizing indoor versus outdoor scenes.A binary and multiclass support vector machine(SVM)classifier with linear and non-linear kernels was used to classify indoor versus outdoor scenes and indoor scenes,respectively.In this paper,we have also discussed the feature extraction methodology of several,state-of-the-art visual descriptors,and four proposed visual descriptors(Ohta color-GIST descriptors,Ohta color-GIST wavelet descriptors,enhanced Ohta color histogram descriptors,and SCGWDs),in terms of experimental perspectives.The proposed enhanced Ohta color histogram descriptors,Ohta color-GIST descriptors,Ohta color-GIST wavelet descriptors,SCGWD,and state-of-the-art visual descriptors were evaluated,using the Indian Institute of Technology Madras Scene Classification Image Database two,an Indoor-Outdoor Dataset,and the Massachusetts Institute of Technology indoor scene classification dataset[(MIT)-67].Experimental results showed that the indoor versus outdoor scene recognition algorithm,employing SVM with SCGWDs,produced the highest classification rates(CRs)—95.48%and 99.82%using radial basis function kernel(RBF)kernel and 95.29%and 99.45%using linear kernel for the IITM SCID2 and Indoor-Outdoor datasets,respectively.The lowest CRs—2.08%and 4.92%,respectively—were obtained when RBF and linear kernels were used with the MIT-67 dataset.In addition,higher CRs,precision,recall,and area under the receiver operating characteristic curve values were obtained for the proposed SCGWDs,in comparison with state-of-the-art visual descriptors.

Research Progress on Bio‑inspired Flapping‑Wing Rotor Micro Aerial Vehicle Development

Flapping-wing rotor(FWR)is an innovative bio-inspired micro aerial vehicle capable of vertical take-off and landing.This unique design combines active flapping motion and passive wing rotation around a vertical central shaft to enhance aerodynamic performance.The research on FWR,though relatively new,has contributed to 6%of core journal publications in the micro aerial vehicle field over the past two decades.This paper presents the first comprehensive review of FWR,analysing the current state of the art,key advances,challenges,and future research directions.The review highlights FWR’s distinctive kinematics and aerodynamic superiority compared to traditional flapping wings,fixed wings,and rotary wings,discussing recent breakthroughs in efficient,passive wing pitching and asymmetric stroke amplitude for lift enhancement.Recent experiments and remote-controlled take-off and hovering tests of single and dual-motor FWR models have showcased their effectiveness.The review compares FWR flight performance with well-developed insect-like flapping-wing micro aerial vehicles as the technology readiness level progresses from laboratory to outdoor flight testing,advancing from the initial flight of a 2.6 g prototype to the current free flight of a 60-gram model.The review also presents ongoing research in bionic flexible wing structures,flight stability and control,and transitioning between hovering and cruise flight modes for an FWR,setting the stage for potential applications.

Development of the NWPU20 Micro Air Vehicle

This paper reports the design, construction, and operation of NWPU20 Micro Air Vehicle (MAV), which is the smallest that has, up to now, been developed in PR China. The miniaturization level in PR China makes smaller MAV, in our opinion, not possible to implement. The NWPU20 is a 20-centimeter span, battery powered, fixed-wing aircraft with an off-the-shelf color video camera that can transmit live video back to the ground station. The on-board electronic subsystems are smallest and lightest among the commercial products, including an 8-gram wireless video camera, an 8-gram receiver, a 5-gram electronic speed controller (ESC), two 4.5-gram fully proportional radio frequency control servos, and the total mass of NWPU20 is less than 80 grams. An experimental model was fabricated and tested in the Low Turbulent Flow Wind Tunnel (LTFWT) at Northwestern Polytechnical University (NWPU) to research low Reynolds number flow characteristics of the NWPU20. The result of the wind tunnel test shows that stall angle of attack of NWPU20 can reach 30°, which is higher than that of the general aircrafts, and the maximum lift-to-drag ratio of NWPU20 can nearly reach 6 at the angle of attack of 10°, which can satisfy design requirements of the NWPU20. A small-sized propulsion/torque testing system was developed to measure and analyze the propulsion and torque performances of the motor-propeller combination used in the NWPU20. A center of gravity (c.g.) testing apparatus was developed and used to adjust the c.g. of the NWPU20 so that it has good longitudinal static stability and control. The NWPU20 prototype has undergone successfully flight tests many times; it flies at 32 kilometers per hour, with an endurance of 15 minutes, and a maximum communications range of 300 meters. With the color video camera, NWPU20 successfully transmits real-time video back to the ground station. The success of NWPU20 proves preliminarily that 20-centimeter span micro air vehicle is feasible and usable.

Low-Power Operational Amplifier for Real-Time Signal Processing System of Micro Air Vehicle

A low-power complementary metal oxide semiconductor（CMOS） operational amplifier （op-amp） for real-time signal processing of micro air vehicle （MAV） is designed in this paper.Traditional folded cascode architecture with positive channel metal oxide semiconductor（PMOS） differential input transistors and sub-threshold technology are applied under the low supply voltage.Simulation results show that this amplifier has significantly low power,while maintaining almost the same gain,bandwidth and other key performances.The power required is only 0.12 mW,which is applicable to low-power and low-voltage real-time signal acquisition and processing system.

A leveling mechanism for the platform based on booms-constraint control of aerial vehicle

In order to achieve an automatic leveling function for work platforms of aerial vehicles with mixed-booms( MAV) in full elevating domain,an auto-leveling mechanism for the platform is proposed based on a control method of booms-constraint,where mixed-boom structures and elevating characteristics are considered. Three models of constraint strategies include non-constraint model,elevating constraint model and lowering constraint model,which is designed to meet the leveling requirements in full working extent. Through the hydro-mechatronic unified modeling,a virtual prototype model is set up based on the auto-leveling mechanism,and leveling performances of the platform are studied during booms elevating to the maximum working height and extent. Simulation results show that the control method of booms-constraint can realize auto-leveling of the platform under two typical working conditions,meanwhile a leveling deviation appears at the constrained point,but the platform inclination is adjusted in the permissible range. The control method does not only restrict booms' freedom elevating to a certain extent,but also impacts the booms extending to the maximum working range. Experimental results verify that the auto-leveling mechanism based on booms-constraint control is valid and rational,which provides an effective technology approach for development of the platform leveling of MAV.

微多普勒辅助的城市环境无人机编队检测方法

针对城市复杂环境下电磁环境复杂、多径杂波和干扰信号密集等现象,传统的无人机(UAV)检测方法通过获取回波信号提取目标多普勒信息进行检测,易受到环境影响导致检测效果不理想。该文提出微多普勒辅助的城市环境无人机编队检测方法,充分利用无人机的微动特征,能够在复杂环境下提高检测精度。首先,参数化建模表征城市复杂环境下无人机旋翼的雷达回波微多普勒信号,利用YOLOv5s检测微多普勒闪烁脉冲,有效提取位置信息;然后,引入雷达信号分选方法的脉冲重复间隔(PRI)变换,分类获得无人机编队数量;最后,利用Kmeans算法验证无人机编队检测方法的准确性。结果表明,所提方法在信噪比2 dB时7架无人机的检测精度高于90%,能够用于城市复杂环境存在干扰脉冲、多径效应、局部脉冲丢失的无人机编队检测。

基于改进LSTM网络的无人机MEMS-IMU零偏在线标定方法

针对在卫星信号拒止、视觉系统退化场景中无人机MEMS-IMU零偏无法准确估计并补偿导致导航误差迅速发散的问题,提出一种基于改进长短时记忆(LSTM)网络的零偏在线标定方法。首先,为解决MEMS-IMU零偏数据非线性强、传统循环时间网络训练效果差的问题,设计序列到序列的LSTM神经网络结构,引入教师强迫机制,提高了网络特征学习能力。然后,在导航过程中使用训练后的网络对MEMS-IMU零偏在线标定,补偿后的IMU量测与视觉信息联合优化,保证了导航定位精度。实验结果表明,在纯惯性导航实验中,所提方法的绝对位置误差比传统LSTM方法减小了6.5%;在EUROC数据集下进行的视觉惯性组合导航实验中,所提方法的平均绝对位置误差比传统LSTM方法减小了15%。

低脉冲重复频率条件下无人机微动参数提取

针对低脉冲重复频率条件下的无人机微动特征提取问题,提出一种基于原子放缩正交匹配追踪(orthogonal matching pursuit,OMP)分解的微动参数估计方法。首先,通过计算时频谱熵从无人机回波数据中筛选出具有显著微动特征的信号。其次,采用变分模态分解(variational modal decomposition,VMD)算法提取旋翼叶片的旋转频率,提出了基于原子放缩的OMP分解方法实现了对无人机旋翼叶片长度的估计。仿真实验表明所提方法相比于传统的OMP方法和VMD-OMP方法都具有明显优势。最后,采用实测数据验证了所提算法的有效性。

MAV放宽静稳定性设计的效益/代价分析

介绍了一种固定翼微型飞行器,通过对不同静稳定性设计的升阻特性、力矩特性、配平损失等进行计算分析,确定了在该类微型飞行器上应用放宽静稳定性技术的代价和效益,分析了不同操纵面布局的影响,并给出了微型飞行器在操纵面布局和控制增稳应用方面的一般计算方法和建议。

极小昆虫翅膀拍动模式的气动特性实验研究

不同于较大的昆虫(如熊蜂、果蝇和鹰蛾等,飞行雷诺数Re>100),极小昆虫(如丽蚜小蜂、西花蓟马和瘿蚊等,Re<100)的翅膀采用更复杂的三自由度拍动形式,对应特殊的非定常高升力机制——“划桨”机制;同时,微小型飞行器(MAV)的飞行雷诺数较高(Re=10~3~10~5),其任务环境复杂多变,当MAV面对超载荷或高机动任务时,利用极小昆虫翅膀拍动模式飞行或许可以作为一种新的解决方案.为了深入理解MAV雷诺数下极小昆虫翅膀拍动模式的气动特性,基于三自由度拍动翅膀模型实验(包括直接测力和染色液流动显示,Re=3.9×10^(3)~1.0×10^(4))对3种典型极小昆虫(丽蚜小蜂、西花蓟马和瘿蚊)悬停飞行状态下翅膀拍动模式的气动力特性进行研究.发现在上挥(upstroke)阶段初期,即“划桨(rowing)”阶段,翅膀受到的铅垂升力急剧增大;在所考虑的雷诺数范围内,翅膀在整个上挥阶段的平均铅垂升力系数(大于3.1,丽蚜小蜂)明显大于较大昆虫翅膀拍动模式下获得的铅垂升力系数(1.5~2.0).同时,观察到在“划桨”阶段初期,翅膀的前缘和后缘产生一对旋转方向相反的旋涡;进入“划桨”阶段中末期,后缘旋涡脱落,但是前缘旋涡始终附着.这表明在MAV雷诺数下,翅膀上挥过程中的高升力产生机制为“延迟失速机制”,而不是极低雷诺数下的“划桨机制”.此外,发现在翅膀上挥下拍冲程交替即“打开(fling)”阶段,其高升力机制仍然是“打开机制”;然而,也注意到雷诺数效应和双翅效应对“打开”阶段的气动力影响很大.

微小型无人机载FMCW SAR宽波束运动补偿算法

调频连续波(frequency modulated continuous wave,FMCW)体制的合成孔径雷达(synthetic aperture radar,SAR)具有较宽的方位向波束,运动误差在成像处理时会产生较为严重的方位空变性。对此,建立了具有三轴运动误差的信号模型;对宽波束情况下的距离和方位的耦合性以及由运动误差导致的方位空变性进行了分析;在视线向(line of sight,LOS)引入传统的频率分割算法来补偿方位空变误差,在航向将运动误差建模为包络误差与相位误差;提出了“逐方位块补偿法”来消除相位误差中的空变性。最后,通过仿真实验验证了所提算法的有效性。

具有不确定性的扑翼微型飞行器抗干扰控制

研究了具有内部不确定性和外部干扰的扑翼微型飞行器的姿态和位置跟踪控制问题。利用神经网络对扑翼微型飞行器数学模型中复杂非线性和不确定性进行逼近估计,同时对于外部扰动,采用自适应技术来处理干扰对系统的影响。基于反步递推框架,引入一阶滤波器,设计了动态表面控制器,克服了传统反步递推设计中“微分爆炸”的局限性。进一步,利用Lyapunov稳定理论证明了扑翼飞行器姿态和位置闭环系统的稳定性和所有状态变量的半全局一致最终有界性。结果表明,所提控制器不仅能够使稳态误差更好地收敛,而且还提高了收敛速度。仿真结果验证了所提控制方法能够有效地处理不确定性和外部干扰,且能够很好地跟踪期望轨迹。

基于TMS320DM355的MAV机载视频传输系统的设计与实现

为了提高微型飞行器(MAV)无线视频传输的质量、扩大传输距离,设计并实现了一种基于3G WCDMA无线网络的机载视频传输系统。采用TI公司的达芬奇处理器TMS320DM355作为核心处理器,给出了总体设计方案和硬件结构,并阐述了采用PPP协议拨号接入WCDMA网络的方法。采用嵌入式Linux操作系统作为软件开发平台,分析并设计了基于V4L2(Video For Linux Two)驱动的视频采集模块。通过调用TI提供的编解码引擎API,实现了视频MPEG-4压缩编码处理。针对视频传输的实时性要求,采用了实时传输协议RTP和实时传输控制协议RTCP,重点阐述了MPEG-4视频的RTP封包策略,并基于JRTPLIB库文件实现了视频的实时传输功能。实验结果表明,该系统可以满足视频清晰、实时的传输要求。

MAV/UAV task coalition phased-formation method

The formation of the manned aerial vehicle/unmanned aerial vehicle(MAV/UAV) task coalition is considered. To reduce the scale of the problem, the formation progress is divided into three phases. For the task clustering phase, the geographical position of tasks is taken into consideration and a cluster method is proposed. For the UAV allocation phase, the UAV requirement for both constrained and unconstrained resources is introduced, and a multi-objective optimal algorithm is proposed to solve the allocation problem. For the MAV allocation phase, the optimal model is firstly constructed and it is decomposed according to the ideal of greed to reduce the time complexity of the algorithm. Based on the above phases, the MAV/UAV task coalition formation method is proposed and the effectiveness and practicability are demonstrated by simulation examples.

Intervention decision-making in MAV/UAV cooperative engagement based on human factors engineering

Aiming at the intervention decision-making problem in manned/unmanned aerial vehicle（MAV/UAV） cooperative engagement, this paper carries out a research on allocation strategy of emergency discretion based on human factors engineering（HFE）.Firstly, based on the brief review of research status of HFE, it gives structural description to emergency in the process of cooperative engagement and analyzes intervention of commanders. After that,constraint conditions of intervention decision-making of commanders based on HFE（IDMCBHFE） are given, and the mathematical model, which takes the overall efficiency value of handling emergencies as the objective function, is established. Then, through combining K-best and variable neighborhood search（VNS） algorithm, a K-best optimization variable neighborhood search mixed algorithm（KBOVNSMA） is designed to solve the model. Finally,through three groups of simulation experiments, effectiveness and superiority of the proposed algorithm are verified.

Joint Scheduling and Resource Allocation for Federated Learning in SWIPT-Enabled Micro UAV Swarm Networks

Micro-UAV swarms usually generate massive data when performing tasks. These data can be harnessed with various machine learning(ML) algorithms to improve the swarm’s intelligence. To achieve this goal while protecting swarm data privacy, federated learning(FL) has been proposed as a promising enabling technology. During the model training process of FL, the UAV may face an energy scarcity issue due to the limited battery capacity. Fortunately, this issue is potential to be tackled via simultaneous wireless information and power transfer(SWIPT). However, the integration of SWIPT and FL brings new challenges to the system design that have yet to be addressed, which motivates our work. Specifically,in this paper, we consider a micro-UAV swarm network consisting of one base station(BS) and multiple UAVs, where the BS uses FL to train an ML model over the data collected by the swarm. During training, the BS broadcasts the model and energy simultaneously to the UAVs via SWIPT, and each UAV relies on its harvested and battery-stored energy to train the received model and then upload it to the BS for model aggregation. To improve the learning performance, we formulate a problem of maximizing the percentage of scheduled UAVs by jointly optimizing UAV scheduling and wireless resource allocation. The problem is a challenging mixed integer nonlinear programming problem and is NP-hard in general. By exploiting its special structure property, we develop two algorithms to achieve the optimal and suboptimal solutions, respectively. Numerical results show that the suboptimal algorithm achieves a near-optimal performance under various network setups, and significantly outperforms the existing representative baselines. considered.

某型MAV的自主导航算法及飞行试验

针对某型固定翼MAV,在飞行姿态控制和高度控制已经比较完善、机载GPS精度受到制约并存在信号延时的状况下,对该MAV的导航算法进行了探讨。历经多次计算机仿真和飞行试验,根据PID控制原理,设计并完善了适合该MAV的导航算法,给出了基于此算法的飞行试验结果。试验结果表明,该算法满足MAV执行长距离飞行任务的要求。