A salient edges detection algorithm of multi-sensor images and its rapid calculation based on PFCM kernel clustering

Multi-sensor image matching based on salient edges has broad prospect in applications, but it is difficult to extract salient edges of real multi-sensor images with noises fast and accurately by using common algorithms. According to the analysis of the features of salient edges, a novel salient edges detection algorithm and its rapid calculation are proposed based on possibility fuzzy C-means （PFCM） kernel clustering using two-dimensional vectors composed of the values of gray and texture. PFCM clustering can overcome the shortcomings that fuzzy C-means （FCM） cluster- ing is sensitive to noises and possibility C-means （PCM） clustering tends to find identical clusters. On this basis, a method is proposed to improve real-time performance by compressing data sets based on the idea of data reduction in the field of mathematical analysis. In addition, the idea that kernel-space is linearly separable is used to enhance robustness further. Experimental results show that this method extracts salient edges for real multi-sensor images with noises more accurately than the algorithm based on force fields and the FCM algorithm; and the proposed method is on average about 56 times faster than the PFCM algorithm in real time and has better robustness.     

Impulse feature extraction method for machinery fault detection using fusion sparse coding and online dictionary learning

Impulse components in vibration signals are important fault features of complex machines. Sparse coding(SC) algorithm has been introduced as an impulse feature extraction method, but it could not guarantee a satisfactory performance in processing vibration signals with heavy background noises. In this paper, a method based on fusion sparse coding(FSC) and online dictionary learning is proposed to extract impulses efficiently. Firstly, fusion scheme of different sparse coding algorithms is presented to ensure higher reconstruction accuracy. Then, an improved online dictionary learning method using FSC scheme is established to obtain redundant dictionary and it can capture specific features of training samples and reconstruct the sparse approximation of vibration signals. Simulation shows that this method has a good performance in solving sparse coefficients and training redundant dictionary compared with other methods. Lastly, the proposed method is further applied to processing aircraft engine rotor vibration signals. Compared with other feature extraction approaches, our method can extract impulse features accurately and efficiently from heavy noisy vibration signal, which has significant supports for machinery fault detection and diagnosis.     

Accuracy enhancement of the spherical actuator with a two-level geometric calibration method

This paper presents a two-level geometric calibration method for the permanent magnet （PM） spherical actuator to improve its motion control accuracy. The proposed actuator is com- posed of a stator with circumferential coils and a rotor with multiple PM poles. Due to the assembly and fabrication errors, the real geometric parameters of the actuator will deviate from their design values. Hence, the identification of such errors is critical for the motion control tasks. A two-level geometric calibration approach is proposed to identify such errors. In the first level, the calibration model is formulated based on the differential form of the kinematic equation, which is to identify the geometric errors in the spherical joint. In the second level, the calibration model is formulated based on the differential form of torque formula, which is to calibrate the geometric parameters of the magnetization axes of PM poles and coils axes. To demonstrate the robustness and availability of the calibration algorithm, simulations are conducted. The results have shown that the proposed two-level calibration method can effectively compensate the geometric parameter errors and improve the positioning accuracy of the spherical actuator.     

Precise control of a magnetically suspended double-gimbal control moment gyroscope using differential geometry decoupling method

Precise control of a magnetically suspended double-gimbal control moment gyroscope (MSDGCMG) is of vital importance and challenge to the attitude positioning of spacecraft owing to its multivariable, nonlinear and strong coupled properties. This paper proposes a novel linearization and decoupling method based on differential geometry theory and combines it with the internal model controller (IMC) to guarantee the system robustness to the external disturbance and parameter uncertainty. Furthermore, by introducing the dynamic compensation for the inner-gimbal rate-servo system and the magnetically suspended rotor (MSR) system only, we can eliminate the influence of the unmodeled dynamics to the decoupling control accuracy as well as save costs and inhibit noises effectively. The simulation results verify the nice decoupling and robustness performance of the system using the proposed method.     

Real-time fault detection method based on belief rule base for aircraft navigation system

Real-time and accurate fault detection is essential to enhance the aircraft navigation system’s reliability and safety. The existent detection methods based on analytical model draws back at simultaneously detecting gradual and sudden faults. On account of this reason, we propose an online detection solution based on non-analytical model. In this article, the navigation system fault detection model is established based on belief rule base (BRB), where the system measuring residual and its changing rate are used as the inputs of BRB model and the fault detection function as the output. To overcome the drawbacks of current parameter optimization algorithms for BRB and achieve online update, a parameter recursive estimation algorithm is presented for online BRB detection model based on expectation maximization (EM) algorithm. Furthermore, the proposed method is verified by navigation experiment. Experimental results show that the proposed method is able to effectively realize online parameter evaluation in navigation system fault detection model. The output of the detection model can track the fault state very well, and the faults can be diagnosed in real time and accurately. In addition, the detection ability, especially in the probability of false detection, is superior to offline optimization method, and thus the system reliability has great improvement.     

Electrochemical machining of a convex strips structure on a revolving part by using site directed power interruption

Revolving parts with complex surface structures are widely used in machinery and mechanical equipment. The ECM process provides its adequacy to cut hard materials with different shapes, and its applications are widely increased, due to its outstanding advantages. In this paper, a new method for machining a convex strips structure on a cylinder by using site directed power interruption(SDPI) in the ECM process is presented. A variable correction value of the power-off time was defined and optimized to obtain the ideal interval for better machining accuracy and stability.The electric field distribution and the simulated convex profiles show that the stray current density can be reduced effectively by using the proposed method. The correction value has an important influence on the machining accuracy. A suitable correction value in the range of 0.6–1.2 s can effectively improve the machining accuracy of the convex strips structure. Experiments were also conducted to verify the proposed method. Results have confirmed that the stray corrosion on the convex strips surface is significantly reduced and the machining accuracy of convex strips structure is remarkably improved by using the proposed method with a suitable correction value in the ECM process. Finally, a convex strip with a height of 2 mm on a thin-wall revolving part was also produced successfully using a correction value of 0.9.     

Dynamic modeling of a hose-drogue aerial refueling system and integral sliding mode backstepping control for the hose whipping phenomenon

Dynamic modeling of a hose-drogue aerial refueling system（HDARS） and an integral sliding mode backstepping controller design for the hose whipping phenomenon（HWP） during probe-drogue coupling are studied. Firstly, a dynamic model of the variable-length hose-drogue assembly is built for the sake of exploiting suppression methods for the whipping phenomenon.Based on the lumped parameter method, the hose is modeled by a series of variable-length links connected with frictionless joints. A set of iterative equations of the hose＇s three-dimensional motion is derived subject to hose reeling in/out, tanker motion, gravity, and aerodynamic loads accounting for the effects of steady wind, atmospheric turbulence, and tanker wake. Secondly,relying on a permanent magnet synchronous motor and high-precision position sensors, a new active control strategy for the HWP on the basis of the relative position between the tanker and the receiver is proposed. Considering the strict-feedback configuration of the permanent magnet synchronous motor, a rotor position control law based on the backstepping method is designed to insure global stability. An integral of the rotor position error and an exponential sliding mode reaching law of the current errors are applied to enhance control accuracy and robustness. Finally,the simulation results show the effectiveness of the proposed model and control laws.     

A guidance law for UAV autonomous aerial refueling based on the iterative computation method

The rendezvous and formation problem is a significant part for the unmanned aerial vehicle（UAV） autonomous aerial refueling（AAR） technique. It can be divided into two major phases： the long-range guidance phase and the formation phase. In this paper, an iterative computation guidance law（ICGL） is proposed to compute a series of state variables to get the solution of a control variable for a UAV conducting rendezvous with a tanker in AAR. The proposed method can make the control variable converge to zero when the tanker and the UAV receiver come to a formation flight eventually. For the long-range guidance phase, the ICGL divides it into two sub-phases： the correction sub-phase and the guidance sub-phase. The two sub-phases share the same iterative process. As for the formation phase, a velocity coordinate system is created by which control accelerations are designed to make the speed of the UAV consistent with that of the tanker.The simulation results demonstrate that the proposed ICGL is effective and robust against wind disturbance.     

A generative deep learning framework for airfoil flow field prediction with sparse data

Deep learning has been probed for the airfoil performance prediction in recent years.Compared with the expensive CFD simulations and wind tunnel experiments, deep learning models can be leveraged to somewhat mitigate such expenses with proper means. Nevertheless, effective training of the data-driven models in deep learning severely hinges on the data in diversity and quantity. In this paper, we present a novel data augmented Generative Adversarial Network(GAN), da GAN, for rapid and accurate fl...     

Testability integrated evaluation method based on testability virtual test data

Testability virtual test is a new test method for testability verification, which has the advantages such as low cost, few restrictions and large sample of test data. It can be used to make up the deficiency of testability physical test. In order to take the advantage of testability virtual test data effectively and to improve the accuracy of testability evaluation, a testability integrated eval- uation method is proposed in this paper based on testability virtual test data. Considering the char- acteristic of testability virtual test data, the credibility analysis method for testability virtual test data is studied firstly. Then the integrated calculation method is proposed fusing the testability vir- tual and physical test data. Finally, certain helicopter heading and attitude system is presented to demonstrate the proposed method. The results show that the testability integrated evaluation method is feasible and effective.     

Real-time drogue recognition and 3D locating for UAV autonomous aerial refueling based on monocular machine vision

Drogue recognition and 3D locating is a key problem during the docking phase of the autonomous aerial refueling(AAR). To solve this problem, a novel and effective method based on monocular vision is presented in this paper. Firstly, by employing computer vision with red-ring-shape feature, a drogue detection and recognition algorithm is proposed to guarantee safety and ensure the robustness to the drogue diversity and the changes in environmental conditions, without using a set of infrared light emitting diodes(LEDs) on the parachute part of the drogue. Secondly, considering camera lens distortion, a monocular vision measurement algorithm for drogue 3D locating is designed to ensure the accuracy and real-time performance of the system,with the drogue attitude provided. Finally, experiments are conducted to demonstrate the effectiveness of the proposed method. Experimental results show the performances of the entire system in contrast with other methods, which validates that the proposed method can recognize and locate the drogue three dimensionally, rapidly and precisely.     

基于CFD的软式空中加油锥套气动特性研究

无人机空中加油缺少人为的观测与控制,若要进行自主控制下的空中加油,获取精准的气动数据则十分关键。基于CFD研究了国外某型锥套的气动特性,通过改变锥套支撑臂数量、空速以及稳定伞面积3个因素,构建了不同的三维子模型。利用数值计算分析了其对锥套气动参数的影响,并探究了影响规律。仿真结果表明,支撑臂数量和稳定伞面积均会影响阻力系数,而空速对阻力系数几乎没有影响。该方法可以用来预测影响因素发生变化时锥套阻力系数的变化趋势,为分析锥套气动特性提供了理论依据。     

N-fold Bernoulli probability based adaptive fast-tracking algorithm and its application to autonomous aerial refuelling

Recently, deep learning has been widely utilized for object tracking tasks. However, deep learning encounters limits in tasks such as Autonomous Aerial Refueling (AAR), where the target object can vary substantially in size, requiring high-precision real-time performance in embedded systems. This paper presents a novel embedded adaptiveness single-object tracking framework based on an improved YOLOv4 detection approach and an n-fold Bernoulli probability theorem. First, an Asymmetric Convolutional Network (ACNet) and dense blocks are combined with the YOLOv4 architecture to detect small objects with high precision when similar objects are in the background. The prior object information, such as its location in the previous frame and its speed, is utilized to adaptively track objects of various sizes. Moreover, based on the n-fold Bernoulli probability theorem, we develop a filter that uses statistical laws to reduce the false positive rate of object tracking. To evaluate the efficiency of our algorithm, a new AAR dataset is collected, and extensive AAR detection and tracking experiments are performed. The results demonstrate that our improved detection algorithm is better than the original YOLOv4 algorithm on small and similar object detection tasks; the object tracking algorithm is better than state-of-the-art object tracking algorithms on refueling drogue tracking tasks.     

无人机软管式自主空中加油视觉导航技术

近年来,机器视觉技术的迅速发展给无人机自主空中加油提供了一种新思路,克服了传统GPS动态适应能力差的不足。基于双目视觉,提出了一种实用的无人机自主空中加油导航方案,并提出了一种利用深度学习进行检测跟踪配合几何约束进行位姿计算的技术。根据加油机和受油机距离的不同,将导航分为远端导航和近端导航。全程对加油锥套进行检测和跟踪,并在近端进行锥套位姿计算。实验结果表明,该算法满足加油的鲁棒性要求,稳定跟踪锁定目标距离范围大于40m。当与目标距离小于10m时,距离误差优于±10cm。说明本文提出的方法具有较强的应用前景。     

Modeling and simulation of bow wave effect in probe and drogue aerial refueling

In a probe and drogue aerial refueling system, the bow wave of the receiver aircraft will produce a strong aerodynamic effect on the drogue once the receiver follows the drogue at a close distance. It is a major difficulty of docking control in the probe and drogue refueling. This paper analyses the bow wave effect and presents a simple method to model it. Firstly, the inviscid flow around the receiver is modeled based on the stream function defined by basic stream singularities.Secondly, a correction function is developed to eliminate the error caused by the absence of air viscosity. Then, the aerodynamic coefficients are used to calculate the induced aerodynamic force on the drogue. The obtained model is in an analytical form that can be easily applied to the controller design and the real-time simulations. In the verification part, computational fluid dynamics(CFD)simulation tests are conducted to validate the obtained flow fields and aerodynamic forces. Finally,the modeling method is applied to an F-16 receiver aircraft in a previously developed autonomous aerial refueling simulation system. The simulations results are analyzed and compared with the NASA flight-test data, which demonstrates the effectiveness of the proposed method.     

基于深度图推理的卫星背板部件检测方法

在轨加注是一种典型的在轨服务操作,它对于降低空间运输成本和任务风险起着重要的作用,视觉感知系统可以感知操作任务周围环境并提供给控制系统。目前在轨加注依赖于人,在人员监控下完成或通过遥操作完成,缺乏自主性。本文围绕未来高自主性的基于深度强化学习的在轨加注方法,对基于深度学习的视觉感知方法展开了研究,针对基于深度学习的方法对相似实例的检测存在精确率低、对光照变化敏感等缺点,提出了基于深度图推理的卫星背板部件检测方法。提出的方法可以有效地检测复杂形状的目标,不依赖于手工设计的特征;提高了复杂光照环境下部件的检测正确率;可以有效区分外形相似的不同部件;其有效性在数学仿真和物理仿真中均得到了验证。     

空中加油对接过程的动力学建模与仿真

空中加油的成功与否主要依赖于加油锥套与受油插头的顺利对接,对对接过程中产生的动力学行为的研究是实现顺利对接的前提。利用多刚体系统动力学建立对接过程的动力学模型,求解出加油锥套与受油装置间的广义速度;并依据建立的接触动力学模型,得到局部接触点间的相对速度。基于非线性连续碰撞理论,提出对接过程中碰撞力的计算方法,并在机械系统动力学仿真软件ADAMS的环境下,构建加油锥套和受油装置的对接虚拟样机。通过对虚拟样机的动力学仿真,得到了对接过程中的碰撞力及速度响应曲线,同时,得出能够实现成功对接时各参数的具体范围。结果表明,制约对接成功的主要参数是初始相对速度、加油锥套的俯仰角和相对偏距。该研究对空中加油的对接和受油机的导航控制具有重要的意义。     

先进布局飞机空中加油流场特性分析

针对先进布局飞机空中加油出现的问题,综合考虑加油机的加油区、加油机与受油机之间、吊舱锥套与受油机之间的流场特性,对先进布局飞机空中加油中遇到的流场进行了分析.通过理论分析和试飞验证,总结出一套切实可行的先进布局飞机空中对接加油试飞方法.     

无人机空中加油自主编队控制器设计

针对自主空中加油(AAR)任务中的飞行器编队形成问题,提出了一个完整的自主控制器方案。采用基于视线的编队制导律控制队形,产生制导指令。针对加油编队过程中产生的模型误差和尾涡扰动等不确定性,基于RBF神经网络和自适应Backstepping方法,设计了UAV姿态和速度非线性控制律来在线逼近和补偿不确定性,保证系统的鲁棒性和稳定性。使用协调转弯方式将航迹角指令转换为姿态指令,以实现控制律对航迹角指令的精确跟踪。最后,仿真结果验证了制导律和控制律的有效性。     

无人机空中加油过程中基于机器视觉的相对位姿估计(英文)

本文主要研究了在无人机空中自主加油过程中,利用安装在无人机上的摄像机得到的视觉图像确定无人机与加油机之间相对位置关系的算法。本文以经典的OI算法为基础展开研究,文章主要分为三个部分:首先简要介绍了OI算法的基本实现方法,指出了在OI算法中,旋转矩阵R的初值的选择对于位姿估计的精度有一定的影响。其次主要介绍了一种线性算法用于获取R初值的方法,同时针对空中加油过程中无人机与加油机之间的相对姿态角变化较小的特点,提出了可以将旋转矩阵R的初值设为单位矩阵。论文最后在仿真环境下对相关算法展开实验研究,同时研究了在高斯噪声环境下特征点在加油机表面的分布对位姿估计结果的影响,论文得到如下的结论:1、针对空中加油过程中无人机与加油机之间的相对姿态角变化较小的特点,在高斯噪声环境下,以单位矩阵作为旋转矩阵的初值得到的位姿估计的结果好于以线性算法为初值的结果。2、在相同的高斯噪声环境下,特征点之间的距离越大时得到的位姿估计结果的精度越高。3、特征点的数量对相对位姿估计的精度没有明显影响。