This paper presents a novel ultrasonic signal denoising method that integrates adaptive variational mode decomposition (AVMD) with convolutional neural networks (CNNs). Initially, the whale optimisation algorithm (WOA) is employed to optimise key parameters of variational mode decomposition, specifically the decomposition modes K and the penalty factor α. The ultrasonic signals are then decomposed into intrinsic mode functions (IMFs) and various statistical feature parameters, such as energy entropy, sample entropy, kurtosis and correlation factors, are calculated for each IMF. The signal-to-noise ratio (SNR) of the reconstructed signal from the IMFs is used to assign label values, forming a feature dataset. Subsequently, a CNN is utilised to train and recognise this dataset, achieving an accuracy rate of 93.94% on the test set. The results demonstrate that the CNN effectively distinguishes between various IMF combinations based on the reconstructed SNR and can proficiently identify IMF combinations with higher SNR. Finally, denoising experiments on actual ultrasonic echo signals validate the feasibility of this method for noise reduction applications. © 2024 British Institute of Non-Destructive Testing. All rights reserved.

In ultrasonic testing of Carbon Fiber Reinforced Polymers (CFRP), signals of near-surface flaws are often submerged in interface signals, resulting in blind spots for defect detection. To address this issue, this paper presents an autocorrelation imaging algorithm that combines Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) with ${l_0}$l0-norm sparse representation. Firstly, the ultrasonic signal can be modelled as a convolution process. By using MOMEDA, the sequence of reflected pulses in the ultrasonic signal is obtained. Then, the ${l_0}$l0-norm sparse representation is utilised to enhance the temporal resolution of the sequence, successfully separating near-surface defect signals from interface signals. Finally, the processed data is input into the autocorrelation algorithm, achieving automated imaging of near-surface flaws. Simulation results demonstrate the efficacy of the algorithm in separating overlapping signals. Finally, experimental validation was conducted on flaws at three different depths. The algorithm presented in this paper is capable of identifying all near-surface flaws, with a defect size error rate consistently below 5%.

The poor pose accuracy of industrial robots restricts their further application in aviation manufacturing. Kinematic calibration based on position errors is a traditional method to improve robot accuracy. However, due to the difference between length errors and angle errors in the order of magnitude, it is difficult to accurately calibrate these geometric parameters together. In this paper, a two-step method for robot kinematic parameters calibration and a novel method for position and orientation measurement are proposed and combined to identify these two kinds of errors respectively. The redundant parameter errors that affect the identification are also analyzed and eliminated to further improve the accuracy of this two-step method. Taking the Levenberg-Marquardt algorithm as the underlying algorithm, simulation results indicate that the proposed two-step calibration method has faster iteration speed and higher identification accuracy than the traditional one. On this basis, the calibration and measurement methods proposed in this paper are verified on a heavy-duty robot used for fiber placement. Experimental results show that the mean absolute position error decreases from 0.9906 mm to 0.3703 mm after calibration by the proposed two-step calibration method with redundancy elimination. The absolute position accuracy has increased by 41.81% compared with the traditional method based on position errors only and 14.97% compared with the two-step calibration method without redundancy elimination. At the same time, the orientation errors after calibration are not more than 0.1485°, and the average of absolute errors is 0.0447. © 2023

目标抓取是机器人最为典型的应用之一,而随机运动目标的跟踪抓取则是机器人目标抓取领域最具挑战性的课题,可显著提升机器人操作的效率、柔性以及人机或者多机协作能力,具有广泛的应用前景。视觉是机器人最为重要的感知技术之一,结合机器视觉与机器人控制的视觉伺服技术为随机运动目标的跟踪抓取提供了可行的解决方案。然而,当前的视觉伺服技术对于随机运动目标的跟踪抓取仍然存在明显不足,其核心问题在于对随机运动目标的跟踪抓取算法缺乏的实时性与鲁棒性。本文以视觉伺服技术为基础,围绕视觉引导下的随机运动目标的跟踪抓取这一主题,对视觉目标的跟踪以及机器人的跟踪抓取控制算法进行改进了深入研究。主要研究内容包括:研究了基于改进几何粒子滤波的视觉跟踪算法,提升了动态跟踪的实时性和鲁棒性。首先,通过边缘提取和形态学膨胀的方式,对特征点进行精简。然后,用HSV颜色特征替代原有的灰度值特征。最后,在每一次准确的跟踪后对目标模板按一定比例进行更新。通过OTB2015数据集的测试,改进后的算法将跟踪速度由45.68fps提高到52.71fps,中心位置误差由0.61提高到0.74。重构了运动目标物在三维空间中的位姿,使改进几何粒子滤波的视觉跟踪的结果能够用于机器人的伺服控制。采用RGB-D相机彩图与深度图相结合的方式对视觉跟踪结果进行三维重构。重构过程中为了提高目标物位姿的计算速度,没有重构整个点云信息,而是有针对性地选取目标物上少量点的深度信息,通过SVD分解得到目标物的位姿。建立了“远距离跟踪近距离拦截抓取”的机器人动态跟踪抓取策略,提升了机器人跟踪抓取的快速性。跟踪过程采用基于位置的视觉伺服（position based visual servoing,PBVS）,并对PBVS中目标物的位姿进行预测和偏移,减少了跟踪过程中的机器人运动的滞后,降低了跟踪过程机器人本体遮挡目标物可能性。搭建了机器人视觉跟踪抓取实验平台,开展了一维直线运动、二维随机运动和三维随机运动三类运动目标物的跟踪抓取实验。实验结果表明,本文提出的基于改进几何粒子滤波运动目标跟踪抓取算法具有良好的实时性和鲁棒性,三维随机运动目标物的最大平移速度达到0.25m/s,最大旋转角速度达到70°/s。

大型复杂构件如风电叶片、航空结构件、船舶壳体的打磨是一项复杂而又繁重的工作,目前仍以人工打磨为主。为了提高大型复杂构件打磨质量和效率,机器人取代人工打磨已经是必然趋势。为实现大型复杂构件机器人高效高精加工,需要对加工轨迹规划模型、工件模型处理、曲面轨迹规划等过程进行研究。以上研究中仍存在以下问题:机器人加工模型依赖人工多次实验和调整,费时费力;工件在吊装放置或搬运后CAD模型与工件尺寸不一致,机器人加工精度不能保证;大型复杂构件模型数据信息大、处理困难,造成机器人轨迹规划速度慢精度低。本文根据大型复杂构件特点,提出了一种基于三维点云的大型复杂构件机器人自主加工轨迹规划方法,并结合OpenCascade几何引擎开发出大型复杂构件机器人自主加工轨迹规划软件。本文主要研究内容如下:对机器人自主加工轨迹规划中机器人末端和工件建立模型。结合大型复杂构件尺寸巨大、加工精度要求高等特点,分别建立机器人末端的工艺参数模型和工件的姿态矢量参数模型。基于赫兹接触理论构建了机器人末端的工艺参数模型,基于非均匀主成分分析法求解出工件的姿态信息。对大型复杂构件点云处理与模型重构。基于迭代就近点法提出一种靶标变换矩阵的点云空间转换方法,根据靶标变换矩阵对点云模型做出空间变换,实现工件在搬运后点云信息自动变换。通过最大偏角约束,对三维点云分片处理。提出一种无序点排序算法,将复杂的点云模型简化为有序点集,最后通过B样条曲线拟合完成三维重构。将三维重构的曲面作为加工对象,采用黄金分割法对加工廓形约束下的关键参数最优化求解。利用包容盒算法进行轨迹规划并获得轨迹点坐标。采用曲线点抽稀算法,将密集的轨迹点精简成等弦差的轨迹点,提高轨迹规划速率。结合曲面的参数几何特性,计算轨迹点在曲面上的参数微分值,根据模型的姿态信息,自动约束机器人末端矢量方向,结合轨迹点坐标与方向矢量生成机器人加工轨迹信息。基于所述的基于三维点云的大型复杂构件机器人自主加工轨迹规划方法,设计并开发了大型复杂构件机器人加工轨迹规划系统。并以风电叶片为实验对象,验证了本文轨迹规划方法的有效性和大型复杂构件机器人轨迹规划软件的实用性。

近年来现代制造业在自动化、智能化方向取得了长足的进步,工业机器人在加工生产中也扮演着日益重要的角色。对于弱刚性构件,如果采用单侧机器人加工,在加工时由于工件刚性较弱会产生较大的变形,难以保证加工表面质量和精度。双机器人对侧互为支撑加工为弱刚性构件加工提供了新的解决方案。本文基于阻抗控制策略,提出一种针对弱刚性构件磨抛的双机机器人对侧顺应控制策略,利用镜像加工力的相互抵消,减小工件在加工中的受力变形,并通过顺应控制策略解决加工中出现的过磨问题,达到提升表面加工质量、降低次品率的目的。本文主要研究内容如下:首先,对六自由度协作机器人的运动学与动力学建模方法进行研究。针对本文所涉及的UR5机器人,完成正逆运动学建模,并利用微分运动学求解UR5机器人的定位误差补偿模型以及Jacobi矩阵。最后针对空间三自由度机器人模型,完成动力学建模,建立机器人构型与关节力矩间的映射关系。其次,针对加工中存在的过磨与刚性碰撞问题,提出一种基于位置阻抗控制原理的机器人顺应控制策略。对单侧机器人,以关节角速度增量为控制量设计控制器,并且根据外力矩与控制量之间的映射关系提出了线性与非线性两种控制策略。对双机器人加工系统,以两侧机器人的交互力作为力反馈闭环的输入,分别提出线性与非线性控制策略,并且通过仿真验证各控制器的轨迹跟踪与力顺应性能,证明控制策略的有效性。接着,基于机器人的运动学与动力学模型,分别从运动范围与响应速度两个方面对最佳顺应关节进行选取。针对各控制策略中力响应特性的不同,对不同参数下控制器的稳定性进行分析,选取最优的控制器参数,提升了控制系统的鲁棒性。最后,利用双目视觉标定平台完成了两侧机器人的结构参数标定,提升机器人的绝对定位精度。利用双机力控打磨平台,对单侧加工与双机对侧加工分别进行研究。通过分析机器人在加工时末端力与力矩的变化情况,对比各组参数下的表面加工质量,确定两侧机器人刚度非对称时对应的控制器参数为最优的控制器参数,证明了理论分析的结论。

The increasing integrated manufacturing of composite components required efficiency and reliable one-sided connection. In this paper, the effect of riveting displacement on the mechanical behavior of CFRP single-lap bolted joints with a new type of one-sided connected fastener named elliptical-head non-lug self-locking (EHNL) rivet nut was studied. A three-dimensional FE model considering CFRP damage and nut material plastic deformation was developed. The whole process of the joint from nut forming to static tensile could be simulated by the FE model. The unique load-drop phenomenon in the tensile response of the joint with the EHNL rivet nut was explained. The forming quality, tensile, and fatigue behavior of joints and progressive damage of CFRP with five riveting displacements were comprehensively studied by experiments and numerical simulations. The results showed that the tensile strength increased with the increase of the riveting displacement, while the fatigue life decreased due to the larger residual stress and damage of CFRP caused by the larger riveting displacement in the nut-forming process. The fatigue life of the joint should be considered more significantly in the selection of the riveting displacement.

As a kind of interference rivet with compensation at the head, the reduced countersunk head half-crown rivet can achieve long life sealing riveting for airplane types or engine rooms with special requirements for sealing and fatigue performance. In the present study, a quartered three-dimensional finite element model was created to simulate the riveting process. The FE model was validated from the perspective of driven head dimensions and interferences based on the experiment results. The effects of feed displacement of rivet dies, feed speed ratio of rivet dies, pre-feed displacement of upper rivet die and countersunk depth on the forming quality of riveted lap joints were systematically studied. The results showed that the average interference increased with the increase of the feed displacement of rivet dies, while it decreased with the increase of the feed speed ratio of rivet dies and pre-feed displacement of upper rivet die. The countersunk depth of 1.05 mm is the transition depth for the rivet studied in this paper. In addition, the results of combined effect of feed speed ratio of rivet dies and pre-feed displacement of upper rivet die on the forming quality proved that the influence laws of single parameter were no longer suited for the scenario of combined effect of multiple riveting parameters. This paper can provide scientific and normative guidance for the applications of reduced countersunk head half-crown rivet in engineering structure.

近红外光学跟踪系统以其高精度、便捷的特点迅速发展成为手术导航中的重要组成部分。基于双目视觉设计了一套高精度、低成本的光学跟踪系统，该系统使用安装在器械上的被动标记球（NDI Passive Sphere）作为标记，并在双目相机镜头前添加近红外滤光片，以消除环境光的干扰。首先，采用轮廓过滤算法，用于标记轮廓的提取，使用最小二乘椭圆拟合算法获取标记投影中心的像素坐标；其次，设计了器械识别算法，使每个器械可以单独有序储存标记球的中心像素坐标，用于多个器械的分辨；最后，通过左右视图对应标记中心匹配，重建其空间坐标，进而推导出器械尖端在世界坐标系中的坐标。实验使用该系统跟踪器械，利用器械的无规则摆放实验验证了器械识别算法的准确性和鲁棒性，准确率达95%，平均识别时间仅需4 ms。并对系统进行了稳定性、静态定位精度、动态跟踪等测试。结果表明：稳定性误差可达0.13 mm，静态定位精度可达0.373 mm，所提出的近红外光学跟踪系统具有较高的精度和稳定性，可以满足手术导航的要求。