Kinematic and Joint Compliance Modeling Method to Improve Position Accuracy of a Robotic Vision System.

In the field of robotic automation, achieving high position accuracy in robotic vision systems (RVSs) is a pivotal challenge that directly impacts the efficiency and effectiveness of industrial applications. This study introduces a comprehensive modeling approach that integrates kinematic and joint compliance factors to significantly enhance the position accuracy of a system. In the first place, we develop a unified kinematic model that effectively reduces the complexity and error accumulation associated with the calibration of robotic systems. At the heart of our approach is the formulation of a joint compliance model that meticulously accounts for the intricacies of the joint connector, the external load, and the self-weight of robotic links. By employing a novel 3D rotary laser sensor for precise error measurement and model calibration, our method offers a streamlined and efficient solution for the accurate integration of vision systems into robotic operations. The efficacy of our proposed models is validated through experiments conducted on a FANUC LR Mate 200iD robot, showcasing notable improvements in the position accuracy of robotic vision system. Our findings contribute a framework for the calibration and error compensation of RVS, holding significant potential for advancements in automated tasks requiring high precision.

Perspective transformation based-initial value estimation for the speckle control points matching in an out-of-focus camera calibration using a synthetic speckle pattern.

Despite camera calibration methods using regular planar chessboard or circular marker array calibration targets having been widely used, the control point extraction accuracy is low if the image is defocused or if the noise level is high. Due to the noise robustness of digital image correlation (DIC) in speckle image matching, random speckle pattern is a better choice for camera calibration than chessboard or circular markers, if the imaging quality is low. The foremost process of this method is to conduct speckle control points matching DIC, where the initial value must be estimated close to the true value. It is challenging to provide accurate initial values for DIC if the difference of physical pixel scale is large between the reference image and the target image or if the target image is out-of-focus. To solve this problem, this work presents an efficient initial value estimation method for speckle control points matching using DIC, based on perspective transformation. Firstly, the four pairs of corners of the speckle regions in the reference image and target image are detected. Secondly, the target image is transformed to a new image that has the considerable size of pixel scale with the reference image, then four neighborhood points of the control point in the reference image and the corresponding points in the transformed new image are matched coarsely by fixed subset searching. Lastly, the matched points in the transformed target image are transformed back to the origin target image by the inverse perspective transformation matrix, then the initial value for DIC can be estimated by the matched four pairs of neighborhood points. Experiment results confirm the higher calibration accuracy delivered by the proposed method, rather than that of the chessboard or the circular marker array. Measurement precision is higher than the speckle pattern calibration method that uses SIFT-based initial value estimation.

Accurate estimation of camera response function for high dynamic range measurement.

A high dynamic range surface has large reflectivity variations and measuring it with a structured light technique could cause pixel saturation that will seriously degrade the measurement accuracy. Accurate identification of saturated pixels will significantly improve the measurement accuracy. This paper introduces an accurate estimation approach to the camera response function (CRF) for high dynamic range measurement and proposes a method that uses the estimated CRF to identify the saturated pixels in captured images. We perform experiments using the proposed saturation identification method and the other existing methods to compare both the saturation identification accuracy and measurement accuracy. The experiment results verify both the accuracy and suitability of the proposed method.

3D Metrology Using One Camera with Rotating Anamorphic Lenses.

In this paper, a novel 3D metrology method using one camera with rotating anamorphic lenses is presented based on the characteristics of double optical centers for anamorphic imaging. When the anamorphic lens rotates -90° around its optical axis, the 3D data of the measured object can be reconstructed from the two anamorphic images captured before and after the anamorphic rotation. The anamorphic lens imaging model and a polynomial anamorphic distortion model are firstly proposed. Then, a 3D reconstruction model using one camera with rotating anamorphic lenses is presented. Experiments were carried out to validate the proposed method and evaluate its measurement accuracy. Compared with stereo vision, the main advantage of the proposed 3D metrology approach is the simplicity of point matching, which makes it suitable for developing compact sensors for fast 3D measurements, such as car navigation applications.

Error self-correction method for phase jump in multi-frequency phase-shifting structured light.

Among 3D measurement approaches, multi-frequency phase-shifting structured light has advantages such as high resolution and high sampling rate owing to its point-to-point calculation method. However, there is always phase jump in the measurement process, which greatly reduces measurement accuracy. This paper proposes an error self-correction method for phase jump based on the multi-frequency heterodyne approach. The method uses redundant measurement data to implement self-correction and does not require additional data acquisition steps. We perform both simulations and experiments using the proposed error self-correction method and the classical heterodyne approach to compare the results. The experiment results verify both the accuracy and suitability of the proposed method.

Underwater fuel assembly deformation measurement with multisensor dual-line structured light.

The fuel assembly is the core component of the nuclear energy system, and its excessive deformation will affect the normal insertion of the fuel rod and endanger safe operation of the reactor. In this paper, we present an underwater fuel assembly deformation measurement system based on 10 sets of visual measurement units. Benefitting from the waterproof design, the shielding-radiation design, the reflective structure design, the dual-optical line laser triangulation principle, and the underwater multilayer refractive geometry, the measurement system has shorter measurement time, higher measurement accuracy, and better environmental adaptability. Through the underwater field measurement and verification of the standard rod and fuel assembly, the bow deformation measurement accuracy of the measurement system is better than 0.3 mm, and the twist deformation measurement accuracy of the system is better than 0.15°. The proposed method allows transient and high-precision measurement in a certain irradiation dose and certain depth water, which provides a strong guarantee for measurement of fuel assembly deformation parameters in deep water and high radiation.

A Topology-Based Stereo Matching Method for One Shot 3D Measurement Using Coded Spot-Array Structured Light.

In this paper, a topology-based stereo matching method for 3D measurement using a single pattern of coded spot-array structured light is proposed. The pattern of spot array is designed with a central reference ring spot, and each spot in the pattern can be uniquely coded with the row and column indexes according to the predefined topological search path. A method using rectangle templates to find the encoded spots in the captured images is proposed in the case where coding spots are missing, and an interpolation method is also proposed for rebuilding the missing spots. Experimental results demonstrate that the proposed technique could exactly and uniquely decode each spot and establish the stereo matching relation successfully, which can be used to obtain three-dimensional (3D) reconstruction with a single-shot method.

Laser-speckle-projection-based handheld anthropometric measurement system with synchronous redundancy reduction.

Human body measurement is essential in modern rehabilitation medicine, which can be effectively combined with the technology of additive manufacturing. Digital image correlation based on laser speckle projection is a single-shot, accurate, and robust technique for human body measurement. In this paper, we present a handheld anthropometric measurement system based on laser speckle projection. A flexible retroreflective marker target is designed for multi-view data registration. Meanwhile, a synchronous redundancy-reduction algorithm based on a re-projected global disparity map is proposed. Experiment results validate that the proposed system is effective and accurate for different human body part measurements. Comparative experiments show that the proposed redundancy-reduction algorithm has high efficiency and can effectively preserve the features of complex shapes. The comprehensive performance of the algorithm is better than the other two tested methods.

Comparative Analysis of Warp Function for Digital Image Correlation-Based Accurate Single-Shot 3D Shape Measurement.

Digital image correlation (DIC)-based stereo 3D shape measurement is a kind of single-shot method, which can achieve high precision and is robust to vibration as well as environment noise. The efficiency of DIC has been greatly improved with the proposal of inverse compositional Gauss-Newton (IC-GN) operators for both first-order and second-order warp functions. Without the algorithm itself, both the registration accuracy and efficiency of DIC-based stereo matching for shapes with different complexities are closely related to the selection of warp function, subset size, and convergence criteria. Understanding the similarity and difference of the impacts of prescribed subset size and convergence criteria on first-order and second-order warp functions, and how to choose a proper warp function and set optimal subset size as well as convergence criteria for different shapes are fundamental problems in realizing efficient and accurate 3D shape measurement. In this work, we present a comparative analysis of first-order and second-order warp functions for DIC-based 3D shape measurement using IC-GN algorithm. The effects of subset size and convergence criteria of first-order and second-order warp functions on the accuracy and efficiency of DIC are comparatively examined with both simulation tests and real experiments. Reference standards for the selection of warp function for different kinds of 3D shape measurement and the setting of proper convergence criteria are recommended. The effects of subset size on the measuring precision using different warp functions are also concluded.

Efficient Background Segmentation and Seed Point Generation for a Single-Shot Stereo System.

Single-shot stereo 3D shape measurement is becoming more popular due to its advantages of noise robustness and short acquisition period. One of the key problems is stereo matching, which is related to the efficiency of background segmentation and seed point generation, etc. In this paper, a more efficient and automated matching algorithm based on digital image correlation (DIC) is proposed. The standard deviation of image gradients and an adaptive threshold are employed to segment the background. Scale-invariant feature transform (SIFT)-based feature matching and two-dimensional triangulation are combined to estimate accurate initial parameters for seed point generation. The efficiency of background segmentation and seed point generation, as well as the measuring precision, are evaluated by experimental simulation and real tests. Experimental results show that the average segmentation time for an image with a resolution of 1280 × 960 pixels is 240 milliseconds. The efficiency of seed point generation is verified to be high with different convergence criteria.

Modeling and Calibration of a Novel One-Mirror Galvanometric Laser Scanner.

A laser stripe sensor has limited application when a point cloud of geometric samples on the surface of the object needs to be collected, so a galvanometric laser scanner is designed by using a one-mirror galvanometer element as its mechanical device to drive the laser stripe to sweep along the object. A novel mathematical model is derived for the proposed galvanometer laser scanner without any position assumptions and then a model-driven calibration procedure is proposed. Compared with available model-driven approaches, the influence of machining and assembly errors is considered in the proposed model. Meanwhile, a plane-constraint-based approach is proposed to extract a large number of calibration points effectively and accurately to calibrate the galvanometric laser scanner. Repeatability and accuracy of the galvanometric laser scanner are evaluated on the automobile production line to verify the efficiency and accuracy of the proposed calibration method. Experimental results show that the proposed calibration approach yields similar measurement performance compared with a look-up table calibration method.

Development and Verification of a Novel Robot-Integrated Fringe Projection 3D Scanning System for Large-Scale Metrology.

Large-scale surfaces are prevalent in advanced manufacturing industries, and 3D profilometry of these surfaces plays a pivotal role for quality control. This paper proposes a novel and flexible large-scale 3D scanning system assembled by combining a robot, a binocular structured light scanner and a laser tracker. The measurement principle and system construction of the integrated system are introduced. A mathematical model is established for the global data fusion. Subsequently, a robust method is introduced for the establishment of the end coordinate system. As for hand-eye calibration, the calibration ball is observed by the scanner and the laser tracker simultaneously. With this data, the hand-eye relationship is solved, and then an algorithm is built to get the transformation matrix between the end coordinate system and the world coordinate system. A validation experiment is designed to verify the proposed algorithms. Firstly, a hand-eye calibration experiment is implemented and the computation of the transformation matrix is done. Then a car body rear is measured 22 times in order to verify the global data fusion algorithm. The 3D shape of the rear is reconstructed successfully. To evaluate the precision of the proposed method, a metric tool is built and the results are presented.

Aberration correction of double-sided telecentric zoom lenses using lens modules.

A systematic approach for the aberration design of a four-component double-sided telecentric zoom lens system is presented. The Gaussian structure of the zoom system is specified previously which means the powers and movements of components are known. Each component is treated as a lens module during the design stage with specified first-order properties and third-order aberration targets. The third-order aberration targets of the first component are determined by minimizing the whole aberrations of the zoom lens system using a genetic algorithm (GA). And the aberration targets of components behind are determined by reoptimization with already fixed structures of previous components. Mean pupil spherical aberration of every component in every zoom position is adopted in the objective function to control high-order aberrations. The thin lens structure of each component can be determined from their first-order properties and aberration targets. After lens thickening and reoptimization, the zoom lens system can finally be determined.

Aberration design of zoom lens systems using thick lens modules.

A systematic approach for the aberration design of a zoom lens system using a thick lens module is presented. Each component is treated as a thick lens module at the beginning of the design. A thick lens module refers to a thick lens component with a real lens structure, like lens materials, lens curvatures, lens thicknesses, and lens interval distances. All nine third-order aberrations of a thick lens component are considered during the design. The relationship of component aberrations in different zoom positions can be approximated from the aberration shift. After minimizing the aberrations of the zoom lens system, the nine third-order aberrations of every lens component can be determined. Then the thick lens structure of every lens component can be determined after optimization according to their first-order properties and third-order aberration targets. After a third optimization for minimum practical third-order aberrations of a zoom lens system, the aberration design using the thick lens module is complete, which provides a practical zoom lens system with thick lens structures. A double-sided telecentric zoom lens system is designed using the thick lens module in this paper, which shows that this method is practical for zoom lens design.

Paraxial analysis of double-sided telecentric zoom lenses with three components.

A general method for the calculation of paraxial design parameters of a double-sided telecentric zoom lens system with three components is given. Formulas that define the interval distances between components while zooming, the extremum of magnification, and the magnification of each component are derived. The kinetic property of the zoom system is also discussed. As a result of the study, a classification of three-component double-sided telecentric zoom lenses is given, which is based on the magnification, signs of component optical power, and the position of stop while zooming. Some numerical examples are given in the third section of the paper.

Numerical Simulation and Experimental Analysis on Seam Feature Size and Deformation for T-Joint Laser-GMAW Hybrid Welding.

As an innovative technique, laser-GMAW hybrid welding manifests significant superiority in enhancing welding productivity and quality, albeit the optimization of process parameters poses a challenge for practical application. The present manuscript elucidates the influence of process parameters on the dimensional characteristics of the welding seam and the distortion of 8 mm T-joints in the context of laser-GMAW hybrid welding, and channels both simulation and experimentation. The outcomes denote that the dual conical model serves as an efficacious aid for the numerical simulation of T-joint laser-GMAW hybrid welding. Furthermore, the repercussions of process parameters on welding seam dimensional characteristics remain consistently similar in both the simulation and experimental results. From the simulation outcomes, it becomes apparent that the distortion of the base material can be efficiently managed by implementing anti-distortion measures. This inquiry offers both a theoretical and experimental foundation for optimizing process parameters of T-joint laser-GMAW hybrid welding, presenting certain engineering applicability.

UTRAD: Anomaly detection and localization with U-Transformer.

Anomaly detection is an active research field in industrial defect detection and medical disease detection. However, previous anomaly detection works suffer from unstable training, or non-universal criteria of evaluating feature distribution. In this paper, we introduce UTRAD, a U-TRansformer based Anomaly Detection framework. Deep pre-trained features are regarded as dispersed word tokens, and represented with transformer-based autoencoders. With reconstruction on more informative feature distribution instead of raw images, we achieve a more stable training process and a more precise anomaly detection and localization result. In addition, our proposed UTRAD has a multi-scale pyramidal hierarchy with skip connections that help detect both multi-scale structural and non-structural anomalies. As attention layers are decomposed to multi-level patches, UTRAD significantly reduces the computational cost and memory usage compared with the vanilla transformer. Experiments on industrial dataset MVtec AD and medical datasets Retinal-OCT, Brain-MRI, Head-CT have been conducted. Our proposed UTRAD out-performs all other state-of-the-art methods in the above datasets. Code released at https://github.com/gordon-chenmo/UTRAD.

A Neurodynamic Approach to Distributed Optimization With Globally Coupled Constraints.

In this paper, a distributed neurodynamic approach is proposed for constrained convex optimization. The objective function is a sum of local convex subproblems, whereas the constraints of these subproblems are coupled. Each local objective function is minimized individually with the proposed neurodynamic optimization approach. Through information exchange between connected neighbors only, all nodes can reach consensus on the Lagrange multipliers of all global equality and inequality constraints, and the decision variables converge to the global optimum in a distributed manner. Simulation results of two power system cases are discussed to substantiate the effectiveness and characteristics of the proposed approach.

Toward Support-free 3D Printing: A Skeletal Approach for Partitioning Models.

Minimizing support structures is crucial in reducing 3D printing material and time. Partition-based methods are efficient means in realizing this objective. Although some algorithms exist for support-free fabrication of solid models, no algorithm ever considers the problem of support-free fabrication for shell models (i.e., hollowed meshes). In this paper, we present a skeleton-based algorithm for partitioning a 3D surface model into the least number of parts for 3D printing without using any support structure. To achieve support-free fabrication while minimizing the effect of the seams and cracks that are inevitably induced by the partition, which affect the aesthetics and strength of the final assembled surface, we put forward an optimization system with the minimization of the number of partitions and the total length of the cuts, under the constraints of support-free printing angle. Our approach is particularly tailored for shell models, and it can be applicable to solid models as well. We first rigorously show that the optimization problem is NP-hard and then propose a stochastic method to find an optimal solution to the objectives. We propose a polynomial-time algorithm for a special case when the skeleton graph satisfies the requirement that the number of partitioned parts and the degree of each node are bounded by a small constant. We evaluate our partition method on a number of 3D models and validate our method by 3D printing experiments.

[A study on new computer-aided modeling method of hip joint].

The main reason of invalidation of prosthetic hip joint is the prostheses flexibility and shift, dislocation and disjunction. Promoting the long time stability of the prostheses is the key of improving the long term hip joint replacement effect. Former research work was focused on the upper segment of femur, and assumed the acetabulum cup to be a spheric concave, and the external form of acetabulum prostheses was basically semi spheric. This paper presents a method of acquiring the point data on the surface of the hip bone using the reverse engineering technology. By analyzing the acetabulum surface fitting error we use rotating elliptical surface to fit the acetabulum surface, together with the optimal technique to build up the CAD model of acetabulum surface. We compare the fitting error between the sphere fitting and rotating elliptical surface fitting and get the result that the rotating elliptical surface fitting error is smaller than the sphere fitting error, and the rotating elliptical surface can describe the shape of the acetabulum better.