Point cloud anomaly detection is essential for various industrial applications. The huge computation and storage costs caused by the increasing product classes limit the application of single-class unsupervised methods, necessitating the development of multi-class unsupervised methods. However, the feature similarity between normal and anomalous points from different class data leads to the feature confusion problem, which greatly hinders the performance of multi-class methods. Therefore, we introduce a multi-class point cloud anomaly detection method, named GLFM, leveraging global-local feature matching to progressively separate data that are prone to confusion across multiple classes. Specifically, GLFM is structured into three stages: Stage-I proposes an anomaly synthesis pipeline that stretches point clouds to create abundant anomaly data that are utilized to adapt the point cloud feature extractor for better feature representation. Stage-II establishes the global and local memory banks according to the global and local feature distributions of all the training data, weakening the impact of feature confusion on the establishment of the memory bank. Stage-III implements anomaly detection of test data leveraging its feature distance from global and local memory banks. Extensive experiments on the MVTec 3D-AD, Real3D-AD and actual industry parts dataset showcase our proposed GLFM’s superior point cloud anomaly detection performance.Note to Practitioners—The proposed GLFM is employed for point cloud anomaly detection in industrial inspection, capable of simultaneously processing data across multiple classes. GLFM requires the collection of a set of normal product samples for model training, where the features of these samples are stored. If the feature distribution of a test sample deviates substantially from that of the normal samples, it is flagged as anomalous. GLFM not only exhibits outstanding performance on public datasets but has also been validated on a real-world industrial parts point cloud dataset.

Calibrating unknown transformation relationships is an essential task for multirobot cooperative systems. Traditional linear methods are inadequate to decouple and simultaneously solve the unknown matrices due to their intercoupling. This article proposes a novel dual-robot accurate calibration method that uses convex optimization and Lie derivative to solve the dual-robot calibration problem simultaneously. The key idea is that a convex optimization model based on dual-robot transformation chain is established using Lie representation of special Euclidean group in 3 dimensions [SE(3)]. The Jacobian matrix of the established optimization model is explicitly derived using the corresponding Lie derivative ofSE(3). To balance the influence of the magnitudes of the rotational and translational optimization variables, a weight coefficient is defined. Due to the closure and smoothness of Lie group, the optimization model can be solved simultaneously using Newton-like iterative methods without additional orthogonalization processing. The performance of the proposed method is verified through simulation and actual calibration experiments. The results show that the proposed method outperforms the previous calibration methods in terms of accuracy and stability. The actual experiments are used to compare the proposed method with two existing calibration methods, and the mean measurement error of a certified ceramic sphere is reduced from 0.9205 and 0.5363 to 0.4381 mm, respectively.

Industrial robots are characterized by good flexibility and a large working space, and offer a new approach for the machining of large and complex parts with small machining allowances (extra material allowed for subsequent machining). Parts of this type (such as aircraft skin parts, wind turbine blades, etc.) are easily deformed due to their large scale and low stiffness. Therefore, these parts cannot be directly machined according to the designed model. A feasible method is to plan a robotic machining path by using the point clouds of parts after clamping from onsite measurement which contains inherent defects of measurement such as noise points and abrupt points. In this article, a novel method is proposed to plan and optimize a robotic machining path that meets the requirements of smoothness, dexterity, and stiffness based on the point cloud from onsite measurement. The dual nonuniform rational B-spline curves of the machining path points and tool axis points are generated at first. Next, an objective function of smoothness optimization is established to filter out the local mutation of the path by considering the constraints of both the deformation energy and the deviation. Then, the objective function of robot postures optimization is established to optimize dexterity and Cartesian stiffness of a robot during the machining process. To show the feasibility of the proposed method, simulation and experiments are carried out. It is proved that the proposed method can generate a smooth machining trajectory. The stability of joint rotation and the rigidity and dexterity of the robot are improved during the machining process.

Multirobot systems have shown great potential in dealing with complicated tasks that are impossible for a single robot to achieve. One essential problem encountered in cooperatively working of the multirobot systems is the unknown initial transformation relationships from hand to eye, base to base, and flange to tool. In this article, the problem of multicoordinates calibration for a dual-robot system is formulated to a matrix equation AXB = YCZ. A novel approach for simultaneously solving the unknowns in equation AXB = YCZ is proposed, which is composed of a closed form method based on the Kronecker product and an iterative method which converts the calculation of a nonlinear problem to an optimization problem of a strictly convex function. The closed form method is used to quickly obtain an initial estimation for the iterative method to improve the efficiency and accuracy of iteration. In addition, a series of conditions on the solvability of the problem are proposed to guide the operators to select appropriate robot attitudes during the calibration process. To show the feasibility and superiority of the proposed iterative method, two other calibration methods are chosen to be compared to the proposed method through simulation and practical experiments. The comparison results verify the superiority of the proposed method in accuracy, efficiency, and stability.

Free-form surface matching that aligns measured points with a design model is a common problem in manufacturing automation. In this paper, an iterative variance-minimization matching (VMM) method is proposed to address measured points that have measuring defects, such as uneven/open point distributions and measuring noise. The basic idea is that the objective function is defined as the variance of the closest distance from each measured point to the design model, and the measuring defects are considered by incorporating an average distance item into the objective function. Using the defined average distance item, a strategy for analyzing the effect of measuring defects on VMM and existing methods is presented. It is shown that the VMM method does not easily become trapped in a local optimum when measuring defects exist. To consider convergence speed and convergence stability, a new distance based on the first-order point-to-point distance and point-to-tangent distance is developed and used in the objective function. To demonstrate the availability of the proposed method, quadratic convergence and positive definiteness are theoretically analyzed. The proposed method is efficient and insensitive to measuring defects and is useful for shape matching tasks involving free-form surface features. Note to Practitioners-This paper is motivated by the problem of matching measured points with a design model to automate manufacturing processes such as geometric inspection, workpiece localization, and allowance distribution. Measured points are obtained by applying a scanning device where measuring defects usually appear. Existing matching methods suffer from the drawback that the measured points may incline toward dense data and become trapped in a local optimum, due to measuring defects. To address this practical issue, this paper proposes a new method called variance-minimization matching (VMM), in which the objective function is optimized to weaken the effect of measuring defects. By examining the differences between VMM and existing methods, it is found that VMM can achieve quadratic convergence speed. Most importantly, the method is insensitive to uneven/open point distributions. In summary: 1) this method allows us to improve the matching accuracy in the presence of measuring defects; 2) there is no need to obtain a high-quality scan of the entire workpiece, potentially reducing scanning difficulty and improving scanning efficiency; and 3) the requirement of uniform sampling for measured points is reduced.

In the first part of this paper, a free-form surface matching method called variance-minimization matching (VMM) was proposed to address uneven/open point distributions and measuring noise. The convergence property and sensitivity to measuring defects were theoretically studied. In the second part of this paper, a series of experiments are presented to verify the feasibility of the proposed method in free-form surface matching. The experiments are divided into four sets: a measuring defects experiment, a noise experiment, a convergence experiment, and an artificial experiment. In the first set of experiments, the existing methods are prone to becoming trapped in a local optimum affected by uneven/open point distributions, which shows that measured points incline toward dense areas. However, in VMM, there is little inclination regardless of the increase in the number of measuring defects. In the second set of experiments, sensitivity to varying noise is tested. The results show that VMM helps prevent unstable sliding in the presence of Gaussian noise. In the third set of experiments, we compare convergence speed and convergence stability under different initial positions. It is verified that VMM exhibits the quadratic convergence. Finally, a set of artificial experiments is implemented, revealing that the proposed method is appropriate for use in automated manufacturing processes such as geometric inspection and allowance distribution. Note to Practitioners-Measuring defects usually occur when using a scanning device to obtain the measured points of a workpiece. Weakening the effect of measuring defects on matching results is critical to promoting manufacturing automation. This paper proposes a new method called variance-minimization matching (VMM) that considers measuring defects. In the first part of this paper, the modeling and theoretical analysis of VMM were introduced. In the second part of this paper, simulated experiments are performed to verify the feasibility of VMM in addressing uneven/open point distributions, measuring noise, and large initial positions. Next, artificial experiments employing VMM in geometric inspection and allowance distribution are presented. The proposed method also applies to other automated manufacturing processes, such as workpiece localization, deformation analysis, and complex parts repair.

Range image registration is a popular problem in pattern recognition and computer vision, and it has a wide range of applications in real life. The objective of registration is to match two models as close as possible. In this area, the best known iterative closest point (ICP) method is sensitive to the initial position of two models and it is easy stuck in local minima. In recent years, heuristic algorithms have been used for registration with good ability for global searching. However, the features of models are ignored generally in the iterative evolution process, so the tailored methods are lack of versatility for different models registration. In this paper, normal angle histogram is added into the fruit fly optimization algorithm for registration. The searching step of each individual is relative to the initial position of two models. The versatility and effectiveness of proposed algorithm are illustrated by a series of experiments.

Visually-guided robot grinding is a novel and promising automation technique for blade manufacturing. One common problem encountered in robot grinding is hand-eye calibration, which establishes the pose relationship between the end effector (hand) and the scanning sensor (eye). This paper proposes a new calibration approach for robot belt grinding. The main contribution of this paper is its consideration of both joint parameter errors and pose parameter errors in a hand-eye calibration equation. The objective function of the hand-eye calibration is built and solved, from which 30 compensated values (corresponding to 24 joint parameters and six pose parameters) are easily calculated in a closed solution. The proposed approach is economic and simple because only a criterion sphere is used to calculate the calibration parameters, avoiding the need for an expensive and complicated tracking process using a laser tracker. The effectiveness of this method is verified using a calibration experiment and a blade grinding experiment. The code used in this approach is attached in the Appendix.

3D parts inspection can be conducted by comparing the ideal design geometry with the real measurement points. Since the design coordinate system is different from the measurement coordinate system, these measurement points should be registered to the design coordinate system first. In this research area, iterative closest point (ICP) is the best-known algorithm, however, in order to converge to the global minima, ICP needs the good initial parameter which is hard to get in the actual inspection process. In this research, a hybrid Cuckoo Search (CS) method is proposed to solve the registration problem and two different optimizing strategies based on CS are described. The proposed algorithm seems much superior to other algorithms in terms of accuracy and robustness. Experiment results show that the proposed algorithm is effective.