This paper describes the calibration of a 2D vision system utilizing a high-precision robot that moves accurately along the z-axis. To achieve this, a one-axis high-precision robot was designed and manufactured, achieving a minimum travel distance of 0.007 μm during testing. The 2D vision system comprised an RGB camera, an LVDT sensor, and a laser sensor for distance measurement. Additionally, a subpixel-based algorithm was developed for the calibration and size measurement of the system. Following the calibration process with the high-precision robot, the size error was found to be within ±0.1 mm when using the LVDT sensor and ±0.2 mm with the laser sensor. Thus, the 2D vision system and calibration algorithm presented in this paper are deemed suitable for accurate object measurement by robots.

Accurate 3D human pose reconstruction from a single RGB image remains challenging due to scale ambiguity and perspective distortions. Current single-view methods primarily rely on learned priors or kinematic constraints, but they often struggle to maintain geometric consistency with the physical scene. This results in horizon alignment drift and instability when rendered in metric environments. To overcome these limitations, this study introduces a vanishing-point-driven framework that integrates scene geometry into the pose correction process. Under the Manhattan-world assumption, dominant vanishing points are detected to estimate the ground plane and recover the camera orientation with high precision. A lightweight 3D pose estimation network generates initial joint coordinates in camera-centric space. These coordinates are then refined through a VP-based ground-alignment transformation, which resolves scale ambiguity and minimizes geometric drift. The corrected poses are normalized to physical scale and streamed to NVIDIA Omniverse^TM for real-time digital-twin visualization. Experiments conducted on indoor scenes from the NYU Depth V2 dataset demonstrate sub-pixel accuracy in vanishing-point localization and significant improvements in geometric alignment between the reconstructed poses and the true scene layout. This confirms the effectiveness of the proposed approach for single-view digital-twin human modeling.

Accurate localization in industrial environments is challenging due to factors such as dust and reflections that degrade perception. To overcome these limitations, we propose an environment-independent localization method that relies solely on ultra-wideband (UWB) positioning. Our system employs LiDAR-SLAM in an offline stage to create a global map frame and calibrate the transformation between this frame and the UWB anchors. During operation, the robot estimates its position using a Kalman filter applied to UWB measurements transformed into the map frame. This paper presents a preliminary feasibility study conducted in an office-like environment to verify the core calibration and localization pipeline. The results show that the proposed method effectively aligns UWB positions with a pre-built SLAM map, achieving a 94% reduction in root-mean-square error (RMSE) compared to raw UWB measurements when validated against LiDAR-SLAM ground truth. This initial verification establishes the technical viability of the framework and lays the groundwork for future validation in harsh, large-scale industrial settings.

Belt-pulley looseness is a crucial factor in ensuring the safe operation of machinery used in industrial applications, such as compressors and fans. Traditionally, belt looseness has been inspected using contact-based current and vibration sensors. However, these methods are time-consuming and require manual attachment of the sensors. In order to overcome the limitations of these traditional methods, we propose a remote diagnosis method for detecting belt looseness using a smartphone. By utilizing a four-mirror system, the smartphone can construct a stereo system that enables 3D reconstruction of the object. This allows us to reconstruct the 3D trajectory of the belt and diagnose the level of looseness. To further enhance the accuracy of our proposed system, we have developed a calibration algorithm specifically designed for the four-mirror system. In our actual experiment, we successfully diagnosed four levels of belt looseness. As the level of looseness increased, we observed a curved shape in the 3D trajectory of the belt, along with noticeable quantitative differences. To quantitatively analyze these differences, we introduced a measure called the residual, which reflects the curvilinearity of the 3D trajectory. Our findings confirmed a significant correlation between the residual and the level of belt looseness.

Estimation and compensation of geometric errors for rotary axes are among methods to improve machining accuracy of five-axis machine tools. Studies have been conducted on various methodologies for estimating geometric errors for rotary axes, which are essential for improving machining accuracies of five-axis CNC machine tools. This paper presents a method for estimating geometric errors of a rotating/tilting table using a cross-shaped calibration artifact with a touch trigger probe. The proposed method includes rotary axes error estimation equations for angles of each rotary and tilt axis based on locations of probing points. Computer simulations were performed based on a MATLAB/Simulink and ADAMS cosimulation system using the probing cycle process to verify the proposed method. Computer simulation results confirmed the usefulness of the proposed method in terms of volumetric errors.

Industrial robot manipulators require high absolute position accuracy of the end effector to perform precise and complex tasks. However, manufacturing errors cause differences between nominal and actual parameters, and errors between the expected and actual positions of the end effector, resulting in undesired lower absolute position accuracy. Accordingly, to increase the absolute position accuracy of the end effector, kinematic calibration is required to correct the nominal parameters close to the actual parameters. However, in this study, redundancy of parameters may occur from the overlapping degrees of freedom of parameters in adjacent frames, which causes the problem of unnecessarily correcting many parameters in the optimization process. Thus, to solve this problem and use only the necessary parameters, this paper focuses on the linear relationship of redundant parameters and proposes a method of automatically discriminating and removing it through the Pearson Correlation Analysis. Additionally, through simulations on the two manipulator models, we verify the accuracy of redundancy of parameters determined by the proposed method, and demonstrate consistency and efficiency by comparing the results before and after redundancy removal.

As per ISO376 : 2011, creep uncertainty can be measured directly or indirectly. In this regard, this paper seeks to provide a comparison between direct and indirect creeps computed from hysteresis. All computations for direct and indirect creeps were done using equations from ISO376 : 2011. Five force measuring devices were experimentally examined for this purpose. Results showed that the behaviors of direct and indirect creeps were quite different. The relative creep that was directly measured was constant. On the other hand, the relative creep that was indirectly estimated varied with the applying force. Therefore, the directly measured creep cannot be replaced by the indirect one. This paper proposes a method to use a representative value for indirect creep, as the maximum of the creep. For the force measuring devices that had good hysteresis characteristics, the directly and indirectly measured creeps were comparable. However, for the force measuring devices with poor hysteresis characteristics, the indirectly estimated creep was much higher than the directly measured creep. Therefore, it is highly recommended to measure the creep directly for the force measuring devices characterized by poor hysteresis.

Three-axis magnetometers are widely used in various fields requiring azimuth information. However, accuracy of azimuth estimation based on magnetometer signals may be degraded because of errors such as offset, scale factor, nonorthogonality, hard-iron distortion, and soft-iron distortion. Recently, several ellipsoid-fitting calibration techniques have been proposed and have received much attention. However, comparative analysis of calibration accuracies between these techniques has not been conducted. This study compared and analyzed performance of four ellipsoid-fitting magnetometer calibration techniques such as the linear least square method, the two-step algorithm, and two different nonlinear least square methods. Our analysis and experimental results reveal superiority of the linear least square method compared to other methods in terms of calibration accuracy as well as ease of use in practice.

This paper presents the design and the control performance of a novel dynamic compliant-arm support with parallel elastic actuators that was developed to assist with the daily living activities of those whose arms are compromised by muscular disease or the aging process. The parallel elastic-arm support consists of a compliant mechanism with combined passive and active components for human interaction and to reach the user’s desired positions. The achievement of these tasks requires impedance control, which can change the virtual stiffness, damping coefficients, and equilibrium points of the system; however, the desired-position tracking by the impedance control is limited when the end-effector weight varies according to the equipping of diverse objects. A prompt algorithm regarding weight calibration and friction compensation is adopted to overcome this problem. A result comparison shows that, by accurately assessing the desired workspace, the proposed algorithm is more effective for the accomplishment of the desired activities.