This paper presents a robust nonlinear control strategy for a laser scanner’s actuator operating under stochastic mechanical disturbances. External perturbations are represented as bounded stochastic torque inputs, capturing the aggregate effect of structural imbalance, impacts, or system degradation. Experimental observations of vibration-induced angular fluctuations motivate this modeling choice, highlighting the need for control strategies tolerant to bounded stochastic disturbances resulting from unpredictable mechanical faults. A smooth hyperbolic-based control law is proposed to achieve robust velocity tracking under these uncertain conditions. Global asymptotic stability is formally established through Lyapunov analysis, and simulation results confirm that the proposed method effectively maintains convergence and bounded control effort in the presence of estimated disturbance torque. The formulation is suitable for fault-tolerant control of precision actuators where physical irregularities are difficult to isolate or diagnose in real time.

ABSTRACT The tibial‐calcaneal angle (TCA) is a crucial biomechanical parameter used to assess foot alignment and classify rearfoot positions such as varus, rectus and valgus. Advances in plantar pressure sensing and deep learning (DL) have improved the accuracy of foot posture assessment, yet current methods rarely integrate plantar pressure with TCA, limiting precision. This manuscript presents an automated system for rearfoot classification based on plantar pressure mapping. The proposed method combines a 32 32 force‐sensitive resistor (FSR) sensor array with a convolutional neural network (CNN) to improve classification accuracy, reducing dependence on manual and radiographic techniques. Plantar pressure data were collected from 100 participants, and the CNN was trained to classify four alignment categories: rectus–rectus, rectus–valgus, valgus–valgus, and valgus–rectus. In this notation, the first term denotes the left foot and the second the right. Rectus indicates neutral alignment, while valgus refers to outward heel deviation. The model achieved 90.00% accuracy, showing strong generalization across categories. The valgus–rectus class exhibited the highest recall, approximately 93.75% with 97.58% specificity, while valgus–valgus reached 88.57% recall. Overall, the proposed system demonstrates high accuracy and robust performance, representing a clinically viable alternative to conventional image‐based methods.

Autonomous mobile robots are essential in industry, logistics, and service applications, such as infrastructure inspection, where navigation in dark and narrow environments is one of the most challenging tasks. This project presents the design and development of a patented artificial vision system for a mobile robot, which combines a high-precision laser scanner and a stereo-vision system. The objective is to enhance the robot’s surface scanning capabilities in complex environments. The proposed methodology allows to determine the relative position between systems to integrate data of the laser scanner with stereo-vision cameras and inertial sensors , providing robust and accurate environmental perception that enables the characterization of ventilation ducts surface. Experimental results show a mean correspondence error of 7 . 4515 mm in absolute terms for the combined data.

To achieve successful autonomous navigation, a vision system that provides continuous and precise three-dimensional data of the system’s surroundings is always needed. An indispensable step in the acquisition of reliable data is the calibration of the system, preferably with a time-efficient and low-complexity approach. In this paper, a robust and efficient calibration method is proposed for the information fusion of a stereo vision system and a Technical Vision System. The proposed methodology achieves an error of xe= 6.9401 mm, ye= 8.0997 mm and ze= 15.1822 mm in 3.0202 seconds of processing time, as proven through experimental results.

This study presents a robust control strategy to mitigate the effects of friction and mechanical vibrations in a 1-DOF electromechanical actuator for laser scanning systems. Using hyperbolic tangent functions ensures bounded control signals, preventing actuator saturation. Real-time vibration measurements with MPU6050 modules were conducted to characterize the system’s mechanical behavior. The stability of the system is ana-lyzed using the direct Lyapunov method and Barbalat’s lemma, ensuring global asymptotic stability. Simulations demonstrated the control strategy’s effectiveness in improving stability and accuracy. Future work will focus on implementing this control in a physical prototype for validation.

non-contact 3D spatial coordinate measurement systems, based on optical laser scanning as technical vision systems (TVS) for signal processing, are essential methodologies for obtaining topographies in high-risk environments where human exploration is limited. However, these systems have limitations in resolution, particularly when addressing features such as surface texture and small curvatures at edges. Therefore, in this work, we propose the implementation of speckle pattern projection as a complementary innovative solution. Supported by the digital image correlation (DIC) methodology and the use of multivariate methods such as principal component analysis (PCA), we obtain results from different wall surfaces in a pipe prototype. Additionally, we analyze the behavior of the signal received by the 3D scanner sensor, which provides complementary information about the study surface. This demonstrates that the combination of speckle pattern projection and three-dimensional laser scanning is an additional tool for advanced detection of substance material during pipeline monitoring.

This work presents improvements to a patented 3D laser scanning system with a friction-compensated laser positioning mechanism. By employing a dynamic friction model, the system accurately identifies angular positioning errors through a combination of manual and automatic measurements with different control inputs. The study conducts experiments involving multiple friction cycles and SEM micrograph analysis to compare the microscopic irregularities of steel surfaces before and after friction-based positioning. Parameters of the friction system are derived or estimated from processed input-output test data. A nonlinear control is proposed to compensate for friction effects, resulting in improved positioning accuracy. Experimental implementation using an STM32 board demonstrates a remarkable reduction in the uncertainty of 3D coordinates measured by applying friction-compensated laser positioning. The findings highlight the effectiveness of the proposed friction compensation method, making the 3D laser scanning system more precise and reliable for diverse applications.

Laser Scanners used for Structural Health Monitoring applications such as Pipelines Structural Inspections normally needs Point Clouds from a large quantity of individual measurements that should be adjusted or post-processed to decrease overall point-cloud errors depending on scanner's characteristics. The posterior adjustment is commonly addressed by different mathematical methods or computational algorithms. According to application requirements methods such as machine learning, signal filtering, or RANSAC algorithms are used. This paper shows the application of an adapted/modify RANSAC algorithm especially suited for the pipeline inspection task. Aiming to increase the percentage of useful data per capture.

Stereo vision systems are well know depth estimation methods with a large number of applications such as automatic inspection, autonomous navigation, process control, etc. The functioning principle of these systems is the triangulation between the real-world surface point and its respective projections on the image planes of each camera. One of the key points in order to obtain accurate measurements on stereo vision systems are the calibration of extrinsic and intrinsic parameters. This is why the work of this paper focuses on a camera calibration method to correct the error generated by the lens distortion. The proposed method divides the image in quadrants and generates an equation for each quadrant to correct the error generated by the lens distortion. The performed experiment demonstrated an accuracy improvement using the calibration method compared to the measures taken without a calibration method.

The present work is dedicated to improvement of the energetic efficiency of the mobile robot performing its task of cross-terrain inspections. It was improved by optimization of charge/discharge dynamics in a pair "solar panel-battery". The irradiance intensity was enhanced by additional system design improvement, in order to extend the time of mission performance. The validity of proposed approach was proved in a computational experiment.

Friction is one of the significant factors to degrade the transient accuracy of positioning systems. In this respect, the present paper proposes implementing a nonlinear control to perform friction compensation to improve the positioning accuracy for a controlled DC motor of a laser positioning system with friction, which is assumed to be appropriately described using a dynamic friction model. The global asymptotic positioning stability proof was performed using Lyapunov’s direct method.

The paper presents a technique developed for the enhancement of a Particle Image Velocimetry (PIV) measurement process in a customized machine vision system. The PIV measurements are performed for an accurate “Velocity Profile” mathematical model validation of a Magnetohydrodynamic (MHD) stirrer prototype. Data mining and filtering have been applied to a raw measurement database from the customized machine vision system designed to evaluate the MHD stirrer prototype. Outlier's elimination and smoothing have been applied to raw data to approximate the PIV measurements output to the velocity profile mathematical model in order to increase the accuracy of a customized machine vision system for 2D velocity profile measurements. The accurate measurement of the 2D velocity profile is fundamental owing to the requirement of future enhancement of the customized machine vision system to construct the 3D velocity profile of the MHD stirrer prototype. The presented technique can be used for measurement and validation in the design of devices that require fluid manipulation for tasks such as pumping, networking, propelling, stirring, mixing, and even cooling without a need for mechanical components due to MHD's non-intrusive nature provides a solution to mechanical moving elements issues.

This paper proposes an Artificial Neural Network (ANN) to accurately predict the real angles obtained by a Triangulation Vision System. The performance of the ANN is compared with the K-Nearest Neighbors algorithm from previous publications. For the experimentation it was necessary to create a database to train and prove both methods in different coordinates on a determinate area through the dynamic triangulation method. Afterwards, the root mean square error is calculated to obtain the accuracy of each algorithm. Finally, several laser scanning measurements were taken at different distances to analyze the measurement dispersion of both algorithms.

Laser vision systems have demonstrated to be useful in applications for autonomous navigation, structural health monitoring, manufacturing, reverse engineering, among others. A variant of these systems are the dynamic triangulation systems, where, these vision systems consist in a positioning laser, a scanning aperture and a fixed distance between them. The positioning laser points the laser beam over the surface to scan and it is detected by the scanning aperture. The purpose of this paper is to present the principle of operation of this system, the disadvantages when taking measures at different distances, and the implementation of the k-nearest neighbor algorithm (kNN) to solve these disadvantages.

Previous research have shown the advantages of a novel Technical Vision System (TVS), developed at the Autonomous University of Baja California (UABC), which uses triangulation to determine spatial coordinates on any object under observation. Present paper proposes a new application for the TVS use on drones with remote laser scanner in agriculture to determine a vegetation index of scanned crops. Thereby the needed algorithm to position the laser ray in the TVS field-of-view are proposed, implemented using microcontroller and tested using various experimental factors. Experimentation results shows the advantages and disadvantages of every algorithm comparing the positioning errors after control.

A 3D spatial measurement system has been enhanced by computational intelligence. The measurement system is based in opto-electronic scanning instrumentation for industrial task, robot navigation, medical scanning, and structural health monitoring applications. This paper presents new research performed in the data processing of a vision sensing database. Multivariate outlier analysis has been implemented by Mahalanobis distance in order to improve Support Vector Machine (SVM) regression algorithm results. Measurement error regression data has been used for spatial 3D measurements error correction. Demonstrating that optical systems for measurements that present non-linear behavior could be positive impacted by computational intelligence.

Frequency domain sensors (FDS) are important elements in control, data acquisition and monitoring systems. Such sensors have some outstanding characteristics like output of quasi-digital signals, high sensitivity, high resolution, wide dynamic range, anti-interference capacity and good stability. A FDS converts a physical variable (measurand) into a frequency domain ouput. When the measurand changes, the output has a proportional frequency shift. In systems that use FDS, measuring the frequency shift is desirable. Accuracy of most frequency measurement techniques is limited by measurement time, and if more precision is required, longer times for measuring are needed. In this work, a novel approach using the rational approximations principle for measuring frequency shift in the output of a FDS is introduced. Also algorithms for simulating the mathematical model of frequency measurement process are proposed, and resolution of measurement is improved by analyzing the data obtained.

The presented paper is a follow up work and improvement of a previously published research of a 3D Vision System for mobile robot navigation application. Sensor fusion and redundancy are integrated to the system in order to increase the overall robustness as well as the accuracy of the system measurements by finding the energetic center of the laser signal used by the vision system. New experimental results are presented to demonstrate the increase of system accuracy.