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13th International Conference on Modern Mechanical Engineering: Science and Education, MMESE 2024

作者： Ermakov, Sergey P. Pompeev, Kirill P. Faculty of Control Systems and Robotics ITMO University Saint Petersburg Russia

ISBN: (纸本)9783031820823

The paper discusses the development of a methodology for assessing the reliability of TPs introduced into production to ensure a given accuracy and, based on it, an algorithm for the accuracy assessment of existing TPs. They were tested using the example of factory technological processes, which confirmed the adequacy of the proposed methodology and the operability of the created algorithm, and also made it possible to identify the shortcomings of these technologies and errors made by the technologist, in particular, when developing the technology for manufacturing a prismatic part, for which appropriate recommendations were given. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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Application of Machine Learning Methods for Production Data Analysis

Application of Machine Learning Methods for Production Data ...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Igor Volkov Yuri Andreev Faculty of Control Systems and Robotics ITMO University Saint- Petersburg Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

Ensuring the quality of CNC-machined parts is a challenging task in modern manufacturing due to frequent shifts in production conditions and the high costs of traditional inspection methods. In this study, we propose a data-driven approach that integrates machine learning (ML) models with statistical acceptance sampling to dynamically optimize quality control processes. Production data from ERP, MES, and IIoT systems were analyzed with Gradient Boosting model. By continuously retraining the model on newly completed batches, the system adapts to evolving conditions, maintaining predictive accuracy. A key innovation of this work is the timely adjustment of sampling parameters (e.g., sample size, acceptance number) according to predicted defect levels. This adaptive strategy conserves inspection resources when defect risks are low and intensifies monitoring when risks are high. The results demonstrate a marked reduction in inspection costs and a robust capacity to detect defects promptly, aligning with industry goals of minimizing scrap and rework. Overall, this study validates the effectiveness of applying machine learning methods to production data analysis, laying a foundation for future enhancements in adaptive, proactive quality control systems.

关键词： Industries Adaptation models Technological innovation Data analysis Costs Process control Production Quality control Inspection Predictive models

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Brain over Brawn: Using a Stereo Camera to Detect, Track, and Intercept a Faster UAV by Reconstructing the Intruder's Trajectory

Field Robotics

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Field robotics 2025年 2卷 222-240页

作者： Antonella Barišić Frano Petric Stjepan Bogdan Laboratory for Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb Croatia

This paper presents our approach to intercepting a faster intruder UAV, inspired by the MBZIRC 2020 Challenge 1. By utilizing a priori knowledge of the shape of the intruder's trajectory, we can calculate an interception point. Target tracking is based on image processing by a YOLOv3 Tiny convolutional neural network, combined with depth calculation using a gimbal-mounted ZED Mini stereo camera. We use RGB and depth data from the camera, devising a noise-reducing histogram-filter to extract the target's 3D position. Obtained 3D measurements of target's position are used to calculate the position, orientation, and size of a figure-eight shaped trajectory, which we approximate using a Bernoulli lemniscate. Once the approximation is deemed sufficiently precise, as measured by the distance between observations and estimate, we calculate an interception point to position the interceptor UAV directly on the intruder's path. Our method, which we have significantly improved based on the experience gathered during the MBZIRC competition, has been validated in simulation and through field experiments. Our results confirm that we have developed an efficient, visual-perception module that can extract information describing the intruder UAV's motion with precision sufficient to support interception planning. In a majority of our simulated encounters, we can track and intercept a target that moves 30% faster than the interceptor. Corresponding tests in an unstructured environment yielded 9 out of 12 successful results.

关键词： Autonomous aerial vehicles Trajectory Target tracking Cameras Three-dimensional displays Robots Radar tracking Image reconstruction Estimation Detectors

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Autonomous, Mobile Manipulation in a Wall-Building Scenario: Team LARICS at MBZIRC 2020

Field Robotics

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Field robotics 2025年 2卷 201-221页

作者： Ivo Vatavuk Marsela Polic Ivan Hrabar Frano Petric Matko Orsag Stjepan Bogdan Laboratory for Robotics and Intelligent Control systems University of Zagreb Faculty of Electrical Engineering and Computing Unska 3 Zagreb Croatia

In this paper we present our hardware design and control approaches for a mobile manipulation platform used in Challenge 2 of the MBZIRC 2020 competition. In this challenge, a team of UAVs and a single UGV collaborate in an autonomous, wall-building scenario, motivated by construction automation and large-scale robotic 3D printing. The robots must be able, autonomously, to detect, manipulate, and transport bricks in an unstructured, outdoor environment. Our control approach is based on a state machine that dictates which controllers are active at each stage of the Challenge. In the first stage our UGV uses visual servoing and local controllers to approach the target object without considering its orientation. The second stage consists of detecting the object's global pose using OpenCV-based processing of RGB-D image and point-cloud data, and calculating an alignment goal within a global map. The map is built with Google Cartographer and is based on onboard LIDAR, IMU, and GPS data. Motion control in the second stage is realized using the ROS Move Base package with Time-Elastic Band trajectory optimization. Visual servo algorithms guide the vehicle in local object-approach movement and the arm in manipulating bricks. To ensure a stable grasp of the brick's magnetic patch, we developed a passively-compliant, electromagnetic gripper with tactile feedback. Our fully-autonomous UGV performed well in Challenge 2 and in post-competition evaluations of its brick pick-and-place algorithms.

关键词： Robots Robot kinematics Navigation Manipulators Cameras Buildings Grippers Automation Service robots Laser radar

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Graph-Based Decentralized Exploration and Semantic Inspection for Multi-Robot systems

Graph-Based Decentralized Exploration and Semantic Inspectio...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Nada E. Abbas Tamara Petrovic Faculty of Electrical Engineering and Computing LARICS Laboratory for Robotics and Intelligent Control Systems University of Zagreb Zagreb Croatia

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

This paper presents a decentralized graph-based exploration and inspection framework for Multi-Robot systems, designed to address challenges in subterranean and largescale environments. Unlike prior works that focus solely on exploration or inspection, this framework integrates volumetric exploration, semantic inspection, and dynamic task allocation into a unified decentralized system. A key novelty of this work is the seamless integration of these modules in a multi-robot setting, allowing UAVs to autonomously coordinate their tasks without relying on centralized control. The framework employs a hierarchical graph structure, utilizing a dense local graph for immediate navigation and a sparse global graph for long-term planning and repositioning. Extensive simulations in large-scale complex-shaped environments demonstrate that the proposed approach improves the completeness of the generated maps, reduces inconsistencies in the constructed mesh, and accelerates the overall exploration-inspection process compared to existing decentralized strategies.

关键词： Semantics Inspection Prediction algorithms Dynamic scheduling Aircraft navigation Robustness Planning Multi-robot systems Resource management Centralized control

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Cascade LTI System for Acoustic Emission Digital Signal Processing: Result Visualization via Scalogram for Defect Pattern Representation

Cascade LTI System for Acoustic Emission Digital Signal Proc...

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Digital Signal Processing and its Applications (DSPA), International Conference on

作者： Yeldos A. Altay Alexey V. Fedorov Department of Robotics and Technical Means of Automation Satbayev University Almaty Kazakhstan Faculty of Control Systems and Robotics ITMO University Saint-Petersburg Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

Modern acoustic emission (AE) diagnostic systems are sensitive instruments for detecting developing defects. A significant limitation of the AE method is the difficulty of isolating signals against the background of interference, which significantly reduces the signal-to-interference ratio. Therefore, in this article a cascade filtering scheme for visualizing acoustic signatures through a scalogram is proposed to isolate AE signals that characterize the process of defect formation. Acoustic portraits of the isolated signals characterizing the process of defect formation are given. It is shown that the amplitude of the AE signal from a defective structure during crack formation is characterized by the highest values of the signal amplitude and has a rich frequency-time structure, the signal shape of which is determined by discrete AE.

关键词： Linear systems Visualization Time-frequency analysis Filtering Shape Instruments Interference Digital signal processing Acoustic emission Signal to noise ratio

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Power Line Following Based on Measurements of the Magnetic Field

Power Line Following Based on Measurements of the Magnetic F...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Goran Vasiljević Dean Martinović Stjepan Bogdan Faculty of Electrical Engineering and Computing Laboratory for Robotics and Intelligent Control Systems Unska 3 University of Zagreb Zagreb Croatia

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

In this paper, we present a method to control the UAV to follow the power line with a specific position and orientation based only on the measurement of the magnetic field generated by the current flow in the power line. In this way, it is possible to localize the UAV with respect to the power line without the need for additional sensors, even in poor visibility conditions. The measurements from four magnetometers attached to the UAV are used to solve an optimization problem that involves determining the relative pose of the UAV with respect to the power line. Based on the relative pose, the UAV is controlled to follow the power line in a predefined position and orientation. Experiments in a test setup have confirmed that the method is applicable in a realistic environment.

关键词： Location awareness Visualization Power measurement Magnetic field measurement Magnetometers Current measurement Position measurement Autonomous aerial vehicles Magnetic fields Optimization

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Parameter Estimation of Autonomous Underwater Vehicles Dynamics. A Data-Driven Optimization Framework

Parameter Estimation of Autonomous Underwater Vehicles Dynam...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Radda Iureva Alexey Margun Egor Lykoyanov Faculty of Control Systems and Robotics ITMO Univresity Saint Petersburg Russia State Research Center of the Russian Federation Concern CSRI Elektropribor JSC ITMO University Saint Petersburg Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

Accurate parameter estimation in ship dynamics models is pivotal for enhancing navigation precision, optimizing control systems, and ensuring maritime safety. This study explores two distinct methodologies: the “Grey Box” approach, where the governing equations of ship dynamics are known but certain parameters remain unidentified, and the “Black Box” approach, which relies entirely on trajectory data without any prior knowledge of the underlying mathematical models. Leveraging machine learning techniques, specifically Long Short-Term Memory (LSTM) networks, alongside optimization algorithms like L-BFGS-B, this research aims to evaluate and compare the efficacy of both approaches in estimating model parameters and predicting ship trajectories. The findings demonstrate that while the Grey Box approach benefits from incorporating physical laws for parameter tuning, the Black Box method offers flexibility in modeling complex, nonlinear dynamics purely based on empirical data. The integrated use of these methodologies provides a robust framework for enhancing the accuracy and reliability of ship dynamics models, contributing significantly to maritime engineering applications.

关键词： Parameter estimation Accuracy Predictive models Prediction algorithms Mathematical models Trajectory Vehicle dynamics Marine vehicles Optimization Long short term memory

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Optimal control for Minimizing Inescapable Ellipsoids in Linear Periodically Time-Varying systems Under Bounded Disturbances

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IEEE control systems Letters 2025年 9卷 228-233页

作者： Dogadin, Egor Peregudin, Alexey ITMO University Faculty of Control Systems and Robotics St. Petersburg Russian Federation University of Sheffield School of Electrical and Electronic Engineering Sheffield United Kingdom

This letter addresses optimal controller design for periodic linear time-varying systems under unknown-but-bounded disturbances. We introduce differential Lyapunov-type equations to describe time-varying inescapable ellipsoids and define an integral-based measure of their size. To minimize this measure, we develop a differential Riccati equation-based approach that provides exact solutions for state-feedback, observer synthesis, and output-feedback control. A key component is a systematic procedure for determining the optimal time-varying parameter, reducing an infinite-dimensional optimization to a simple iterative process. A numerical example validates the method’s effectiveness. © 2017 IEEE.

关键词： Invariant ellipsoids optimal control optimal filtering output feedback time-varying systems

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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