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 TP...
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 ...
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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 controlsystems.
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 inter...
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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.
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...
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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.
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 s...
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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.
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 i...
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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.
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 li...
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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.
Accurate parameter estimation in ship dynamics models is pivotal for enhancing navigation precision, optimizing controlsystems, and ensuring maritime safety. This study explores two distinct methodologies: the “Grey...
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ISBN:
(数字)9798331511241
ISBN:
(纸本)9798331511258
Accurate parameter estimation in ship dynamics models is pivotal for enhancing navigation precision, optimizing controlsystems, 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.
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 e...
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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 classifi...
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