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International Conference on Methods and Models in Automation and robotics (MMAR)

作者： Bartłomiej Sulikowski Krzysztof Gałkowski Łukasz Hładowski Anton Kummert Joerg Velten Institute of Control and Computation Engineering University of Zielona Góra Zielona Góra Poland Institute of Automation Electronic and Electrical Engineering University of Zielona Góra Zielona Góra Poland Faculty of Electrical Information and Media Engineering University of Wuppertal Wuppertal Germany

In this paper spatially interconnected (ladder) systems have been discussed. First, the multidimensional, in that case, 2D - continuous in time and discrete in the spatial variable, has been introduced. Next, the temporal dynamics has been discretized, which has allowed to create the general, discrete model for the system dynamics. It has been assumed that system elements are the subject of uncertainty of the norm bounded form. Finally, robust stability testing and robust stabilizing controller design conditions have been proposed and its effectiveness is illustrated by an simulation example.

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Infradar-Localization: Single-Chip Infrared- and Radar-Based Monte Carlo Localization

Infradar-Localization: Single-Chip Infrared- and Radar-Based...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Dong Wang Marco Masannek Stefan May Andreas Nüchter Faculty of Electrical Engineering Precision Engineering Information Technology Nuremberg Institute of Technology Georg Simon Ohm Nuremberg Germany Department of Informatics XVII Robotics Julius-Maximilians-University Würzburg Würzburg Germany

This paper proposes a novel approach for indoor robot localization that leverages a fusion of information from single-chip infrared (Time-of-Flight) and radar sensors. The aim of our research is the development of a cost-effective and lightweight system that can achieve high-precision robot localization. Unlike traditional localization methods based on LiDARs or cameras, our proposed system uses single-chip infrared and radar sensors to overcome the limitations of high cost and bulky hardware. Specifically, we employ a Doppler radar-based velocity motion model for the estimation of the robot's ego-motion, eliminating the need for additional sensors such as IMU or wheel encoders. Next, we describe a hybrid sensor model for single-chip infrared and radar sensors that provides robust and accurate environmental perception with dynamic outlier removal. Finally, we integrate these components into a Monte Carlo localization framework to generate accurate real-time estimation of the robot's position and orientation. This is the first time a single-chip infrared and radar fusion-based framework has been applied to robot localization, to the best of our knowledge. Through a comprehensive experimental evaluation, we demonstrate the system's high accuracy and efficiency, achieving an average localization error of 9 cm in diverse indoor environments. This remarkable performance, combined with the low-cost and lightweight nature of our proposed solution, positions it as a highly promising alternative for a wide range of applications, including robotics, smart homes, and autonomous vehicles. The significant advancements of this novel approach offer vast potential to revolutionize the field of localization, enabling more precise and cost-effective navigation systems.

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Identification of PMSM Moment of Inertia based on Model Reference Adaptive Algorithm 4

Identification of PMSM Moment of Inertia based on Model Refe...

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4th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2022

作者： Ding, Fan Zhang, Tao Jia, Danping Zhang, Hualiang Pei, Quanshen Huang, Haoran School of Information Science and Engineering Shenyang University of Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences School of Electrical Engineering Dalian University of Technology Dalian China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

A method that can identify the rotational inertia in a timely manner is proposed because the performance of a permanent magnet synchronous motor servo system is easily affected by the rotational inertia. Firstly, principles of model-referenced adaptive recognition algorithm is introduced, and the influence of the adaptive gain on the identification results is analyzed. Then, design a suitable range of adaptive gain value, use the set error value to compare the identification error with the set value, and then select the appropriate gain for identification. Finally, through simulation verification, this method has fast identification convergence speed, high precision and strong anti-disturbance. © 2022 IEEE.

关键词： Adaptive algorithms

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Comparison of Prevailing Technological Trends in Communication and control Infrastructure 14

Comparison of Prevailing Technological Trends in Communicati...

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14th International Congress on Ultra Modern Telecommunications and control Systems and Workshops, ICUMT 2022

作者： Pitner, Tomas Oujezsky, Vaclav Mamula, Ondfei Kasl, Frantisek Strelec, Martin Vodrazka, Jiri Masaryk University Faculty of Informatics Brno Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Prague Czech Republic New Technologies for the Information Society University of West Bohemia Plzen Czech Republic Czech Technical University Faculty of Electrical Engineering Prague Czech Republic

ISBN: (数字)9798350398663

ISBN: (纸本)9798350398663

In this contribution, we aim to present results of our comparison study into prevailing technological trends in smart grid communication and control infrastructure. We define a set of criteria, by which we then assess a range of technologies and discuss insights that can be gained from such comparison, as well as its limitations. © 2022 IEEE.

关键词： Smart power grids

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OCTOPUS: Optimized Cross-border TeleOperated Medicine Pouring Using NextGen Seamless Communication Networks

OCTOPUS: Optimized Cross-border TeleOperated Medicine Pourin...

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IEEE International Conference on Communications (ICC)

作者： Edwin Babaians Praveen Gorla Serkut Ayvaşık Jan Plachý Zdenek Becvar Wolfgang Kellerer Eckehard Steinbach Chair of Media Technology School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence Technical University of Munich Germany Dept. of Telecommunication Engineering Faculty of Electrical Engineering Czech Technical University Prague Chair of Communication Networks School of Computation Information and Technology Technical University of Munich Germany

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Teleoperated robotic systems have become instrumental in advancing remote healthcare services, especially in tasks that require precision and expert oversight. The advent of cutting-edge telecommunication infrastructures, such as 5G, has amplified interest in these systems, although their full potential remains untapped. This study delves into the effectiveness of teleoperated robotic systems for medicine dispensing, comparing the performance of Wi-Fi and 5G networks in a transnational setup between two cities - Prague and Munich. We focus on the robot's ability to accurately dispense a predefined volume of a syrup-like substance, simulating a delicate healthcare operation, under the guidance of a distant operator. Our research examines the system's holistic performance in real-world implementation across diverse scenarios, encompassing varying network states and feedback methods. Two primary feedback scenarios are considered: one incorporating real-time video streaming and another offering explicit quantitative data on the dispensed volume. Using a blend of quantitative and qualitative methods, we aim to determine the influence of network type and feedback on task efficacy and user satisfaction. This study provides insights into the potential and hurdles of deploying teleoperated robotic systems in crucial healthcare contexts, guiding future advancements in this domain, especially in scenarios, where precision and dependability are crucial.

关键词： 5G mobile communication Urban areas Medical services Streaming media User experience Haptic interfaces Task analysis

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Dynamic Blockchain-Empowered Trustworthy End-Edge Collaborative Computing via Rotating Multi-Agent DRL

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IEEE Transactions on Wireless Communications 2025年

作者： Xu, Chi Zhang, Peifeng Yu, Haibin Li, Yonghui Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China University of Chinese Academy of Sciences Beijing China University of Sydney School of Electrical and Information Engineering Sydney Australia

Blockchain-empowered end-edge collaborative computing is a promising technology for enhancing the timeliness and trustworthiness of Industrial Internet of Things (IIoT). However, integrating task offloading with blockchain consensus inevitably escalates resource consumption across communication, computation, and energy domains. Thus, the joint optimization of task offloading, resource allocation and blockchain consensus is very important for IIoT. This paper studies a general end-edge collaborative computing scenario with multiple end devices and multiple edge servers. We first propose a novel dynamic blockchain (DBC) scheme by developing a dynamic leader election mechanism and designing a dynamic consensus waiting time window. Then, by fully considering the constraints of multi-task size and deadline, communication bandwidth, computing frequency, battery capacity, Byzantine fault tolerant and trustworthiness, we formulate the trustworthy processing efficiency (TPE) maximization problem with respect to end-edge task division, communication and computation resource allocation, leader election and consensus waiting window. To address this problem, we transform it into a Markov decision process and design a compound reward by fully considering the penalty for computing timeout and consensus failure. After that, we propose a rotating multi-agent deep reinforcement learning (R-MADRL) algorithm tailored to the proposed DBC scheme, where an entropy-based dual-critic DRL algorithm is proposed for rotating multi-agent training and decentralized execution. Extensive experiments validate the effectiveness and superiority of the proposed DBC with R-MADRL, where three benchmark DRL algorithms and three blockchain consensus schemes are compared. The results demonstrate that R-MADRL achieves stable convergence with more than 60.32% TPE reward than other algorithms while the task timeout ratio of DBC is reduced by more than 66.49% compared with other schemes. © 2025 institute of El

关键词： Markov processes

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Landé gJ factors of the even-parity electronic levels of the terbium atom

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Journal of Quantitative Spectroscopy and Radiative Transfer 2022年 288卷

作者： Suski, M. Chomski, M. Furmann, B. Ruczkowski, J. Stefańska, D. Institute of Materials Research and Quantum Engineering Faculty of Materials Engineering and Technical Physics Poznan University of Technology Piotrowo 3A Poznan60-965 Poland Institute of Robotics and Machine Intelligence Faculty of Control Robotics and Electrical Engineering Poznan University of Technology Piotrowo 3A Poznan60-965 Poland

In the present work Landé gJ factors for 17 even-parity electronic levels of the terbium atom are presented. The spectra of the Zeeman splitting of the hyperfine structure were recorded with the method of laser spectroscopy in a hollow cathode discharge lamp in a constant magnetic field, with laser induced fluorescence detection. The analysis of 47 spectral lines of terbium in the spectral range between 490 and 609 nm enabled the determination of the gJ factors for the presented even-parity levels for the first time. Moreover, verification of the gJ factor values for 17 odd-parity levels, previously reported by other authors from the analysis of Fourier transform spectra. © 2022 Elsevier Ltd

关键词： Atoms

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USD: Unsupervised Soft Contrastive Learning for Fault Detection in Multivariate Time Series

USD: Unsupervised Soft Contrastive Learning for Fault Detect...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hong Liu Xiuxiu Qiu Yiming Shi Zelin Zang School of Information and Electrical Engineering Hangzhou City University Hangzhou China Academy of Edge Intelligence Hangzhou City University Hangzhou City University China College of Information Engineering Zhejiang University of Technology Hangzhou China Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Centre for Artificial Intelligence and Robotics (CAIR) HKISI-CAS HongKong School of Engineering Westlake University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Unsupervised fault detection in multivariate time series is critical for maintaining the integrity and efficiency of complex systems, with current methodologies largely focusing on statistical and machine learning techniques. However, these approaches often rest on the assumption that data distributions conform to Gaussian models, overlooking the diversity of patterns that can manifest in both normal and abnormal states, thereby diminishing discriminative performance. Our innovation addresses this limitation by introducing a combination of data augmentation and soft contrastive learning, specifically designed to capture the multifaceted nature of state behaviors more accurately. The data augmentation process enriches the dataset with varied representations of normal states, while soft contrastive learning fine-tunes the model’s sensitivity to the subtle differences between normal and abnormal patterns, enabling it to recognize a broader spectrum of anomalies. This dual strategy significantly boosts the model’s ability to distinguish between normal and abnormal states, leading to a marked improvement in fault detection performance across multiple datasets and settings, thereby setting a new benchmark for unsupervised fault detection in complex systems.

关键词： Technological innovation Sensitivity Fault detection Time series analysis Contrastive learning Signal processing Data augmentation Data models Complex systems Speech processing

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Analysis of fast prototyping of microcontroller-based ML software for acoustic signal classification

Analysis of fast prototyping of microcontroller-based ML sof...

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Signal Processing Algorithms, Architectures, Arrangements and Applications (SPA)

作者： Filip Marciniak Wojciech Marciniak Tomasz Marciniak Faculty of Computing and Telecommunications Poznan University of Technology Poznań Poland DECYBEL Student Science Club Poznan University of Technology Poznań Poland Faculty of Automatic Control Robotics and Electrical Engineering Institute of Automatic Control and Robotics Division of Electronic Systems and Signal Processing Poznan University of Technology Poznań Poland

The paper analyzes the preparation of software for acoustic signal classification with machine learning techniques for microcontrollers. The design process was tested for three types of devices: Nordic Thingy:53, *** and Arduino Nano 33 BLE Sense Lite. The classifier training process was carried out using the Edge Impulse platform. Experimental studies were carried out for the process of classifying sound signals generated by the vacuum cleaner motor. The results of the training and the model test were presented for different configurations.

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A Parallel and Asynchronous Blob Shape Descriptor Implemented in CMOS Technology

A Parallel and Asynchronous Blob Shape Descriptor Implemente...

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International Conference on Microelectronics, MIEL

作者： P. Bogacki M. Długosz T. Talaśka R. Długosz Aptiv Services Poland Kraków Poland Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications AGH University of Science and Technology Kraków Poland Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan University of Technology Poznan Poland Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz University of Science and Technology Bydgoszcz Poland

The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in Advanced Driver Assistance Systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock.

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