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

作者： Piotr Kozierski Talar Sadalla Szymon Drgas Adam Dąbrowski Joanna Zietkiewicz Poznan University of Technology Pozuan Faculty of Computing Institute of Automation and Robotics Faculty of Electrical Engineering Institute of Control

The article presents studies on the automatic whispery speech recognition. In the performed research a new corpus with whispery speech has been used. The aim of studies presented in this paper was to check, how the vocabulary size and the language model order influence on the speech recognition quality. It has been concluded that even using recordings with 5,000 different words only it is possible to prepare large vocabulary continuous speech recognition (LVCSR) model. It has been also found that the third order of language model is the best choice. The difference between normal and whispery speech is negligible and is manifested only in higher word error rate index (about 1.5 times higher for whispery speech).

关键词： Speech Speech recognition Vocabulary Training Hidden Markov models Error analysis

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Toward improving tracking precision in motion capture systems

Toward improving tracking precision in motion capture system...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Marek Retinger Jacek Michalski Piotr Kozierski Wojciech Giernacki Institute of Robotics and Machine Intelligence Faculty of Control Robotics and Electrical Engineering Poznan University of Technology Poznan Poland

Motion capture systems are a must-have part of a modern research laboratory. The system has a wide area of applications such as movie, sport, medicine or experiments validations. The system provides accurate pose (both position and orientation) data and allows tracking several objects at the same time. The calibration process is a crucial part of using the system, which determines the further accuracy of tracking. This paper presents research struggling with this problem and describes a new approach to the camera placement process for the OptiTrack system, using the dedicated application programming interface and its features. The document discusses the general concept, shows the benefits of using it and confirms its usefulness. It helps to improve camera placement to ensure detection and reduction or elimination of "low tracking zones". The working tool is also presented.

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Neuro-Evolution of Continuous-Time Dynamic Process controllers

Mendel

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Mendel 2021年第2期27卷 7-11页

作者： Sekaj, Ivan Kenický, Ivan Zúbek, Filip Institute of Robotics and Cybernetics Faculty of Electrical Engineering and Information Technology Slovak University of Technology in Bratislava Bratislava Slovakia

Artificial neural networks are means which are, among several other approaches, effectively usable for modelling and control of non-linear dynamic systems. In case of modelling systems input and output signals are a-priori known, supervised learning methods can be used. But in case of controller design of dynamic systems the required (optimal) controller output is a-priori unknown, supervised learning cannot be used. In such case we only can define some criterion function, which represents the required control performance of the closed-loop system. We present a neuro-evolution design for control of a continuous-time controller of non-linear dynamic systems. The controller is represented by an MLP-type artificial neural network. The learning algorithm of the neural network is based on an evolutionary approach with genetic algorithm. An integral-type performance index representing control quality, which is based on closed-loop simulation, is minimised. The results are demonstrated on selected experiments with controller reference value changes as well as with noisy system outputs. © 2021, Brno University of Technology. All rights reserved.

关键词： Neural networks

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Comparison of camera-based and 3D LiDAR-based place recognition across weather conditions

Comparison of camera-based and 3D LiDAR-based place recognit...

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International Conference on control, Automation, robotics and Vision (ICARCV)

作者： Kamil Żywanowski Adam Banaszczyk Michał R. Nowicki Institute of Robotics and Machine Intelligence Faculty of Control Robotics and Electrical Engineering Poznan University of Technology Poznan Poland

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

Place recognition based on camera images provides excellent results on benchmarking datasets but might struggle in real-world adverse weather conditions like direct sun, rain, fog, or just darkness at night. In automotive applications, the sensory setups include 3D LiDARs that provide information complementary to cameras. The presented article focuses on the evaluation of camera-based, LiDAR-based, and joint camera-LiDAR-based place recognition. The processing for all data inputs is performed using a similar architecture of a neural network and is evaluated under varying weather conditions using the newly available USyd dataset. The experiments performed on the same trajectories in diverse weather conditions over 50 weeks prove that a 16-line 3D LiDAR can be used to supplement image-based place recognition to increase its performance. This proves that there is a need for more research into place recognition performed with multi-sensory setups.

关键词： Laser radar Three-dimensional displays Cameras Meteorology Training Pipelines Image recognition

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Survey: IRobot Create in the Educational and Research Process at Slovak University of Technology

Survey: IRobot Create in the Educational and Research Proces...

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2015 International Conference on Simulation, Modelling and Mathematical Statistics(SMMS 2015)

作者： Martin DEKAN Franti?ek DUCHO? Lubo? CHOVANEC Peter PáSZTó Michal T?LGYESSY Institute of Robotics and Cybernetics Faculty of Electrical Engineering and Information TechnologySlovak University of Technology

This paper deals with mobile robotic platform iRobot Create at Slovak University of Technology. In the introduction, the platform iRobot Create and its abilities are presented. The overview of existing applications which used Create as its base is provided. The main body of the article describes our own hardware and software modules created for this mobile robotic platform. In the conclusion, possibilities for further use and modifications of these modules are presented.

关键词： iRobot create Kinect Tele-control Design

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Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutions

arXiv

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arXiv 2025年

作者： Yao, Tianliang Lu, Bo Kowarschik, Markus Yuan, Yixuan Zhao, Hubin Ourselin, Sebastien Althoefer, Kaspar Ge, Junbo Qi, Peng Department of Control Science and Engineering College of Electronic and Information Engineering Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Department of Electronic Engineering Faculty of Engineering The Chinese University of Hong Kong 999077 Hong Kong Robotics and Microsystems Center School of Mechanical and Electrical Engineering Soochow University Suzhou215131 China Bavaria Forchheim91301 Germany CREATe Division of Surgery & Interventional Science UCL LondonHA7 4LP United Kingdom School of Biomedical Engineering & Imaging Sciences King’s College London LondonSE1 7EH United Kingdom Centre for Advanced Robotics @ Queen Mary School of Engineering and Materials Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Cardiology Zhongshan Hospital Fudan University Shanghai Institute of Cardiovascular Diseases Shanghai200032 China State Key Laboratory of Cardiovascular Diseases Medical Innovation Center Shanghai East Hospital School of Medicine Tongji University Shanghai200092 China

Endovascular procedures have revolutionized the treatment of vascular diseases thanks to minimally invasive solutions that significantly reduce patient recovery time and enhance clinical outcomes. However, the precision and dexterity required during these procedures poses considerable challenges for interventionists. Robotic systems have emerged offering transformative solutions, addressing issues such as operator fatigue, radiation exposure, and the inherent limitations of human precision. The integration of Embodied Intelligence (EI) into these systems signifies a paradigm shift, enabling robots to navigate complex vascular networks and adapt to dynamic physiological conditions. Data-driven approaches, advanced computer vision, medical image analysis, and machine learning techniques, are at the forefront of this evolution. These methods augment procedural intelligence by facilitating real-time vessel segmentation, device tracking, and anatomical landmark detection. Reinforcement learning and imitation learning further refine navigation strategies and replicate experts’ techniques. This review systematically examines the integration of EI principles into robotic technologies, in relation to endovascular procedures. We discuss recent advancements in intelligent perception and data-driven control, and their practical applications in robot-assisted endovascular procedures. By critically evaluating current limitations and emerging opportunities, this review establishes a framework for future developments, emphasizing the potential for greater autonomy and improved clinical outcomes. Emerging trends and specific areas of research, such as federated learning for medical data sharing, explainable AI for clinical decision support, and advanced human-robot collaboration paradigms, are also explored, offering insights into the future direction of this rapidly evolving field. © 2025, CC BY.

关键词： Medical robotics

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The Journey/DAO/TAO of Embodied Intelligence: From Large Models to Foundation Intelligence and Parallel Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1313-1316页

作者： Tianyu Shen Jinlin Sun Shihan Kong Yutong Wang Juanjuan Li Xuan Li Fei-Yue Wang College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China School of Electrical and Information Engineering Jiangsu UniversityZhenjiang 212013China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Peng Cheng Laboratory Shenzhen 518000China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China IEEE

THE tremendous impact of large models represented by ChatGPT[1]-[3]makes it necessary to con-sider the practical applications of such models[4].However,for an artificial intelligence(AI)to truly evolve,it needs to possess a physical“body”to transition from the virtual world to the real world and evolve through interaction with the real *** this context,“embodied intelligence”has sparked a new wave of research and technology,leading AI beyond the digital realm into a new paradigm that can actively act and perceive in a physical environment through tangible entities such as robots and automated devices[5].

关键词： Intelligence DAO TAO

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Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge

arXiv

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arXiv 2025年

作者： Imran, Muhammad Krebs, Jonathan R. Sivaraman, Vishal Balaji Zhang, Teng Kumar, Amarjeet Ueland, Walker R. Fassler, Michael J. Huang, Jinlong Sun, Xiao Wang, Lisheng Shi, Pengcheng Rokuss, Maximilian Baumgartner, Michael Kirchhof, Yannick Maier-Hein, Klaus H. Isensee, Fabian Liu, Shuolin Han, Bing Nguyen, Bong Thanh Shin, Dong-Jin Ji-Woo, Park Choi, Mathew Uhm, Kwang-Hyun Ko, Sung-Jea Lee, Chanwoong Chun, Jaehee Kim, Jin Sung Zhang, Minghui Zhang, Hanxiao You, Xin Gu, Yun Pan, Zhaohong Liu, Xuan Liang, Xiaokun Tiefenthaler, Markus Almar-Munoz, Enrique Schwab, Matthias Kotyushev, Mikhail Epifanov, Rostislav Wodzinski, Marek Müller, Henning Qayyum, Abdul Mazher, Moona Niederer, Steven A. Wang, Zhiwei Yang, Kaixiang Ren, Jintao Korreman, Stine Sofia Gao, Yuchong Zeng, Hongye Zheng, Haoyu Zheng, Rui Yue, Jinghua Zhou, Fugen Liu, Bo Cosman, Alexander Liang, Muxuan Zhao, Chang Upchurch, Gilbert R. Ma, Jun Zhou, Yuyin Cooper, Michol A. Shao, Wei Department of Medicine University of Florida GainesvilleFL32611 United States Department of Surgery University of Florida GainesvilleFL32611 United States Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Computer Information Science and Engineering University of Florida GainesvilleFL32611 United States Shanghai Jiao Tong University Shanghai China Electronic & Information Engineering School Harbin Institute of Technology [Shenzhen Shenzhen China Division of Medical Image Computing German Cancer Research Center [DKFZ Heidelberg Germany CO. LTD Beijing China MedAI Seoul Korea Republic of Korea University Seoul Korea Republic of Department of Radiation Oncology Yonsei Cancer Center Heavy Ion Therapy Research Institute Yonsei University College of Medicine Seoul Korea Republic of Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Medical University of Innsbruck Innsbruck Austria Novosibirsk State University Novosibirsk Russia AGH University of Krakow Krakow Poland Sierre Switzerland University of Geneva Geneva Switzerland The Sense Innovation and Research Center Sion Switzerland National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Hubei Wuhan China Aarhus University Department of Clinical Medicine Nordre Palle Juul-Jensens Blvd. 11 Aarhus8200 Denmark Shanghaitech University Pudong Shanghai China Image Processing Center Beihang University Beijing China Department of Computer Science and Engineering University of California Santa CruzCA95064 United States Department of Biostatistics University of Florida GainesvilleFL32611 United States University Health Network ON Canada Agronomy Department University of F

Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aortic dissections. However, existing methods reduce aortic segmentation to a binary problem, limiting their ability to measure diameters across different branches and zones. Furthermore, no open-source dataset is currently available to support the development of multi-class aortic segmentation methods. To address this gap, we organized the AortaSeg24 MICCAI Challenge, introducing the first dataset of 100 CTA volumes annotated for 23 clinically relevant aortic branches and zones. This dataset was designed to facilitate both model development and validation. The challenge attracted 121 teams worldwide, with participants leveraging state-of-the-art frameworks such as nnU-Net and exploring novel techniques, including cascaded models, data augmentation strategies, and custom loss functions. We evaluated the submitted algorithms using the Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD), highlighting the approaches adopted by the top five performing teams. This paper presents the challenge design, dataset details, evaluation metrics, and an in-depth analysis of the top-performing algorithms. The annotated dataset, evaluation code, and implementations of the leading methods are publicly available to support further research. All resources can be accessed at https://***. © 2025, CC BY-NC-SA.

关键词： Computerized tomography

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Knowledge presentation by the MNSM-based controller for swimming motion of a snake-like robot

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High Technology Letters 2018年第1期24卷 103-112页

作者：卢振利 Xie Yafei Xu Huigang Borovac Branislav Li Bin School of Electrical Engineering and Automation Changshu Institute of Technology Changshu 215500 P. R. China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110014 P. R. China School of Information and Electrical Engineering China University of Mining and Technology Xuzhou 221116 P. R. China Faculty of Technical Sciences University of Novi Sad Novi Sad 21000 Serbia

A MNSM( mirror neuron system mechanism)-based controller is developed to present the swimming rhythm of a snake-like robot in Cartesian space. From engineering viewpoint,the proposed controller is composed of a neuron for rhythm angle and two neurons for motion knowledge in XY plane. The given knowledge is a rhythm curve for swimming motion of a snake-like robot. Experimental results show that the proposed controller can present the knowledge of swimming rhythm,which represents the corresponding control law to drive the snake-like robot to swim with different speeds and turning motion. This work provides a novel method to present the knowledge for swimming motion of snake-like robots.

关键词： mirror neuron system mechanism (MNSM) swimming motion snake-like robot knowledge presentation

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Engine Speed control Using Gain Scheduling Method

Engine Speed Control Using Gain Scheduling Method

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International Conference on Cybernetics and Informatics

作者： Jakub Osusky Vojtech Vesely Institute of Automotive Mechatronics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Institute of Robotics and Cybernetics Faculty of Electrical Engineering and Information Technology Slovak University of Technology

ISBN: (纸本)9781509018352

this paper presents a gain scheduled controller design for SISO systems applied on laboratory engine. Presented frequency domain method is based on the M-delta structure of closed loop systems. The small gain theory is exploited to obtain the stability condition. The gain scheduling control is applied on laboratory engine with variable load and compared with classic robust control.

关键词： Gain scheduled control Robust control Small gain theorem Frequency domain

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