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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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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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On an IFAC online pilot survey for a first course on control 5

On an IFAC online pilot survey for a first course on control

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5th Experiment at International Conference, exp.at 2019

作者： Huba, Mikulas Anthony Rossiter, J. Zakova, Katarina Serbezov, Atanas Visioli, Antonio Institute of Automotive Mechatronics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Bratislava Slovakia Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Rose-Hulman Institute of Technology Terre Haute United States Department of Mechanical and Industrial Engineering University of Brescia Brescia Italy

ISBN: (纸本)9781728136370

关键词： engineering education

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Data-driven Algorithm for Scheduling with Total Tardiness

arXiv

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arXiv 2020年

作者： Bouška, Michal Novák, Antonín Sůcha, Přemysl Módos, István Hanzálek, Zdeněk Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Jugoslávských partyzánů 1580/3 Prague Czech Republic Czech Technical University in Prague Faculty of Electrical Engineering Department of Control Engineering Karlovo náměstí 13 Prague Czech Republic

In this paper, we investigate the use of deep learning for solving a classical N P-hard single machine scheduling problem where the criterion is to minimize the total tardiness. Instead of designing an end-to-end machine learning model, we utilize well known decomposition of the problem and we enhance it with a data-driven approach. We have designed a regressor containing a deep neural network that learns and predicts the criterion of a given set of jobs. The network acts as a polynomial-time estimator of the criterion that is used in a single-pass scheduling algorithm based on Lawler's decomposition theorem. Essentially, the regressor guides the algorithm to select the best position for each job. The experimental results show that our data-driven approach can efficiently generalize information from the training phase to significantly larger instances (up to 350 jobs) where it achieves an optimality gap of about 0.5%, which is four times less than the gap of the state-of-the-art NBR heuristic. Copyright © 2020, The Authors. All rights reserved.

关键词： Scheduling algorithms

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On idle energy consumption minimization in production: Industrial example and mathematical model

arXiv

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arXiv 2020年

作者： Benedikt, Ondřej Šůcha, Přemysl Hanzálek, Zdeněk Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Jugoslávských partyzánů 1580/3 Prague Czech Republic Czech Technical University in Prague Faculty of Electrical Engineering Department of Control Engineering Karlovo náměstí 13 Prague Czech Republic

This paper, inspired by a real production process of steel hardening, investigates a scheduling problem to minimize the idle energy consumption of machines. The energy minimization is achieved by switching a machine to some power-saving mode when it is idle. For the steel hardening process, the mode of the machine (i.e., furnace) can be associated with its inner temperature. Contrary to the recent methods, which consider only a small number of machine modes, the temperature in the furnace can be changed continuously, and so an infinite number of the power-saving modes must be considered to achieve the highest possible savings. To model the machine modes efficiently, we use the concept of the energy function, which was originally introduced in the domain of embedded systems but has yet to take roots in the domain of production research. The energy function is illustrated with several application examples from the literature. Afterward, it is integrated into a mathematical model of a scheduling problem with parallel identical machines and jobs characterized by release times, deadlines, and processing times. Numerical experiments show that the proposed model outperforms a reference model adapted from the literature. Copyright © 2020, The Authors. All rights reserved.

关键词： Scheduling

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Cooperative Pursuit with Multi-Pursuer and One Faster Free-moving Evader

arXiv

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arXiv 2020年

作者： Fang, Xu Wang, Chen Xie, Lihua Chen, Jie School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Robotics Institute Carnegie Mellon University PittsburghPA15213 United States School of Electronics and Information Engineering Tongji University Shanghai200092 China Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing100081 China

—This paper addresses a multi-pursuer single-evader pursuit-evasion game where the free-moving evader moves faster than the pursuers. Most of the existing works impose constraints on the faster evader such as limited moving area and moving direction. When the faster evader is allowed to move freely without any constraint, the main issues are how to form an encirclement to trap the evader into the capture domain, how to balance between forming an encirclement and approaching the faster evader, and what conditions make the capture possible. In this paper, a distributed pursuit algorithm is proposed to enable pursuers to form an encirclement and approach the faster evader. An algorithm that balances between forming an encirclement and approaching the faster evader is proposed. Moreover, sufficient capture conditions are derived based on the initial spatial distribution and the speed ratios of the pursuers and the evader. Simulation and experimental results on ground robots validate the effectiveness and practicability of the proposed method. Copyright © 2020, The Authors. All rights reserved.

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Deep Learning with Attention Mechanism for Predicting Driver Intention at Intersection

Deep Learning with Attention Mechanism for Predicting Driver...

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IEEE Symposium on Intelligent Vehicle

作者： Abenezer Girma Seifemichael Amsalu Abrham Workineh Mubbashar Khan Abdollah Homaifar Faculty of Electrical and Computer Engineering Autonomous Control of Information Technology Institute North Carolina A&T State University Greensboro NC USA Intel Corporation Portland Oregon USA USAA San Antonio Texas USA

ISBN: (数字)9781728166735

ISBN: (纸本)9781728166742

In this paper, a driver's intention prediction near a road intersection is proposed. Our approach uses a deep bidirectional Long Short-Term Memory (LSTM) with an attention mechanism model based on a hybrid-state system (HSS) framework. As intersection is considered to be as one of the major source of road accidents, predicting a driver's intention at an intersection is very crucial. Our method uses a sequence to sequence modeling with an attention mechanism to effectively exploit temporal information out of the time-series vehicular data including velocity and yaw-rate. The model then predicts ahead of time whether the target vehicle/driver will go straight, stop, or take right or left turn. The performance of the proposed approach is evaluated on a naturalistic driving dataset and results show that our method achieves high accuracy as well as outperforms other methods. The proposed solution is promising to be applied in advanced driver assistance systems (ADAS) and as part of active safety system of autonomous vehicles.

关键词： Vehicles Hidden Markov models Data models Roads Predictive models Automobiles Vehicle dynamics

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EEG-Based Odor Recognition Using Channel-Frequency Convolutional Neural Network

EEG-Based Odor Recognition Using Channel-Frequency Convoluti...

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第三十八届中国控制会议

作者： Xiaonei Zhang Huirang Hou Qinghao Meng Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University

Analyzing the response of the human brain to odors is critical to assess the function of olfactory and cognition. In this paper, an EEG(electroencephalograph)-based odor perception dataset is collected from ten subjects using thirteen odors stimuli. Based on the developed dataset, we employ channel-frequency convolutional neural network(CFCNN), combined with differential entropy(DE) features from different channels and frequency bands, to classify five odors that were consistently considered pleasant by the ten subjects. Meanwhile, the k-nearest neighbor(k-NN), linear discriminant analysis(LDA), support vector machine(SVM) and back propagation neural network(BPNN) are used as competing methods. The experimental results show that CFCNN is superior to the classic baselines and yields the highest accuracy in distinguishing five pleasant odors. Furthermore, compared with other four frequency bands, the gamma band presents the best classification accuracy, proving the closed relation between the olfaction and gamma band activity of the brain.

关键词： EEG olfaction classification CFCNN differential entropy

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Simulation and Verification of Wing-In-Ground Aero-Olfactory Effect for the Gas-Sensitive Rotorcrafts

Simulation and Verification of Wing-In-Ground Aero-Olfactory...

引用

第三十八届中国控制会议

作者： Jia-Cheng Sun Tao Jing Qing-Hao Meng Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University

This paper proposes a method to simulate odor diffusion process under the wing-in-ground aerodynamic olfactory effect for gas-sensitive rotorcrafts and verifies its correctness by experiments. When an aircraft is flying close to the ground,the flow characteristics will be affected by the ground, the induced drag and wake volocity will be changed, which is called the wing-in-ground effect. The diffusion of odor plume under the wing-in-ground effect is different from the normal *** this paper, the wing-in-ground effect is simulated by the opposite flow of two mirrored wakes. This method converts the influence of ground on wake vortexes into that of mirrored wake vortexes. Inspired by the PIV(particle image velocimetry)principle, a simple and easy-to-operate device that can be realized in the offices is designed and built to verify the accuracy of the simulation. The Hu invariant moments are used to compare the envelope lines of odor-plumes images in the simulation and experiment, and the results are converted into a number indicating the similarity between them. The comparison results show that the simulation model of the wing-in-ground aerodynamic olfactory effect is basically the same as the experimental one, that is to say, the proposed model is a reliable method to simulate the odor diffusion process under the wing-in-ground effect of rotorcrafts.

关键词： wing-in-ground effect aerodynamic olfactory effect gas-sensitive rotorcrafts odor diffusion plume simulation

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An Automatic Detection and Recognition Method for Pointer-type Meters in Natural Gas Stations

An Automatic Detection and Recognition Method for Pointer-ty...

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第三十八届中国控制会议

作者： Yan Huang Xu-Yang Dai Qing-Hao Meng Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University

Currently, pointer-type meters in natural gas stations are read manually, which causes a waste of human *** have proposed lots of methods to realize automatic reading of such meters. However, these methods need high quality source image rigorously so that they cannot perform well in real environments. To solve this problem, an automatic detection and recognition method for pointer-type meters is proposed, which consists of R-FCNs(region-based fully convolutional networks),an improved local threshold segmentation method and a probability-circle method. The experimental results show that the method performs well when the source image is blurred, the illumination is complex or the camera's angle is tilted. It has good robustness and accuracy to detect and recognize the pointer-type meters automatically.

关键词： Pointer-type meter automatic reading R-FCNs threshold segmentation probability-circle method

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