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6th International Conference on robotics and Automation Engineering, ICRAE 2021

作者： Chengbo, Li Xiaoming, Wang Zixi, Huang Guangyao, Pang Xinyu, Han Yaguang, Lin Dalian Free Trade Zone No. 1 High School Dalian China Key Laboratory of Modern Teaching Technology Ministry of Education Xi'an China School of Computer Science Shaanxi Normal University Xi'an710119 China Shenzhen Middle School Shenzhen China Wuzhou University School of Data Science and Software Engineering Wuzhou China Singapore Polytechnic Advanced Robotics and Intelligent Control Centre Singapore

ISBN: (纸本)9781665406970

The application of robots to assist humans in earthquake rescue has always been an important topic that researchers are constantly studying. CoSpace Rescue is an abstract modeling and simulation of earthquake rescue and a simulation platform for studying robot rescue search algorithms. Its mathematical model is a multi-objective optimization problem. In this paper, we propose a search algorithm design framework for the CoSpace Rescue robot. Based On this framework and the idea of the heuristic search algorithm, we design and implement the 'Multi-objective Optimal Dynamic Adaptive On-Demand Search Algorithm'. The results of the experiment show that the algorithm can dynamically adapt to the changes in the environment and significantly improve the search efficiency of the rescue robot. © 2021 IEEE.

关键词： Earthquakes

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Non-cooperative Stochastic Target Encirclement by Anti-synchronization control via Range-only Measurement

Non-cooperative Stochastic Target Encirclement by Anti-synch...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Fen Liu Shenghai Yuan Wei Meng Rong Su Lihua Xie Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control School of Automation Guangdong University of Technology Guangzhou the Centre for Advanced Robotics Technology Innovation (CARTIN) School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

This paper investigates the stochastic moving target encirclement problem in a realistic setting. In contrast to typical assumptions in related works, the target in our work is non-cooperative and capable of escaping the circle containment by boosting its speed to maximum for a short duration. In extreme conditions, where GPS signals are not available, weight restrictions are present, and ground guidance is absent, the agents can rely solely on their onboard single-modality perception tools to measure the distances to the target. The distance measurement allows for creating a position estimator by providing a target position-dependent variable. Furthermore, the construction of the unique distributed anti-synchronization controller (DASC) can guarantee that the two agents track and encircle the target swiftly. The convergence of the estimator and controller is rigorously evaluated using the Lyapunov technique. A real-world UAV-based experiment is conducted to illustrate the performance of the proposed methodology in addition to a simulated Matlab numerical sample. Our video demonstration can be found in the URL https://***/EDVLvP-bk8M.

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Temporally-Adaptive Models for Efficient Video Understanding

arXiv

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arXiv 2023年

作者： Huang, Ziyuan Zhang, Shiwei Pan, Liang Qing, Zhiwu Zhang, Yingya Liu, Ziwei Ang, Marcelo H. Advanced Robotics Centre National University of Singapore Singapore DAMO Academy Alibaba Group China S-Lab National Technological University Singapore Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science China

Spatial convolutions are extensively used in numerous deep video models. It fundamentally assumes spatio-temporal invariance, i.e., using shared weights for every location in different frames. This work presents Temporally-Adaptive Convolutions (TAdaConv) for video understanding, which shows that adaptive weight calibration along the temporal dimension is an efficient way to facilitate modeling complex temporal dynamics in videos. Specifically, TAdaConv empowers spatial convolutions with temporal modeling abilities by calibrating the convolution weights for each frame according to its local and global temporal context. Compared to existing operations for temporal modeling, TAdaConv is more efficient as it operates over the convolution kernels instead of the features, whose dimension is an order of magnitude smaller than the spatial resolutions. Further, kernel calibration brings an increased model capacity. Based on this readily plug-in operation TAdaConv as well as its extension, i.e., TAdaConvV2, we construct TAdaBlocks to empower ConvNeXt and Vision Transformer to have strong temporal modeling capabilities. Empirical results show TAdaConvNeXtV2 and TAdaFormer perform competitively against state-of-the-art convolutional and Transformer-based models in various video understanding benchmarks. Our codes and models are released at: https://***/alibaba-mmai-research/TAdaConv. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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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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A Dynamic Adaptive Search Algorithm for CoSpace Rescue Robot

A Dynamic Adaptive Search Algorithm for CoSpace Rescue Robot

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International Conference on robotics and Automation Engineering (ICRAE)

作者： Li Chengbo Wang Xiaoming Huang Zixi Pang Guangyao Han Xinyu Lin Yaguang Dalian Free Trade Zone No. 1 High School Dalian China Key Laboratory of Modern Teaching Technology Ministry of Education Xi'an China School of Computer Science Shaanxi Normal University Xi'an China Shenzhen Middle School Shenzhen China School of Data Science and Software Engineering Wuzhou University Wuzhou China Advanced Robotics and Intelligent Control Centre Singapore Polytechnic Singapore

ISBN: (纸本)9781665406987

The application of robots to assist humans in earthquake rescue has always been an important topic that researchers are constantly studying. CoSpace Rescue is an abstract modeling and simulation of earthquake rescue and a simulation platform for studying robot rescue search algorithms. Its mathematical model is a multi-objective optimization problem. In this paper, we propose a search algorithm design framework for the CoSpace Rescue robot. Based On this framework and the idea of the heuristic search algorithm, we design and implement the “Multi-objective Optimal Dynamic Adaptive On-Demand Search Algorithm”. The results of the experiment show that the algorithm can dynamically adapt to the changes in the environment and significantly improve the search efficiency of the rescue robot.

关键词： Automation Heuristic algorithms Earthquakes Mathematical models Scheduling Optimization Rescue robots

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Novel Methods Inspired by Reinforcement Learning Actor-Critic Mechanism for Eye-in-Hand Calibration in robotics

Novel Methods Inspired by Reinforcement Learning Actor-Criti...

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IEEE International Conference on Development and Learning, ICDL

作者： Chenxing Li Yinlong Liu Yingbai Hu Fabian Schreier Jan Seyler Shahram Eivazi Department of Computer Science University of Tübingen Tübingen Germany Advanced Development Analytics and Control Festo SE and Co. KG Esslingen Germany State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC) University of Macau Macau China HUA Cloud Intelligent Healthcare Co. Ltd. Ningbo China Multi-Scale Medical Robotics Centre and the Chow Yuk Ho Technology Centre for Innovative Medicine The Chinese University of Hong Kong Hong Kong China

Eye-in-hand camera calibration is an essential step in robotic vision systems. Traditional methods perform the calibration via geometry computation methods where the calibration points are acquired by moving the robot arm while capturing images of a static object (often a checkerboard) of known geometry. Our work aims to explore deep reinforcement learning (DRL) techniques for solving common calibration optimization problems. Inspired by the actor-critic mechanism of DRL, we propose a novel approach to perform eye-in-hand camera calibration on the robot arm. Numerical and real-world experiments prove the feasibility of the proposed method as such here we show the potential of the reinforcement learning method to solve the camera calibration problem.

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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