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2008 American Control Conference (ACC), vol.3

作者： Tamara Petrovic Stjepan Bogdan LARICS-Laboratory for Advanced Robotics and Intelligent Control Systems Faculty of Electrical Engeneering and Computing University of Zagreb Croatia

This paper presents a method for extension of matrix model of manufacturing system in order to provide an efficient tool for analysis of systems with various dispatching sequences of shared resources. Proposed method is used for transformation of system matrices in linear max-plus model. Once the linear model is determined sequence feasibility can be checked. Furthermore, the method provides a straightforward procedure for production cycle calculation and resource utilisation. Efficiency of presented technique is demonstrated on a manufacturing system example at the end of the paper.

关键词： Manufacturing systems Dispatching Resource management Production Flexible manufacturing systems Algebra Laboratories Intelligent robots Intelligent control Computer aided manufacturing

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Fuzzy Arithmetic-based Stability Analysis and Design of Fuzzy Controller

Fuzzy Arithmetic-based Stability Analysis and Design of Fuzz...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： S. Bogdan Z. Kovacic I. Fazinic I. Draganjac Laboratory for Advanced Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

Initially introduced as a model-free control design method, in today practice fuzzy control is dominantly used as yet another nonlinear control technique based either on a linear or nonlinear model of a process. This paper addresses the stability assessment of a fuzzy logic control system based only on the partial knowledge of a controlled process. Lyapunov stability conditions are derived and analyzed by using fuzzy numbers and fuzzy arithmetic. The experimental results obtained for a non-stable second-order system confirmed that this approach could be successfully implemented. Some questions, addressed in the paper, remained open for further investigation.

关键词： Fuzzy control Stability analysis Control systems Fuzzy systems Lyapunov method Sliding mode control Fuzzy logic Fuzzy sets Nonlinear control systems Arithmetic

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Finite-time asynchronous dissipative filtering of conic-type nonlinear Markov jump systems

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Science China(Information Sciences) 2021年第5期64卷 151-162页

作者： Xiang ZHANG Shuping HE Vladimir STOJANOVIC Xiaoli LUAN Fei LIU Key Laboratory of Intelligent Computing and Signal Processing (Ministry of Education) School of Electrical Engineering and Automation Anhui University Department of Automatic Control Robotics and Fluid Technique Faculty of Mechanical and Civil EngineeringUniversity of Kragujevac Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of AutomationJiangnan University

In the present study, the finite-time asynchronous dissipative filter design problem for the Markov jump systems with conic-type nonlinearity is studied. The hidden Markov model can describe the asynchronism embodied in the system modes and the filter modes reasonably. Moreover, a suitable LyapunovKrasovskii function is utilized and linear matrix inequalities are applied to obtain adequate conditions. These techniques guarantee the finite-time boundedness and strict dissipativity of the filtering error dynamic system. Furthermore, the design problems of the passive filter and the H∞ filter are studied by adjusting the three parameters U, G and V. Finally, the filter gains and the optimal index α*are obtained and the correctness and feasibility of the designed approach are verified by a simulation example.

关键词： dissipative filtering hidden Markov model finite-time boundedness Markov jump systems conic-type nonlinearity

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Autonomous Exploration of Unknown 3D Environments Using a Frontier-Based Collector Strategy

Autonomous Exploration of Unknown 3D Environments Using a Fr...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Iván D. Changoluisa Caiza Ana Milas Marco A. Montes Grova Francisco Javier Pérez-Grau Tamara Petrovic University of Zagreb Faculty of Electrical Engineering and Computing LARICS Laboratory for Robotics and Intelligent Control Systems Zagreb Croatia Advanced Center for Aerospace Technologies (CATEC) Aerospace Technology Park of Andalusia Seville Spain

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Autonomous exploration using unmanned aerial vehicles (UAVs) is essential for various tasks such as building inspections, rescue operations, deliveries, and warehousing. However, there are two main limitations to previous approaches: they may not be able to provide a complete map of the environment and assume that the map built during exploration is accurate enough for safe navigation, which is usually not the case. To address these limitations, a novel exploration method is proposed that combines frontier-based exploration with a collector strategy that achieves global exploration and complete map creation. In each iteration, the collector strategy stores and validates frontiers detected during exploration and selects the next best frontier to navigate to. The collector strategy ensures global exploration by balancing the exploitation of a known map with the exploration of unknown areas. In addition, the online path replanning ensures safe navigation through the map created during motion. The performance of the proposed method is verified by exploring 3D simulation environments in comparison with the state-of-the-art methods. Finally, the proposed approach is validated in a real-world experiment.

关键词： Three-dimensional displays Video on demand Navigation Warehousing Buildings Inspection Web sites

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Deep Learning in Cancer Diagnostics 1

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丛书名： SpringerBriefs in Applied Sciences and Technology

1000年

作者： Mohd Hafiz Arzmi Anwar P. P. Abdul Majeed Rabiu Muazu Musa Mohd Azraai Mohd Razman Hong-Seng Gan Ismail Mohd Khairuddin Ahmad Fakhri Ab. Nasir

ISBN: (数字)9789811989377

ISBN: (纸本)9789811989360

关键词： Biological and Medical Physics, Biophysics Artificial Intelligence Cancer Research Computational Intelligence

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Active interactions between animals and technology: biohybrid approaches for animal behaviour research

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Animal Behaviour 2025年 224卷

作者： Papadopoulou, M. Ball, M. Bartashevich, P. Burns, A.L.J. Chiara, V. Clark, M.A. Costelloe, B.R. Fele, M. French, F. Hauert, S. Heinrich, M.K. Herbert-Read, J.E. Hoitt, J. Ioannou, C.C. Landgraf, T. Matchette, S.R. Polverino, G. Sankey, D.W.E. Scott, D.M. Sridhar, V.H. Strömbom, D. Trianni, V. Vo-Doan, T.T. King, A.J. Department of Biosciences Faculty of Science and Engineering Swansea University Swansea United Kingdom Department of Biology Lafayette College Easton PA United States Institute for Theoretical Biology Department of Biology Humboldt-Universität zu Berlin Berlin Germany Cluster of Excellence ‘Science of Intelligence’ Berlin Germany Department Fish Biology Fisheries and Aquaculture Leibniz Institute of Freshwater Ecology and Inland Fisheries Berlin Germany Faculty of Life Sciences Albrecht Daniel Thaer-Institute of Agricultural and Horticultural Sciences Humboldt-Universität zu Berlin Berlin Germany Museum and Institute of Zoology Polish Academy of Science Warszawa Poland School of Biological Sciences University of Bristol Bristol United Kingdom School of Natural Sciences Macquarie University Sydney Australia Centre for the Advanced Study of Collective Behaviour University of Konstanz Konstanz Germany Department of Biology University of Konstanz Konstanz Germany Department of Collective Behavior Max Planck Institute of Animal Behavior Konstanz Germany School of Computing and Digital Media London Metropolitan University London United Kingdom Bristol Robotics Laboratory University of Bristol Bristol United Kingdom IRIDIA Université Libre de Bruxelles Brussels Belgium Department of Zoology University of Cambridge Cambridge United Kingdom Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany Department of Ecological and Biological Sciences University of Tuscia Viterbo Italy School of Natural and Environmental Science Newcastle University Newcastle upon Tyne United Kingdom Centre for Ecology and Conservation Faculty of Environment Science and Economy University of Exeter Penryn Campus Cornwall United Kingdom School of Animal Rural and Environmental Sciences Nottingham Trent University Nottingham United Kingdom Department for the Ecology of Animal Societies Max Planck Institute of Animal Behavior Konstanz Germany Ins

Biohybrid approaches (where living and engineered components are combined) provide new opportunities for advancing animal behaviour research and its applications. This review article and accompanying special issue explores how different types of novel technologies can be used in the field of animal behaviour from three perspectives: (1) comprehension, (2) application and (3) integration. Under the perspective of ‘comprehension,’ we present examples of how technologies like virtual animals or robots can be used in experimental settings to interact with living animals in a standardized manner. Such interactions can advance our understanding of fundamental topics such as mate choice, social learning and collective behaviour. Under ‘application,’ we investigate the potential for technologies to monitor, react and interact with animals in a variety of scenarios. For example, we discuss how drones can be used to keep large herbivores away from valuable crops and robotic predators to deter invasive species. Under ‘integration,’ we discuss possibilities for the coexistence of engineered and biological systems, augmenting the capacity or resilience of either or both components. Integration can be physical, for example, livestock can have sensors sit in their inner body for temperature monitoring, or within the environment, where sensors or robots monitor and interact with animals, such as a short-term earthquake forecasting method. Based upon these three themes, we discuss and classify existing biohybrid animal behaviour research, including the four articles included in our special issue. We also consider the ethics of this emerging field, highlight the advantages and potential issues associated with using technologies to create biohybrid systems and emphasize how such technologies can support the advancement of animal behaviour research. © 2025 The Author(s)

关键词： behavioural control behavioural monitoring biomimetic robotics ethorobotics interspecies interaction sensory integration

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The Classification of Kidney Disease: A Feature-Based Transfer Learning Approach 1

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11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023

作者： Xu, Yifeng He, Haoyu Majeed, Anwar P. P. Abdul Musa, Rabiu Muazu Hu, Bintao Nasir, Ahmad Fakhri Ab. Yap, Eng Hwa School of Robotics Xi’an Jiaotong-Liverpool University Taicang Suzhou215400 China School of Artificial Intelligence and Advanced Computing Xi’an Jiaotong-Liverpool University Taicang Suzhou215400 China Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang26600 Malaysia Center for Fundamental and Continuing Education Department of Credited Co-Curriculum Universiti Malaysia Terengganu Kuala Nerus Terengganu Terengganu Malaysia Xi’an Jiaotong-Liverpool University Taicang Suzhou215400 China Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang26600 Malaysia School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia

ISBN: (数字)9783031706875

ISBN: (纸本)9783031706868

Kidney disease is widespread and poses serious health risks. Therefore, early detection of kidney diseases is crucial for providing patients with timely medical care, thus improving their chances of survival. Conventional means of diagnosing is laborious and at times biased. Hence, computer aided diagnosis (CAD) has been promoted to facilitate this issue. This study investigates the ability of different feature-based transfer learning pipelines in its ability to classify four different types of kidney diseases from histopathological images of the tissues. The InceptionV3, VGG16 and VGG19 pre-trained convolutional neural network models were used to extract the features from the images, and consequently the fully connected layers of the pre-trained models were swapped with an instance-based classifier, namely k-Nearest Neighbors (kNN). It was shown from the study that the InceptionV3 + kNN pipeline was able to discriminate the different classes significantly well on both the validation and test dataset. This suggest that the proposed technique is an efficient means for classifying kidney diseases and a promising CAD approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Towards Practical Multi-Robot Hybrid Tasks Allocation for Autonomous Cleaning

arXiv

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arXiv 2023年

作者： Wang, Yabin Hong, Xiaopeng Ma, Zhiheng Ma, Tiedong Qin, Baoxing Su, Zhou The school of Cyber Science and Engineering Xi’an Jiaotong University Xi’an China Faculty of Computing Harbin Institute of Technology Harbin China Shenzhen Institute of Advanced Technology Chinese Academy of Science Shenzhen China School of Automation Chongqing University Chongqing China Gaussian Robotics Shenzhen China

Task allocation plays a vital role in multi-robot autonomous cleaning systems, where multiple robots work together to clean a large area. However, most current studies mainly focus on deterministic, single-task allocation for cleaning robots, without considering hybrid tasks in uncertain working environments. Moreover, there is a lack of datasets and benchmarks for relevant research. In this paper, to address these problems, we formulate multi-robot hybrid-task allocation under the uncertain cleaning environment as a robust optimization problem. Firstly, we propose a novel robust mixed-integer linear programming model with practical constraints including the task order constraint for different tasks and the ability constraints of hybrid robots. Secondly, we establish a dataset of 100 instances made from floor plans, each of which has 2D manually-labeled images and a 3D model. Thirdly, we provide comprehensive results on the collected dataset using three traditional optimization approaches and a deep reinforcement learning-based solver. The evaluation results show that our solution meets the needs of multi-robot cleaning task allocation and the robust solver can protect the system from worst-case scenarios with little additional cost. The benchmark will be available at https: //***/iamwangyabin/Multi-robot-Cleaning-Task-Allocation. Copyright © 2023, The Authors. All rights reserved.

关键词： Cleaning

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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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Social Adaptation of Robots for Modulating Self-Organization in Animal Societies

Social Adaptation of Robots for Modulating Self-Organization...

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IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops, SASOW

作者： Payam Zahadat Michael Bodi Ziad Salem Frank Bonnet Marcelo Elias De Oliveira Francesco Mondada Karlo Griparic Tomislav Haus Stjepan Bogdan Rob Mills Pedro Mariano Luis Correia Olga Kernbach Serge Kernbach Thomas Schmickl Artificial Life Laboratory of the Department of Zoology Karl-Franzens University Graz Austria Computer Engineering Department Aleppo University Syrian Arab Republic Rbotic Systems Laboratory Swiss Federal Institute of Technology (EPFL) Lausanne Switzerland Ecole Polytechnique Federale de Lausanne Lausanne VD CH Faculty of Electrical Engineering and Computing Laboratory for Robotics and Intelligent Control Systems Zagreb Croatia Universidade de Lisboa Lisboa Lisboa PT Cybertronica Research Research Center of Advanced Robotics and Environmental Science Stuttgart Germany

The goal of the work presented here is to influence the overall behaviour of specific animal societies by integrating computational mechatronic devices (robots) into those societies. To do so, these devices should be accepted by the animals aspart of the society and/or as part of the collectively formed environment. For that, we have developed two sets of robotic hardware for integrating into societies of two different animals: zebra fish and young honeybees. We also developed mechanisms to provide feedback from the behaviours of societies for the controllers of the robotic system. Two different computational methods are then used as the controllers of the robots in simulation and successfully adapted by evolutionary algorithms to influence the simulated animals for desired behaviours. Together, these advances in mechatronic hardware, feedback mechanisms, and controller methodology are laying essential foundations to facilitate experiments on modulating self-organised behaviour in mixed animal -- robot societies.

关键词： Animals Heating Vibrations Robot sensing systems Hardware Biochemistry

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