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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： An, Haibo Liang, Wei Ding, Xiaojian Zhang, Yinlong Tan, Jindong State Key Laboratory of Robotics Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Shenyang Institute of Automation Guangzhou Chinese Academy of Sciences Guangzhou511548 China Fifth Electronic Institute of MIIT Guangzhou510610 China Department of Mechanical Aerospace and Biomedical Engineering University of Tennessee KonxvilleTN37996 United States

ISBN: (纸本)9781728163215

This paper presents a performance degradation analysis for rotate vector (RV) reducer using acoustic emission (AE) techniques. Unlike the AE signals from fixed gear reducer that are steady and have unchanged route, the AE signals from RV reducer have variant propagation paths and strengths. This work investigates the kinematics model using AE techniques to solve the inherent issues of RV reducer performance degradation analysis, such as signal variabilities and inappropriate thresholds. Firstly, the propagation path of AE signals within the crankshaft is developed. Secondly, the attenuation trend of AE signals is exponentially formulated based on the corresponding propagation curve. The proposed method has been evaluated on the developed RV reducer platform. AE signals were collected under various working conditions, i.e., four abrasion states with eight types of loads and five rotation speeds. The experimental analysis on typical parameters (e.g., signal energies) proves the effectiveness and accuracy of the developed method based on AE techniques. © 2019 IEEE.

关键词： Crankshafts

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Simultaneous state and parameter estimation: The role of sensitivity analysis

arXiv

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arXiv 2020年

作者： Liu, Jianbang Gnanasekar, Aristarchus Zhang, Yi Bo, Song Liu, Jinfeng Hu, Jingtao Zou, Tao Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institute for Robotics & Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Department of Chemical & Materials Engineering University of Alberta EdmontonABT6G 1H9 Canada Key Laboratory of Energy Thermal Conversion & Control Southeast University Nanjing210096 China School of Mechanical and Electrical Engineering Guangzhou University Guangzhou510006 China

State and parameter estimation is essential for process monitoring and control. Observability plays an important role in both state and parameter estimation. In simultaneous state and parameter estimation, the parameters are often augmented as extra states of the original system. When the augmented system is observable, various existing state estimation approaches may be used to estimate the states and parameters simultaneously. However, when the augmented system is not observable, how we should proceed to maximally extract the information contained in the measured outputs is not clear. This paper concerns about simultaneous state and parameter estimation when the augmented system is not fully observable. Specifically, we first show how sensitivity analysis is related to observability of a dynamical system, and then illustrate how it may be used to select variables for simultaneous estimation. We also propose a moving horizon state estimation (MHE) design that can use the variable selection results in a natural way. Extensive simulations are carried out to show the efficiency of the proposed approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Sensitivity analysis

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Industrial robot rotate vector reducer fault detection based on hidden markov models

Industrial robot rotate vector reducer fault detection based...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Zhang, Yinlong An, Haibo Ding, Xiaojian Liang, Wei Yuan, Mingze Ji, Chunyang Tan, Jindong Shenyang Institute of Automation Guangzhou Chinese Academy of Sciences Guangzhou511548 China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Fifth Electronic Instituteof MIIT Guangzhou510610 China Department of Mechanical Aerospace and Biomedical Engineering University of Tennessee KonxvilleTN37996 United States

ISBN: (纸本)9781728163215

Reliable fault detection of rotate vector (RV) reducer is of paramount importance for the long-term maintenance of high-precision industrial robots. This paper proposes a Hidden Markov Model (HMM) based RV reducer fault detection using Acoustic Emission (AE) measurements. Compared with the conventional faults from the common rotating machinery (such as bearings and gears), the fault from the RV reducer is more complicated and undetectable due to its inherent inline and two-stage meshing structure. To this end, this work modifies the HMM model by taking into account not only the current observations and previous states, but also the subsequent series of observations within the posteriori probability framework. Through this way, the random and unknown disturbance, which is common in the industrial scenarios, could be reduced. Besides, the HMM is also applied to separate the AE signal bulks within one cycle that has 39 subcycles, which is a critical step for AE signal pre-processings. The proposed method has been evaluated on our collected AE signal dataset from the RV reducer in the industrial robotic platform. The experimental results and analysis validate that the proposed HMM based RV Reducer fault detection model can reliably and accurately detect reducer faults. © 2019 IEEE.

关键词： Hidden Markov models

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Automatic Cell Assembly by Two-fingered Microhand

Automatic Cell Assembly by Two-fingered Microhand

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2019 IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Junnan Chen Xiaoming Liu Shengnan Dong Pengyun Li Xiaoqing Tang Dan Liu Masaru Kojima Qiang Huang Tatsuo Arai Intelligent Robotics Institute School of Mechatronical Engineering Key Laboratory of Biomimetic Robots and Systems Ministry of Education Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing CHINA Department of Systems Innovation Graduate school of Engineering Science Osaka University Toyonaka Osaka Japan Global Alliance Lab The University of Electro-Communications Tokyo Japan

We have successfully achieved manipulation and assembly of microbeads having the size of 100μm diameter by hemispherical end-effectors with high stability and accuracy. The motivation of achieving assembly of actual cells lies in the great significance of it in tissue regeneration and cell analysis. Firstly, the most difficult problem we need to solve is the releasing problem caused by adhesion force. The viscosity on cell surface is much larger than the microbeads which makes cell releasing challenging. Secondly, the cell can generate its deformation, then contact area with end-effector will change during grasping process. This may influence the adhesion force and also bring problem to releasing. Thirdly, cell is much smaller, around 15μm in diameter, so we need to fabricate smaller end-effector to achieve successful manipulation and ensure the stability in the meantime. In this paper, we realize the manipulation by decreasing the adhesion forces and apply vibration to release a cell stably. We found the appropriate scale size for the end-effector is around 10μm diameter. It can not only grasp a 15μm cell but also bring little interference to the environment. As a demonstration of the proposed manipulation method, the repeated experiments were conducted to explore the dependence of adhesion force on the grasping distance, which can be helpful in the improvement of successful rate. Finally, we achieved automatic cell assembly using Hela cells.

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A novel hierarchical control strategy for biped robot walking on uneven terrain

A novel hierarchical control strategy for biped robot walkin...

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IEEE-RAS International Conference on Humanoid Robots

作者： Chencheng Dong Xuechao Chen Zhangguo Yu Zelin Huang Qingqing Li Qinqin Zhou Qiang Huang School of Mechantronical Engineering Beijing Institute of Technology Beijing China Bionic robot mechanism perception and control laboratory Beijing China Beijing Advanced Innovation Center for Intelligent Robotics and Systems Beijing Institute of Technology Beijing China

ISBN: (数字)9781538676301

ISBN: (纸本)9781538676318

When position-controlled biped robot is blind walking on a uneven terrain at a high speed, huge foot contact impacts will be generated. However, traditional admitance control can't absorb the impact and stabilize the robot due to its slow response and Incompleteness. In this paper, we propose a control strategy including respectively designed swing leg control and support leg control with a new approach of control transition. For Swing leg control, double spring damping model is presented to optimize the admitance controller with faster response and better robustness, and a active foot height controller is also proposed to reduce the impact further. On the other hand, the control transition is accomplished by using a bionic fuzzy control. As a result, the foot contact impact can be reduced and the robot can blind walk fast on uneven terrain. Finally, the validity of the proposed strategy is confirmed by the simulation.

关键词： Legged locomotion Foot Trajectory Springs Adaptation models Robot kinematics

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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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Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age

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Nature Ecology and Evolution 2025年第6期9卷 1031-1042页

作者： Pringle, Stephen Dallimer, Martin Goddard, Mark A. Le Goff, Léni K. Hart, Emma Langdale, Simon J. Fisher, Jessica C. Abad, Sara-Adela Ancrenaz, Marc Angeoletto, Fabio Auat Cheein, Fernando Austen, Gail E. Bailey, Joseph J. Baldock, Katherine C. R. Banin, Lindsay F. Banks-Leite, Cristina Barau, Aliyu S. Bashyal, Reshu Bates, Adam J. Bicknell, Jake E. Bielby, Jon Bosilj, Petra Bush, Emma R. Butler, Simon J. Carpenter, Dan Clements, Christopher F. Cully, Antoine Davies, Kendi F. Deere, Nicolas J. Dodd, Michael Drinkwater, Rosie Driscoll, Don A. Dutilleux, Guillaume Dyrmann, Mads Edwards, David P. Farhadinia, Mohammad S. Faruk, Aisyah Field, Richard Fletcher, Robert J. Foster, Chris W. Fox, Richard Francksen, Richard M. Franco, Aldina M. A. Gainsbury, Alison M. Gardner, Charlie J. Giorgi, Ioanna Griffiths, Richard A. Hamaza, Salua Hanheide, Marc Hayward, Matt W. Hedblom, Marcus Helgason, Thorunn Heon, Sui P. Hughes, Kevin A. Hunt, Edmund R. Ingram, Daniel J. Jackson-Mills, George Jowett, Kelly Keitt, Timothy H. Kloepper, Laura N. Kramer-Schadt, Stephanie Labisko, Jim Labrosse, Frédéric Lawson, Jenna Lecomte, Nicolas de Lima, Ricardo F. Littlewood, Nick A. Marshall, Harry H. Masala, Giovanni L. Maskell, Lindsay C. Matechou, Eleni Mazzolai, Barbara McConnell, Alistair Melbourne, Brett A. Miriyev, Aslan Nana, Eric Djomo Ossola, Alessandro Papworth, Sarah Parr, Catherine L. Payo-Payo, Ana Perry, Gad Pettorelli, Nathalie Pillay, Rajeev Potts, Simon G. Prendergast-Miller, Miranda T. Qie, Lan Rolley-Parnell, Persie Rossiter, Stephen J. Rowcliffe, Marcus Rumble, Heather Sadler, Jon P. Sandom, Christopher J. Sanyal, Asiem Schrodt, Franziska Sethi, Sarab S. Shabrani, Adi Siddall, Robert Smith, Simón C. Snep, Robbert P. H. Soulsbury, Carl D. Stanley, Margaret C. Stephens, Philip A. Stephenson, P.J. Struebig, Matthew J. Studley, Matthew Svátek, Martin Tang, Gilbert Taylor, Nicholas K. Umbers, Kate D. L. Ward, Robert J. White, Patrick J. C. Whittingham, Mark J. Wich, Serge Williams, Christopher D. Yakubu, Ibrahim B. Yoh, Natalie Durrell Institute of Conservation and Ecology (DICE) School of Natural Sciences University of Kent Canterbury United Kingdom Centre for Environmental Policy Imperial College London London United Kingdom Department of Geography and Environmental Sciences Northumbria University Newcastle upon Tyne United Kingdom School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Synthotech Ltd Milner Court Hornbeam Square Harrogate United Kingdom Mechanical Engineering Department University College London London United Kingdom HUTAN SWD Kota Kinabalu Malaysia Programa de Pós-Graduação em Gestão e Tecnologia Ambiental da Universidade Federal de Rondonópolis Rondonópolis Brazil Department of Engineering Harper Adams University Newport United Kingdom Applied Ecology Research Group School of Life Sciences Anglia Ruskin University Cambridge United Kingdom Centre for Ecology and Hydrology Penicuik United Kingdom Department of Life Sciences Imperial College London Silwood Park Campus Ascot United Kingdom Department of Urban and Regional Planning Faculty of Earth and Environmental Sciences Bayero University Kano Nigeria Greenhood Nepal Kathmandu Nepal Animal Rural & Environmental Sciences Nottingham Trent University Nottinghamshire United Kingdom School of Biological and Environmental Sciences Liverpool John Moores University Liverpool United Kingdom Lincoln Centre for Autonomous Systems University of Lincoln Lincoln United Kingdom Royal Botanic Garden Edinburgh Edinburgh United Kingdom School of Biological Sciences University of East Anglia Norwich Research Park Norwich United Kingdom Digital Ecology Limited Bristol United Kingdom School of Biological Sciences University of Bristol Bristol United Kingdom Department of Computing Imperial College London London United Kingdom Department of Ecology and Evolutionary Biology University of Colorado Boulder CO United States Faculty of Science Technology Engineering

With biodiversity loss escalating globally, a step change is needed in our capacity to accurately monitor species populations across ecosystems. Robotic and autonomous systems (RAS) offer technological solutions that may substantially advance terrestrial biodiversity monitoring, but this potential is yet to be considered systematically. We used a modified Delphi technique to synthesize knowledge from 98 biodiversity experts and 31 RAS experts, who identified the major methodological barriers that currently hinder monitoring, and explored the opportunities and challenges that RAS offer in overcoming these barriers. Biodiversity experts identified four barrier categories: site access, species and individual identification, data handling and storage, and power and network availability. robotics experts highlighted technologies that could overcome these barriers and identified the developments needed to facilitate RAS-based autonomous biodiversity monitoring. Some existing RAS could be optimized relatively easily to survey species but would require development to be suitable for monitoring of more ‘difficult’ taxa and robust enough to work under uncontrolled conditions within ecosystems. Other nascent technologies (for instance, new sensors and biodegradable robots) need accelerated research. Overall, it was felt that RAS could lead to major progress in monitoring of terrestrial biodiversity by supplementing rather than supplanting existing methods. Transdisciplinarity needs to be fostered between biodiversity and RAS experts so that future ideas and technologies can be codeveloped effectively. © The Author(s) 2025.

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Data Cleaning Based on Multi-sensor Spatiotemporal Correlation 4th

Data Cleaning Based on Multi-sensor Spatiotemporal Correlati...

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4th International Conference on Machine Learning and intelligent Communications, MLICOM 2019

作者： Shao, Baozhu Song, Chunhe Wang, Zhongfeng Li, Zhexi Yu, Shimao Zeng, Peng Liaoning Electric Power Research Institute State Grid Liaoning Electric Power Co. Ltd. 110000 Shenyang China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China Shenyang Power Supply Company State Grid Liaoning Electric Power Co. Ltd. 110000 Shenyang China

ISBN: (纸本)9783030323875

Sensor-based condition monitoring systems are becoming an important part of modern industry. However, the data collected from sensor nodes are usually unreliable and inaccurate. It is very critical to clean the sensor data before using them to detect actual events occurred in the physical world. Popular data cleaning methods, such as moving average and stacked denoise autoencoder, cannot meet the requirements of accuracy, energy efficiency or computation limitation in many sensor related applications. In this paper, we propose a data cleaning method based on multi-sensor spatiotemporal correlation. Specifically, we find out and repair the abnormal data according to the correlation of sensor data in adjacent time and adjacent space. Real data based simulation shows the effectiveness of our proposed method. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Sensor nodes

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Micro Channel for Analyzing Mechanical Adaption of Cancer Cell

Micro Channel for Analyzing Mechanical Adaption of Cancer Ce...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Pengyun Li Xiaoming Liu Xiaoqing Tang Masaru Kojima Jian Huang Qiang Huang Tatsuo Arai Beijing Advanced Innovation Center for Intelligent Robots and Systems and the Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China Osaka University 1-3 Machikaneyama Toyonaka Osaka Japan Key Laboratory of Ministry of Education for Image Processing and Intelligent Control the School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728150734

ISBN: (纸本)9781728150741

It was reported that cancer cells could biologically respond and adapt to the confined environment during migration in extracellular matrix (ECM). But it is not clearly studied that how cancer cells respond and adapt to confined environment in mechanical way. Thus, in this study, we designed and fabricated a kind of one-layer micro channel to mimic confined environment for analyzing mechanical adaption of cell. This micro channel could apply sequential mechanical stresses on a number of cells with high throughput, and the time of cells passing through five rows of micro channels were used to evaluate their adaptability to the sequential and confined space. In experiments, each cell suffered 20 times of mechanical stresses after passing through 20 squeezing channels arranged in 5 rows. We observed the sequential squeezing processes under microscope and investigated the passing time using a high-speed camera. The preliminary results indicated that cancer cells could pass through and adapt to the confined environment faster than normal cells.

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Development of a Tetris playing robot controlled by KNR 15

Development of a Tetris playing robot controlled by KNR

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15th IEEE International Conference on Mechatronics and Automation, ICMA 2018

作者： Wang, Ming Zhang, Yanlu Dong, Huifang Li, Xu Yu, Junzhi Department of Information and Electrical Engineering University of Shandong Jianzhu University Jinan250100 China Department of Intelligent Robotics State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781538660720

Robot plays an increasingly important role in daily life. This paper presents a vision-based wheeled robot for playing Tetris. How to control the robot is elaborated at length in this paper. The robot adopted a KNR controller as its core controller. With a CCD sensor, it identified colored blocks, lines and the destination. All algorithms are programed with LabVIEW. The experiments and the contest validated the effectiveness of the proposed vision-based wheeled robot system. © 2018 IEEE.

关键词： controllers

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