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arXiv 2024年

作者： Kirschner, Robin Jeanne Yang, Jinyu Elshani, Edonis Micheler, Carina M. Leibbrand, Tobias Müller, Dirk Glowalla, Claudio Rajaei, Nader Burgkart, Rainer Haddadin, Sami Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich80992 Germany Department of Orthopaedics and Sports Orthopaedics Klinikum Rechts der Isar TUM School of Medicine Technical University of Munich Munich81675 Germany Institute for Machine Tools and Industrial Management TUM School of Engineering and Design Technical University of Munich Garching near Munich85748 Germany Endoprothetikzentrum der Maximalversorgung BG Hospital Murnau Murnau82418 Germany

Safety for physical human-robot interaction (pHRI) is a major concern for all application domains. While current standardization for industrial robot applications provide safety constraints that address the onset of pain in blunt impacts, these impact thresholds are difficult to use on edged or pointed impactors. The most severe injuries occur in constrained contact scenarios, where crushing is possible. Nevertheless, situations potentially resulting in constrained contact only occur in certain areas of a workspace and design or organisational approaches can be used to avoid them. What remains are risks to the human physical integrity caused by unconstrained accidental contacts, which are difficult to avoid while maintaining robot motion efficiency. Nevertheless, the probability and severity of injuries occurring with edged or pointed impacting objects in unconstrained collisions is hardly researched. In this paper, we propose an experimental setup and procedure using two pendulums modeling human hands and arms and robots to understand the injury potential of unconstrained collisions of human hands with edged objects. Pig feet are used as ex vivo surrogate samples - as these closely resemble the physiological characteristics of human hands - to create an initial injury database on the severity of injuries caused by unconstrained edged or pointed impacts. For the effective mass range of typical lightweight robots, the data obtained show low probabilities of injuries such as skin cuts or bone/tendon injuries in unconstrained collisions when the velocity is reduced to © 2024, CC BY.

关键词： Industrial robots

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AI-based soundscape analysis: Jointly identifying sound sources and predicting annoyance

arXiv

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arXiv 2023年

作者： Hou, Yuanbo Ren, Qiaoqiao Zhang, Huizhong Mitchell, Andrew Aletta, Francesco Kang, Jian Botteldooren, Dick Wireless Acoustics Environmental and Expert Systems Research Group Department of Information Technology Ghent University Gent9052 Belgium AI and Robotics Internet Technology and Data Science Lab Department of Electronics and Information Systems Interuniversity Microelectronics Centre Ghent University Gent9052 Belgium Institute for Environmental Design and Engineering The Bartlett University College London LondonWC1H 0NN United Kingdom

Soundscape studies typically attempt to capture the perception and understanding of sonic environments by surveying users. However, for long-term monitoring or assessing interventions, sound-signal-based approaches are required. To this end, most previous research focused on psycho-acoustic quantities or automatic sound recognition. Few attempts were made to include appraisal (e.g., in circumplex frameworks). This paper proposes an artificial intelligence (AI)-based dual-branch convolutional neural network with cross-attention-based fusion (DCNN-CaF) to analyze automatic soundscape characterization, including sound recognition and appraisal. Using the DeLTA dataset containing human-annotated sound source labels and perceived annoyance, the DCNN-CaF is proposed to perform sound source classification (SSC) and human-perceived annoyance rating prediction (ARP). Experimental findings indicate that (1) the proposed DCNN-CaF using loudness and Mel features outperforms the DCNN-CaF using only one of them. (2) The proposed DCNN-CaF with cross-attention fusion outperforms other typical AI-based models and soundscape-related traditional machine learning methods on the SSC and ARP tasks. (3) Correlation analysis reveals that the relationship between sound sources and annoyance is similar for humans and the proposed AI-based DCNN-CaF model. (4) Generalization tests show that the proposed model’s ARP in the presence of model-unknown sound sources is consistent with expert expectations and can explain previous findings from the literature on soundscape augmentation. VC 2023 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://***/licenses/by/4.0/).https://***/10.1121/10.0022408 (Received 22 May 2023;revised 6 October 2023;accepted 31 October 2023;published online 15 November 2023) Copyright © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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Advanced Centroid State Estimation and Instability Detection Framework Leveraging UKF and AdvSAE-LSTM

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IEEE Sensors Journal 2025年

作者： Mao, Han Zhu, Aibin Tu, Yao Song, Jiyuan Zheng, Chunli Li, Meng Xu, Peng Shi, Lei Liu, Yang Li, Xiao Guan, Zhenpeng Xi'an Jiaotong University Institute of Robotics and Intelligent Systems Shaanxi Key Laboratory of Intelligent Robots Xi'an710049 China Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi'an710049 China Xi'an Jiaotong University School of Energy and Power Engineering Xi'an710049 China Xi'an Jiaotong University School of Mechanical Engineering Xi'an710049 China Xi'an Jiaotong University Honghui Hospital Xi'an710000 China Xi'an Jiaotong University Department of Sports Xi'an710049 China Xi'an Jiaotong University Second Affiliated Hospital Department of General Surgery Xi'an710004 China Fourth Medical Center of PLA General Hospital Department of Rehabilitation Senior Department of Orthopedics Beijing100048 China Sports Medicine and Rehabilitation National Clinical Research Center for Orthopedics Beijing100048 China Peking University Shougang Hospital Orthopedics Department Beijing100144 China

With the growing aging population, fall detection has become a significant research focus in recent years. However, most existing studies have not effectively improved the accuracy of centroid state estimation in human instability, particularly in terms of multi-source information fusion. To address this, we propose a centroid state estimation method based on the Unscented Kalman Filter (UKF), integrating data from the five-link model and trunk IMU sensors, significantly enhancing both the accuracy and robustness of centroid state estimation. Experimental results demonstrate that the proposed method achieves estimation errors of 0.0099 m and 0.0226 m/s for centroid displacement and velocity in the x-direction, and 0.0006 m and 0.0099 m/s in the y-direction, indicating high accuracy. To overcome the reliance of traditional supervised learning methods on large amounts of labeled data for instability state estimation, we propose an unsupervised learning-based instability state estimation method, featuring the AdvSAE-LSTM model. Data from different sensors are preprocessed and independent training datasets are constructed, after which the feature maps from each sensor are concatenated to form a global feature map. Compared to baseline methods, the proposed approach significantly improves instability state detection performance. Specifically, when the SAS threshold is set to 0.02, both the False Positive Rate (FPR) and False Negative Rate (FNR) are 0%, with a detection delay of only 77.16 ms, achieving highly efficient and accurate estimation of human instability states. © 2025 IEEE.

关键词： Kalman filters

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BAN: A Universal Paradigm for Cross-Scene Classification Under Noisy Annotations From RGB and Hyperspectral Remote Sensing Images

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Chen, Wentang Wen, Yibin Zheng, Juepeng Huang, Jianxi Fu, Haohuan Sun Yat-sen University School of Artificial Intelligence Zhuhai519082 China Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province School of Mechanical Engineering Robotics Institute Hangzhou310027 China Southwest Jiaotong University Faculty of Geosciences and Engineering Chengdu610032 China China Agricultural University College of Land Science and Technology Beijing100107 China Ministry of Agriculture and Rural Affairs Key Laboratory of Remote Sensing for Agri-Hazards Beijing100125 China Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen518071 China Tsinghua University Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science Beijing100190 China National Supercomputer Center in Shenzhen Shenzhen518071 China

While domain adaptation (DA) methods have made significant strides in remote sensing community, most current works assume that the source domain labels are accurate. However, limited emphasis has been placed on the scenario where source data are mislabeled with noisy annotations, which is more common in real applications and referred to as noisy DA (NDA). This article formulates remote sensing cross-scene classification on NDA scenarios and proposes a novel network called bilateral adaptation network (BAN), which consists of two parts: 1) forward learning (FL), which utilizes a model learning from the noisy source domain and transfers knowledge to target domain;and 2) backward learning (BL), which utilizes a dual model to acquire knowledge from the target domain and transfer it to source domain. We conduct two parts alternately and adopt a symmetrical Kullback-Leibler (KL) loss to align predictions of the model and its dual model in the same domain. This interactive strategy is able to explore bilateral relationships between domains, implicitly reducing label noise in the source domain. In addition, BAN could serve as a universal paradigm to not only improve the existing NDA methods but also enhance recent DA approaches. Comprehensive evaluations on three publicly available RGB-band remote sensing datasets and two hyperspectral datasets validate the superior effectiveness of our proposed BAN. BAN improves the average accuracy by 6.70%-15.70% on RGB datasets and overall accuracy (OA) by 1.36%-3.14% on hyperspectral datasets with flip-20% noise compared to other state-of-the-art DA and NDA approaches. Promising results indicate the potential of our approach in tackling more general and practical problems with noisy source domain. © 2025 IEEE.

关键词： Contrastive Learning

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Localization state induced superconductivity, pseudogap and strange metal

arXiv

引用

arXiv 2021年

作者： Hung, Kuan-Ming Shieh, Tung-Ho Wu, Kun-Yuan Department of Electronics Engineering National Kaohsiung University of Science and Technology Kaohsiung807 Taiwan Department of Intelligent Robotics Engineering Kun-Shan University Tainan710 Taiwan Division of Intellectual Property & Design Support United Microelectronics Corporation Tainan710 Taiwan

The emergence of superconductivity in unconventional superconductors usually accompanies the normal-state phases of pseudogap, strange metal and Fermi liquid. It indicates these phases are strongly related to the superconducting state and should be dominated by yet unclear mechanism of superconductivity. Here, we propose a microscopic theory that, according to the coupling between localized and extended states, the pairing electrons which leap on and off a localized state result in superconducting instability. Two distinguishable pairing gaps are observed in these states, one dominates the superconducting temperature and another the temperature of a coherent pair confined at localized area. The pairing correlation between these states results in four phases. Their characteristics are manifested in universal Planckian-like resistivity and unusual carrier distribution, as observed in recent experiments. © 2021, CC BY-SA.

关键词： Fermi liquids

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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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Extended Hybrid Zero Dynamics for Bipedal Walking of the Knee-less Robot SLIDER

arXiv

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arXiv 2025年

作者： Zong, Rui Liang, Martin Fang, Yuntian Wang, Ke Chen, Xiaoshuai Chen, Wei Kormushev, Petar Robot Intelligence Lab Dyson School of Design Engineering Imperial College London United Kingdom Department of Electrical and Electronic Engineering Imperial College London United Kingdom Paddington Robotics London United Kingdom Leap AI Aberdeen United Kingdom

Knee-less bipedal robots like SLIDER have the advantage of ultra-lightweight legs and improved walking energy efficiency compared to traditional humanoid robots. In this paper, we firstly introduce an improved hardware design of the bipedal robot SLIDER with new line-feet and more optimized mass distribution which enables higher locomotion speeds. Secondly, we propose an extended Hybrid Zero Dynamics (eHZD) method, which can be applied to prismatic joint robots like SLIDER. The eHZD method is then used to generate a library of gaits with varying reference velocities in an offline way. Thirdly, a Guided Deep Reinforcement Learning (DRL) algorithm is proposed to use the pre-generated library to create walking control policies in real-time. This approach allows us to combine the advantages of both HZD (for generating stable gaits with a full-dynamics model) and DRL (for real-time adaptive gait generation). The experimental results show that this approach achieves 150% higher walking velocity than the previous MPC-based approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Anthropomorphic robots

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DECOMPOSITION OF INDUSTRIAL SYSTEMS FOR ENERGY EFFICIENCY OPTIMIZATION WITH OPTTOPO

arXiv

引用

arXiv 2022年

作者： Thiele, Gregor Johanni, Theresa Sommer, David Krüger, Jörg Department for Process Automation and Robotics Fraunhofer Institute for Production Systems and Design Technology IPK Germany Faculty of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany Weierstrass Institute for Applied Analysis and Stochastics Berlin Germany Institute for Machine Tools and Factory Management Technical University of Berlin Berlin Germany

The operation of industrial facilities is a broad field for optimization. Industrial plants are often a) composed of several components, b) linked using network technology, c) physically interconnected and d) complex regarding the effect of set-points and operating points in every entity. This leads to the possibility of overall optimization but also to a high complexity of the emerging optimization problems. The decomposition of complex systems allows the modeling of individual models which can be structured according to the physical topology. A method for energy performance indicators (EnPI) helps to formulate an optimization problem. The optimization algorithm OptTopo achieves efficient set-points by traversing a graph representation of the overall system. © 2022, CC BY.

关键词： Optimization

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Towards Safe Robot Use with Edged or Pointed Objects: A Surrogate Study Assembling a Human Hand Injury Protection Database

arXiv

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arXiv 2024年

作者： Kirschner, Robin Jeanne Micheler, Carina M. Zhou, Yangcan Siegner, Sebastian Hamad, Mazin Glowalla, Claudio Neumann, Jan Rajaei, Nader Burgkart, Rainer Haddadin, Sami Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich80992 Germany Department of Orthopaedics and Sports Orthopaedics Klinikum rechts der Isar TUM School of Medicine Technical University of Munich Munich81675 Germany Institute for Machine Tools and Industrial Management TUM School of Engineering and Design Technical University of Munich Garching near Munich85748 Germany Endoprothetikzentrum der Maximalversorgung BG Hospital Murnau Murnau82418 Germany Department of Diagnostic and Interventional Radiology Klinikum rechts der Isar TUM School of Medicine Technical University of Munich Munich81675 Germany

The use of pointed or edged tools or objects is one of the most challenging aspects of today’s application of physical human-robot interaction (pHRI). One reason for this is that the severity of harm caused by such edged or pointed impactors is less well studied than for blunt impactors. Consequently, the standards specify well-reasoned force and pressure thresholds for blunt impactors and advise avoiding any edges and corners in contacts. Nevertheless, pointed or edged impactor geometries cannot be completely ruled out in real pHRI applications. For example, to allow edged or pointed tools such as screwdrivers near human operators, the knowledge of injury severity needs to be extended so that robot integrators can perform well-reasoned, time-efficient risk assessments. In this paper, we provide the initial datasets on injury prevention for the human hand based on drop tests with surrogates for the human hand, namely pig claws and chicken drumsticks. We then demonstrate the ease and efficiency of robot use using the dataset for contact on two examples. Finally, our experiments provide a set of injuries that may also be expected for human subjects under certain robot mass-velocity constellations in collisions. To extend this work, testing on human samples and a collaborative effort from research institutes worldwide is needed to create a comprehensive human injury avoidance database for any pHRI scenario and thus for safe pHRI applications including edged and pointed geometries. © 2024, CC BY.

关键词： Risk assessment

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Fabrication of Translucent Graded Dental Crown Using Zirconia-Yttrium Multi-Slurry Tape Casting 3d Printer

SSRN

引用

SSRN 2024年

作者： Romario, Yulius Shan Bhat, Chinmai Ramezani, Maziar Pasang, Tim Chen, Zhangwei Jiang, Cho-Pei Graduate Institute of Manufacturing Technology National Taipei University of Technology 1 Sec. 3 Zhongxiao E. Rd. Taipei10608 Taiwan High-value Biomaterials Research and Commercialization Center National Taipei University of Technology 1 Sec. 3 Zhongxiao E. Rd. Taipei10608 Taiwan Department of Mechanical Engineering Auckland University of Technology Auckland1010 New Zealand Department of Engineering Design Manufacturing and Management Systems Western Michigan University KalamazooMI4900-5200 United States Additive Manufacturing Institute Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robotics College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China Department of Mechanical Engineering National Taipei University of Technology Taiwan 1 Sec. 3 Zhongxiao E. Rd. Taipei10608 Taiwan

This paper aims to fabricate functionally graded dental crown using a multi-slurry tape casting additive manufacturing technology. The different luminescence of the dental crown was obtained with different composition of zirconia and yttria. Zirconia with tunable mechanical properties and translucency are obtained by adding 3, 3.5, 4, 4.5, and 5 mol% of yttrium oxide to zirconia powder. After obtaining the printable slurry with maximum solid loading, the green bodies are prepared using the in-house built high speed multi-ceramic tape casting technology. They are later sintered with two-stage sintering method. After the successful fabrication, the mechanical properties and translucency of the specimens were evaluated with Vickers hardness, three-point bending and translucency parameter tests. Finally, an FGM tooth crown with five photocurable slurries is proposed to demonstrate the translucent gradient effect of sintered part. The solid loading of 80% zirconia and 20% resin delivered samples without any surface cracks. The shrinkage ratio analysis showed that the sintered sample dimension was reduced by 20%, 20%, and 23% along X, Y, and Z directions. The samples fabricated with 3% yttrium oxide to zirconia delivered excellent hardness (1687 HV) and flexural strength (650.6 MPa). However, the relative luminescence increased with increasing the yttrium oxide for 3 – 5 mol%. With the optimized process parameters, the proposed dental crown is fabricated and analyzed for their shrinkage ratio, mechanical, and translucency properties. The study proposes the potential of fabricating customized dental crown with gradient translucent appearance. © 2024, The Authors. All rights reserved.

关键词： Zirconia

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