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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Xiaobo Liu Fang Wan Sheng Ge Haokun Wang Haoran Sun Chaoyang Song Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen Guangdong China School of Design Southern University of Science and Technology Shenzhen Guangdong China Robotics & Autonomous Systems Thrust System Hub Hong Kong University of Science and Technology Hong Kong China Department of Computer Science The University of Hong Kong Hong Kong China

Benchmarking provides experimental evidence of the scientific baseline to enhance the progression of fundamental research, which is also applicable to robotics. In this paper, we propose a method to benchmark metrics of robotic manipulation, which addresses the spatial-temporal reasoning skills for robot learning with the jigsaw game. In particular, our approach exploits a simple set of jigsaw pieces by designing a structured protocol, which can be highly customizable according to a wide range of task specifications. Researchers can selectively adopt the proposed protocol to benchmark their research outputs, on a comparable scale in the functional, task, and system-level of details. The purpose is to provide a potential look-up table for learning-based robot manipulation, commonly available in other engineering disciplines, to facilitate the adoption of robotics through calculated, empirical, and systematic experimental evidence.

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Efficient Visual Perception of Human-Robot Walking Environments Using Semi-Supervised Learning

Efficient Visual Perception of Human-Robot Walking Environme...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Dmytro Kuzmenko Oleksii Tsepa Andrew Garrett Kurbis Alex Mihailidis Brokoslaw Laschowski Department of Mathematics National University of Kyiv-Mohyla Academy Kyiv Ukraine Department of Mathematical Methods of System Analysis Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Department of Computer Science University of Toronto Toronto Canada Department of Systems Design Engineering University of Waterloo Waterloo Canada Temerty Faculty of Medicine University of Toronto Toronto Canada Institute of Biomedical Engineering University of Toronto Toronto Canada Toronto Rehabilitation Institute Toronto Canada Department of Mechanical and Industrial Engineering Robotics Institute University of Toronto Toronto Canada

Convolutional neural networks trained using supervised learning can improve visual perception for human-robot walking. These advances have been possible due to largescale datasets like ExoNet and StairNet - the largest open-source image datasets of real-world walking environments. However, these datasets require vast amounts of manually annotated data, the development of which is time consuming and labor intensive. Here we present a novel semi-supervised learning system (ExoNet-SSL) that uses over 1.2 million unlabelled images from ExoNet to improve training efficiency. We developed a deep learning model based on mobile vision transformers and trained the model using semi-supervised learning for image classification. Compared to standard supervised learning (98.4%), our ExoNet-SSL system was able to maintain high prediction accuracy (98.8%) when tested on previously unseen environments, while requiring 35% fewer labelled images during training. These results show that semi-supervised learning can improve training efficiency by leveraging large amounts of unlabelled data and minimize the size requirements for manually annotated images. Future research $\text{will}$ focus on model deployment for onboard real-time inference and control of human-robot walking.

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Jigsaw-based Benchmarking for Learning Robotic Manipulation

arXiv

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arXiv 2023年

作者： Liu, Xiaobo Wan, Fang Ge, Sheng Wang, Haokun Sun, Haoran Song, Chaoyang Department of Mechanical and Energy Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China School of Design Southern University of Science and Technology Guangdong Shenzhen518055 China Robotics & Autonomous Systems Thrust System Hub Hong Kong University of Science and Technology Hong Kong Department of Computer Science The University of Hong Kong Hong Kong

关键词： Benchmarking

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A Machine Learning Scheme for Tool Wear Monitoring and Replacement in IoT-Enabled Smart Manufacturing 1st

A Machine Learning Scheme for Tool Wear Monitoring and Repla...

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1st International Conference on Innovative Product design and Intelligent Manufacturing System, ICIPDIMS 2019

作者： Patel, Zeel Bharatkumar Muthuswamy, Sreekumar Centre for AI IoT and Robotics Department of Mechanical Engineering Indian Institute of Information Technology Design and Manufacturing Vandalur-Kelambakkam Road Kancheepuram Chennai600127 India

ISBN: (纸本)9789811526954

Tool wear monitoring is an important task in a smart manufacturing industry. Detecting worn-out tools and replacing them in time can increase the efficiency significantly. Various sensors are being used in machine tools to integrate them into a smart manufacturing setup. Continuously decreasing the cost of the sensors is encouraging the use of low-cost indirect methods for the task. Using multiple sensors increases the precision of estimating tool health over the single sensor-based approach. Appropriate mathematical models relating tool wear parameters and sensors data can be used here, but machine learning models become more suitable in a large variety of applications over normal mathematical models. This paper proposes a methodology for multi-sensor-based indirect tool wear monitoring system and presents a comparison of accuracy among various machine learning models. Standard references are used to generate dummy training and testing data. Python is used to create and test the models. In the end, it has been found that Naïve Bayes and support vector machine algorithms are yielding up to 97% accuracy. This is the initial work in the development of an IoT enabled and fully automated manufacturing setup. © 2020, Springer Nature Singapore Pte Ltd.

关键词： AI and IoT Machine learning Scikit-learn Smart manufacturing Tool wear monitoring

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Residual Stress‐Driven Non‐Euclidean Morphing in Origami Structures

arXiv

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arXiv 2023年

作者： Liang, Zihe Chai, Sibo Ding, Qinyun Xiao, Kai Liu, Ke Ma, Jiayao Ju, Jaehyung UM‐SJTU Joint Institute Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Key Laboratory of Mechanism and Equipment Design of Ministry of Education Tianjin University Tianjin300350 China School of Mechanical Engineering Tianjin University Tianjin300350 China Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing100871 China

Non‐Euclidean surfaces are ubiquitous in numerous engineering fields, such as automotive, aerospace, and biomedical engineering domains. Morphing origami has numerous potential engineering applications, including soft robots, mechanical metamaterials, antennas, aerospace structures, and biomedical devices, owing to its intrinsic morphing features from two‐dimensional (2D) planes to three‐dimensional (3D) surfaces. However, the current one‐dimensional (1D) hinge deformation-driven transformation of foldable origami with rigid or slightly deformable panels cannot achieve a 3D complex and large curvilinear morphing. Moreover, most active origami structures use thin hinges with soft materials on their creases, thus resulting in a lower load capability. This study proposes a novel origami morphing method that demonstrates large free‐form surface morphing, e.g., Euclidean to non-Euclidean surface morphing with shape‐locking. We embedded tensorial anisotropic stress in origami panels during the extrusion‐based 3D printing of shape memory polymers. The extrusion‐based 3D printing of isotropic shape memory polymers can produce tensorial anisotropic stress in origami panels during fabrication, which can realize large non‐Euclidean surface morphing with multiple deformation modes. The connecting topology of the origami unit cells influences the global morphing behavior owing to the interaction of the deformation of adjacent panels. Non‐Euclidean morphing integrated with four‐dimensional (4D) printing can provide multimodal shape locking at material and structural levels. The non‐Euclidean surface morphing caused by tensorial residual stress in the panel during 3D printing expands the design space of origami and kirigami structures. Copyright © 2023, The Authors. All rights reserved.

关键词： Anisotropy

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

Towards Safe Robot Use with Edged or Pointed Objects: A Surr...

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IEEE International Conference on robotics and Automation (ICRA)

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

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

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.

关键词： Geometry Databases Collaboration Skin Collision avoidance Protection Injuries

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Integrated framework for optimizing the viscoelastic response of laminated composites under hygrothermal conditions

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engineering Structures 2025年 338卷

作者： Ly, Duy-Khuong Vu, Ho-Nam Topal, Umut Han, Jang-Woo Lee, Jaehun Nguyen, Sy-Ngoc Laboratory for Computational Mechanics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Civil Engineering School of Technology Van Lang University Ho Chi Minh City Viet Nam Department of Civil Engineering Faculty of Technology Karadeniz Technical University Trabzon Turkey Department of Mechanical Robotics and Energy Engineering Dongguk University Seoul04620 Korea Republic of Department of Mechanical Design Engineering Kumoh National Institute of Technology 39117 Korea Republic of Ho Chi Minh City Viet Nam Vietnam National University-Ho Chi Minh City Ho Chi Minh City Viet Nam

This study introduces an innovative computational framework aimed at optimizing the stacking sequences and thickness of laminated composite structures to achieve optimal viscoelastic responses under various hygrothermal conditions. It is worth noting that computational work for viscoelastic problems typically demands substantial effort and time due to repetitive numerical steps. When incorporating optimization into these problems, the computational requirements increase considerably, making traditional methods less suitable for practical engineering applications. Therefore, the present framework utilizes a smoothed finite element technique named smoothed cell element-discrete shear gap with fast convergence. To reduce the computational workload and avoid the recursive algorithm of viscoelasticity, the computational processes are performed in the transformed Laplace domain to linearize the stress–strain relationship in an integral form. Moreover, the framework is integrated with the balance-enhanced symbiotic organism search algorithm, a robust metaheuristic, to create an efficient optimization computational framework. Through systematic analysis, this research contributes to the advancement of composite material design by offering a practical and effective solution for addressing both viscoelastic behavior and hygrothermal considerations in thin laminated structures. © 2025 Elsevier Ltd

关键词： Delamination

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Effective Somatosensory and Cross-Modal Evaluation in Children with Hemiparesis and Neurotypically developing Children using MSI Caterpillar*

Effective Somatosensory and Cross-Modal Evaluation in Childr...

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IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Maria Casado-Palacios Kristina Müller Volker Hömberg Giorgia Bertonati Claudio Campus Marco Crcpaldi Antonio Maviglia Monica Gori Unit for Visually impaired People Center for Human Technologies Istituto Italiano di Tecnologia Genova Italy Neuropediatrics St. Mauritius Therapieklinik Meerbusch Dusseldorf Germany Word Federation for Neurorehabitation European Federation of Neurorehabilitation societies Department of Informatics Bioengineering Robotics and Systems Engineering UniGe Genova Italy Electronic Design Laboratory Istituto Italiano di Tecnologia Genova Italy

ISBN: (数字)9798350307993

ISBN: (纸本)9798350308006

Motor function and tactile perception are deeply interconnected. Individuals with paresis serve as an example of this intricate interplay between motor and tactile systems, by showing that motor recovery depends on improvement in the somatosensory function. In the clinical practice, somatosensory assessment tends to be neglected, or, when conducted, faces challenges due to a lack of consistency in methodology, which might also impact the development of effective rehabilitation strategies. Somatosensory and cross-modal perception in children with hemiparesis is particularly underexplored. With this work, using a new version of the MSI Caterpillar, our goal was (1) to contribute to the understanding of somatosensory perception in children with hemiparesis, (2) investigate how cross-modal inter-actions can influence their perceptions, and (3) bridge the gap between theoretical research and practical application, offering new pathways for technological integration in clinical settings. By measuring the reaction times (RTs) in the detection of a tactile signal and the localization of tactile, audio-tactile and visual-tactile stimuli, we effectively detect somatosensory impairments in the more affected arm of children with hemiparesis compared to the non-dominant one of typically developing peers, suggest the presence of cross-modal interactions, and highlight the dominant role of vision in space location. In addition, our data revealed that attraction towards the wrist in the localization of tactile stimuli is a developmental milestone impaired in hemiparesis.

关键词： Somatosensory Wrist Location awareness Visualization Pediatrics Tactile sensors Motors

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A New Approach for Solving the Inverse Kinematics of Continuum Robot Based on Piecewise Constant Curvature Model

Research Square

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Research Square 2021年

作者： Wu, Haoran Yu, Jingjun Pan, Jie Pei, Xu Robotics Institute Beihang University School of Mechanical Engineering and Automation Beijing100191 China Department of Mechanical Design Beihang University Beijing100191 China

The inverse kinematics of continuum robot is an important factor to guarantee the motion accuracy. How to construct a concise inverse kinematics model is very essential for the motion control of continuum robot. In this paper, a new method for solving the inverse kinematics of continuum robot is proposed based on the geometric and numerical method. Assumed that the deformation of the continuum robot is Piecewise Constant Curvature model (PCC), the envelope surface of the continuum robot based on single-segment is modeled and calculated. The clustering method is used to calculate the intersection of the curves. Then, a distinct sequence is designed for solving the inverse kinematics of continuum robot, and it is also suitable for the multi-segment continuum robots in space. Finally, the accuracy of the inverse kinematics algorithm is verified by the simulation and numerical experiment. The experiment results illustrate that this algorithm is with higher accuracy compared with the Jacobian iterative algorithm. © 2021, CC BY.

关键词： Inverse kinematics

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Deep Residual Learning Based Localization of Near-Field Sources in Unknown Spatially Colored Noise Fields

Deep Residual Learning Based Localization of Near-Field Sour...

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European Signal Processing Conference (EUSIPCO)

作者： Zhuoqian Jiang Jingmin Xin Weiliang Zuo Nanning Zheng Akira Sano Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China National Engineering Laboratory for Visual Information Processing and Applications Xi'an Jiaotong University Xi'an China Shunan Academy of Artificial Intelligence Zhejiang China Department of System Design Engineering Keio University Yokohama Japan

ISBN: (数字)9789082797091

ISBN: (纸本)9781665467995

In this paper, we explore the problem of near-field source localization in an unknown spatially colored noise environment using an end-to-end neural network which is based on deep residual learning. Specifically, the proposed approach uses the multi-dimensional information of the array covariance as input, and finally directly outputs the location information of the near-field sources through the regression structure. The architecture of deep neural network is well designed taking into account the trade-off between the expression ability and compu-tational complexity. In addition, benefiting from the method of generating training data that combines the degree of separation to traverse the spatial location, the proposed approach has a robust performance for different location parameter separation. The simulation results demonstrate that the proposed approach outperforms the existing model-driven methods under various conditions, especially for the adverse scenes with low SNRs, small number of snapshots, or correlated sources.

关键词： Location awareness Deep learning Simulation Neural networks Training data Signal processing algorithms Position measurement

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