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Short term after-effects of small force fields applied by an upper-limb exoskeleton on inter-joint coordination

Short term after-effects of small force fields applied by an...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Océane Dubois Agnès Roby-Brami Ross Parry Nathanaël Jarrassé Sorbonne Universités CNRS UMR 7222 INSERM the Institute of Intelligent Systems and Robotics (ISIR) Paris France LINP2 Université Paris-Nanterre Nanterre Cedex

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Exoskeleton technologies have numerous potential applications, ranging from improving human motor skills to aiding individuals in their daily activities. While exoskeletons are increasingly viewed, for example, as promising tools in industrial ergonomics, the effect of using them on human motor control, particularly on inter-joint coordination, remains relatively uncharted. This paper investigates the effects of generic low-amplitude force fields applied by an exoskeleton on motor strategies in asymptomatic users. The force fields mimic common perturbations encountered in exoskeletons, such as residual friction, over/under-tuned assistance, or structural elasticity. Fifty-five participants performed reaching tasks while connected to an arm exoskeleton, experiencing one of five tested force fields. Their movements before and after exposure to the exoskeleton force field were compared. The study focuses both on spatial and temporal changes in coordination using specific metrics. The results reveal that even brief exposure to a low- amplitude force field, or to uncompensated residual friction and dynamic forces, applied at the joint level can alter the interjoint coordination, while task performance remains unaffected. The tested force fields induced varying degrees of changes in joint contributions and synchronization. This study highlights the importance of monitoring coordination changes to fully understand the impact of exoskeletons on human motor control and thus enable safe and widespread adoption of those devices.

关键词： Performance evaluation Motor drives Friction Perturbation methods Exoskeletons Force Motors

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2022 63rd international Scientific conference on Information Technology and Management Science of Riga Technical University, ITMS 2022 - Proceedings

2022 63rd International Scientific Conference on Information...

引用

63rd international Scientific conference on Information Technology and Management Science of Riga Technical University, ITMS 2022

ISBN: (纸本)9798350399851

The proceedings contain 15 papers. The topics discussed include: classifier selection for an ensemble of network traffic analysis machine learning models;formalization and automated enactment of winter road maintenance regulatory requirements;taxonomy of risks in software development projects;application of multi-perspective modelling approach for building digital twin in smart agriculture;towards multidimensional infection risk monitoring;business process automation in retail;analysis of medical data processing technologies;ML models for winter road surface condition recognition in the case of insufficient coverage;big data-based solutions for sustainable digital services: evaluation of research methods;and evaluation of the performance of methods for classifying EEG signal processing.

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Variable Stiffness Multi-joint Mechanism: Design, Model and Validation

Variable Stiffness Multi-joint Mechanism: Design, Model and ...

引用

international conference on Control, Mechatronics and automation (ICCMA)

作者： Benedict Fletcher Romeo Glovnea Nicolas Herzig Engineering and Design University of Sussex Brighton United Kingdom Engineering and Design University of Sussex Falmer United Kingdom

ISBN: (数字)9798331517519

ISBN: (纸本)9798331517526

Compliant mechanisms have been used in several robotics applications, including locomotion and grasping. Variable stiffness joints are part of these compliant mechanisms and generally use one passive element per joint. This paper presents the design, model, and characterization of a Variable Stiffness Multi-joint Mechanism (VSMM) using a single carbon fibre rod as its passive element. The VSMM employs a variable lever mechanism, allowing the modulation of the joints’ stiffness. We present an analytical model of the mechanism, providing insights into its mechanical behaviour and enabling the prediction of stiffness variations under different design parameters. Through experimental testing, the mechanism demonstrates a stiffness range of 0.19N/ mm to 0.27N/ mm, offering variable compliance that can change to suit specific task requirements.

关键词： Legged locomotion Analytical models Manufacturing processes Mechatronics Modulation Stability analysis Carbon Probes Robots Testing

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Trust-Region Neural Moving Horizon Estimation for Robots

Trust-Region Neural Moving Horizon Estimation for Robots

引用

IEEE international conference on robotics and automation (ICRA)

作者： Bingheng Wang Xuyang Chen Lin Zhao Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Accurate disturbance estimation is essential for safe robot operations. The recently proposed neural moving horizon estimation (NeuroMHE), which uses a portable neural network to model the MHE’s weightings, has shown promise in further pushing the accuracy and efficiency boundary. Currently, NeuroMHE is trained through gradient descent, with its gradient computed recursively using a Kalman filter. This paper proposes a trust-region policy optimization method for training NeuroMHE. We achieve this by providing the second-order derivatives of MHE, referred to as the MHE Hessian. Remarkably, we show that many of the intermediate results used to obtain the gradient, especially the Kalman filter, can be efficiently reused to compute the MHE Hessian. This offers linear computational complexity with respect to the MHE horizon. As a case study, we evaluate the proposed trust region NeuroMHE on real quadrotor flight data for disturbance estimation. Our approach demonstrates highly efficient training in under 5 min using only 100 data points. It outperforms a state-of-the-art neural estimator by up to 68.1% in force estimation accuracy, utilizing only 1.4% of its network parameters. Furthermore, our method showcases enhanced robustness to network initialization compared to the gradient descent counterpart.

关键词： Training Accuracy Neural networks Force Estimation Optimization methods Robustness

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Adversarial Attacks on Monocular Pose Estimation

Adversarial Attacks on Monocular Pose Estimation

引用

IEEE/RSJ international conference on Intelligent Robots and Systems (IROS)

作者： Chawla, Hemang Varma, Arnav Arani, Elahe Zonooz, Bahram NavInfo Europe Adv Res Lab Eindhoven Netherlands

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Advances in deep learning have resulted in steady progress in computer vision with improved accuracy on tasks such as object detection and semantic segmentation. Nevertheless, deep neural networks are vulnerable to adversarial attacks, thus presenting a challenge in reliable deployment. Two of the prominent tasks in 3D scene-understanding for robotics and advanced driver assistance systems are monocular depth and pose estimation, often learned together in an unsupervised manner. While studies evaluating the impact of adversarial attacks on monocular depth estimation exist, a systematic demonstration and analysis of adversarial perturbations against pose estimation are lacking. We show how additive imperceptible perturbations can not only change predictions to increase the trajectory drift but also catastrophically alter its geometry. We also study the relation between adversarial perturbations targeting monocular depth and pose estimation networks, as well as the transferability of perturbations to other networks with different architectures and losses. Our experiments show how the generated perturbations lead to notable errors in relative rotation and translation predictions and elucidate vulnerabilities of the networks. (1)

关键词： Deep learning Training Three-dimensional displays Systematics Perturbation methods Pose estimation Predictive models

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Design and Validation of a Robotic-Assisted Transperineal Prostate Biopsy System

Design and Validation of a Robotic-Assisted Transperineal Pr...

引用

IEEE international conference on robotics and Biomimetics

作者： Yaqing Wang Shaoming Li Fangxun Zhong Yunhui Liu Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong China Hong Kong Centre for Logistics Robotics (HKCLR) Hong Kong China Department of Mechanical and Automation Engineering (MAE) The Chinese University of Hong Kong (CUHK) HKSAR China School of Science and Engineering CUHK-Shenzhen Shenzhen China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Prostate cancer is a common condition among elderly men, significantly impacting quality of life. It accounts for a large percentage of cancer cases, necessitating effective diagnostic methods. Needle biopsy is a vital procedure for accurate detection and grading of prostate cancer, allowing for targeted treatment. The most common approach is transper-ineal prostate biopsy, guided by real-time ultrasound and MRI fusion. The traditional free-hand biopsy technique is highly dependent on the surgeon's expertise and has limitations in both effectiveness and precision. Robotic-assisted biopsy aims to address these issues by enhancing the accuracy and efficiency of the procedure, reducing the need for extensive surgeon experience. This paper introduces an active six-degree-of-freedom (DOF) and one Z-direction passive DOF transperineal prostate biopsy robot incorporating a 2-DOF ultrasonic probe unit and a biopsy unit with 4-DOF movement to replace the manual operation. This system ensures precise orientation and positioning of the biopsy needle, minimizes patient risk, and supports both 2D and 3D ultrasound imaging, thus facilitating a safe and effective biopsy with improved workflow for surgeons.

关键词： Ultrasonic imaging Accuracy Three-dimensional displays Biopsy Surgery Needles Real-time systems Prostate cancer Probes Robots

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Real-time Acoustic Holography with Iterative Unsupervised Learning for Acoustic Robotic Manipulation

Real-time Acoustic Holography with Iterative Unsupervised Le...

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IEEE international conference on robotics and automation (ICRA)

作者： Chengxi Zhong Zhenhuan Sun Teng Li Hu Su Song Liu School of Information Science and Technology ShanghaiTech University Shanghai China Chinese Academy of Sciences Institute of Automation Beijing China Shanghai Engineering Research Center of Intelligent Vision and Imaging Shanghai China

Phase-only acoustic holography is a fundamental and promising technique for contactless robotic manipulation. Through independently controlling phase-only hologram (POH) of phase array of transducers (PAT) and simultaneously driving each channel by sophisticated circuits, a certain acoustic field is dynamically generated in working medium (e.g., air, water or biological tissues) at certain moment. The phase profile of PAT is required dynamically and precisely as per arbitrary expected acoustic field for the sake of versatile and stable robotic manipulation. However, the most conventional methods rely on iterative optimization algorithms which are inevitably time-consuming and probably non-convergent, moreover hindering versatility and fidelity of acoustic robotic manipulation. To address these issues, this paper reports a real-time phase-only acoustic holography algorithm by virtue of iterative unsupervised learning. Using a physics model to construct two queues, which we refer to as experience pools, data pairs consisting of a target acoustic amplitude hologram in expected acoustic field and corresponding POH of PAT are collected on-the-fly, circumventing costly preparation of annotated dataset in advance. With iterative learning between neural network training and experience pools update, both the solution of objective inverse mapping and the adaptation for arbitrary desired acoustic field are mutually enhanced. The experiments and results validated that the proposed approach surpasses previous algorithms in terms of real time and precision.

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BERT-Enhanced DGA Botnet Detection: A Comparative Analysis of Machine Learning and Deep Learning models

BERT-Enhanced DGA Botnet Detection: A Comparative Analysis o...

引用

international conference on Control, automation and Information Sciences (ICCAIS)

作者： Qui Cao Phuc Dao-Hoang Dat-Thinh Nguyen Xuan-Ha Nguyen Kim-Hung Le University of Information Technology Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam

ISBN: (数字)9798331542047

ISBN: (纸本)9798331542054

With the proliferation of Internet of Things, detecting Domain Generation Algorithm (DGA) botnets is critical for protecting networks from evolving and sophisticated cybersecurity threats. This paper explores a novel approach combining BERT with machine learning and deep learning techniques to detect DGA botnets. We provide a comprehensive benchmark by evaluating the performance of various BERT versions and detection methods on diverse datasets. Our experimental results reveal the significant impact of BERT version selection on detection accuracy and the superior performance of deep learning models, such as CNN, MLP, and LSTM, compared to conventional machine learning models. These findings highlight the potential of BERT and deep learning in improving DGA botnet detection and offer valuable insights for future research in this area.

关键词： Deep learning Analytical models Machine learning algorithms Accuracy automation Botnet Benchmark testing Internet of Things Computer security Long short term memory

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TrunkFlex: Enhancing Engagement in Post-Stroke Trunk Rehabilitation through a Follow-and-Stimulate Cable-Driven Robotic System

TrunkFlex: Enhancing Engagement in Post-Stroke Trunk Rehabil...

引用

international conference on Advanced robotics and Mechatronics (ICARM)

作者： Lufeng Chen Yingming Han Chenyang Liu Bin Li Jiacheng Huang Jing Qiu Center for Robotics School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Center for Robotics School of Mechanical and Electric Engineering University of Electronic Science and Technology of China Chengdu China Yingcai Honors College of UESTC University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Trunk rehabilitation plays a pivotal role in enhancing locomotive capacities and balance for patients with hemiplegia. In this paper, we present TrunkFlex, a cable-driven robotic system designed specifically to promote self-engagement in post-stroke trunk rehabilitation. Unlike conventional rehabilitation robots, which often interact with patients in a rigid manner, TrunkFlex incorporates a cable-driven mechanism powered by eight servo motors, allowing for versatile rehabilitation exercises. Our introduced follow-and-stimulate module, along with posture correction and speed limit modules, is integrated into the control scheme and operates in parallel to induce patient-initiated movement, thereby encouraging self-engagement and promoting a more flexible and seamless rehabilitation process. Muscle activation experiments demonstrate a significant enhancement in active engagement, as evidenced by a marked increase in muscle activity intensity compared to passive rehabilitation methods.

关键词： Mechatronics Process control Muscles Clinical trials Stroke (medical condition) Assistive robots Safety Servomotors Robots

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Generating robotic elliptical excisions with human-like tool-tissue interactions

Generating robotic elliptical excisions with human-like tool...

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IEEE international conference on robotics and automation (ICRA)

作者： Artūras Straižys Michael Burke Subramanian Ramamoorthy School of Informatics University of Edinburgh Department of Electrical and Computer Systems Engineering Monash University

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In surgery, the application of appropriate force levels is critical for the success and safety of a given procedure. While many studies are focused on measuring in situ forces, little attention has been devoted to relating these observed forces to surgical techniques. Answering questions like "Can certain changes to a surgical technique result in lower forces and increased safety margins?" could lead to improved surgical practice, and importantly, patient outcomes. However, such studies would require a large number of trials and professional surgeons, which is generally impractical to arrange. Instead, we show how robots can learn several variations of a surgical technique from a smaller number of surgical demonstrations and interpolate learnt behaviour via a parameterised skill model. This enables a large number of trials to be performed by a robotic system and the analysis of surgical techniques and their downstream effects on tissue. Here, we introduce a parameterised model of the elliptical excision skill and apply a Bayesian optimisation scheme to optimise the excision behaviour with respect to expert ratings, as well as individual characteristics of excision forces. Results show that the proposed framework can successfully align the generated robot behaviour with subjects across varying levels of proficiency in terms of excision forces.

关键词： Force measurement Force Surgery Safety Bayes methods Robots Optimization

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