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arXiv 2024年

作者： Liang, Xudong Ding, Yimiao Yuan, Zihao Jiang, Junqi Xie, Zongling Fei, Peng Sun, Yixuan Gu, Guoying Zhong, Zheng Chen, Feifei Si, Guangwei Gong, Zhefeng School of Science Harbin Institute of Technology Shenzhen518055 China Department of Neurology The Fourth Hospital and School of Brain Science and Brain Medicine Zhejiang University School of Medicine Hangzhou310058 China Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-Machine Integration State Key Laboratory of Brain-Machine Intelligence Zhejiang University 1369 West Wenyi Road Hangzhou311121 China NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang University Hangzhou310058 China State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University Shanghai200240 China Robotics Institute School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China State Key Laboratory of Brain and Cognitive Science Institute of Biophysics Chinese Academy of Sciences 15 Datun Road Beijing100101 China University of Chinese Academy of Sciences 19A Yuquan Road Beijing100049 China Huazhong University of Science and Technology Optical and Electronic Information Wuhan430074 China Zhejiang Lab. Hangzhou311121 China

The Drosophila larva, a soft-body animal, can bend its body and roll efficiently to escape danger. However, contrary to common belief, this rolling motion is not driven by the imbalance of gravity and ground reaction forces. Through functional imaging and ablation experiments, we demonstrate that the sequential actuation of axial muscles within an appropriate range of angles is critical for generating rolling. We model the interplay between muscle contraction, hydrostatic skeleton deformation, and body-environment interactions, and systematically explain how sequential muscle actuation generates the rolling motion. Additionally, we constructed a pneumatic soft robot to mimic the larval rolling strategy, successfully validating our model. This mechanics model of soft-body rolling motion not only advances the study of related neural circuits, but also holds potential for applications in soft robotics. © 2024, CC BY.

关键词： Pneumatics

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FOP Networks for Learning Humanoid Body Schema and Dynamics 18

FOP Networks for Learning Humanoid Body Schema and Dynamics

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18th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2018

作者： Diaz Ledezma, Fernando Haddadin, Sami Department of Robotics Science and Systems Intelligence Munich School of Robotics and Machine Intelligence Technische Universität München München80797 Germany

ISBN: (纸本)9781538672839

Robot inverse dynamics modeling is performed mainly via standard system identification and/or machine learning techniques. In this paper we part from the theoretical framework of First-Order Principles Networks (FOPnet), combining data-aided learning with basic knowledge to learn the model of a targeted robot. The framework, previously used for learning the dynamics of a fixed-base serial manipulator, is now extended to the learning of the kinematics and dynamics of tree-structured robots with floating base. Our approach leverages the principle of compositionality to separate the main problem into two partially independent modules. The first defines the robot's body schema by characterizing its morphology and topology. The second is dependent upon the latter and defines the inertial properties of the multi-body system. To demonstrate the capabilities of the approach, a simulated humanoid robot with 30 degrees of freedom is used. We discuss the implementation of our method and evaluate its estimation and generalization capabilities in comparison with other common machine learning approaches. Finally, we present experimental results on a 7- DoF manipulator. © 2018 IEEE.

关键词： Anthropomorphic robots

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Collision Preventing Phase-Progress Control for Velocity Adaptation in Human-Robot Collaboration

Collision Preventing Phase-Progress Control for Velocity Ada...

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IEEE-RAS International Conference on Humanoid Robots

作者： Dinmukhamed Zardykhan Petr Svarny Matej Hoffmann Erfan Shahriari Sami Haddadin Munich School of Robotics and Machine Intelligence Technical University of Munich Munich Germany Czech Technical University in Prague Czech Republic

ISBN: (数字)9781538676301

ISBN: (纸本)9781538676318

As robots are leaving dedicated areas on the factory floor and start to share workspaces with humans, safety of such collaboration becomes a major challenge. In this work, we propose new approaches to robot velocity modulation: While the robot is on a path prescribed by the task, it predicts possible collisions with the human and gradually slows down, proportionally to the danger of collision. Two principal approaches are developed-Impulse Orb and Prognosis Window-that dynamically determine the possible robot-induced collisions and apply a novel velocity modulating approach, in which the phase progress of the robot trajectory is modulated while the desired robot path remains intact. The methods guarantee that the robot will halt before contacting the human, but they are less conservative and more flexible than solutions using reduced speed and complete stop only, thereby increasing the effectiveness of human-robot collaboration. This approach is especially useful in constrained setups where the robot path is prescribed. Speed modulation is smooth and does not lead to abrupt motions, making the behavior of the robot also better understandable for the human counterpart. The two principal methods under different parameter settings are experimentally validated in a human-robot interaction scenario with the Franka Emika Panda robot, an external RGB-D camera, and human keypoint detection using OpenPose.

关键词： Collision avoidance Task analysis Prognostics and health management Collaboration End effectors Trajectory

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Abnormal Data Cleaning for Wind Turbines by Image Segmentation Based on Active Shape Model and Class Uncertainty

SSRN

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SSRN 2022年

作者： Liang, Guoyuan Su, Yahao Wu, Xinyu Ma, Jiajun Long, Huan Song, Zhe Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China School of Electrical Engineering Southeast University Jiang Su Province Nanjing China School of Business Nanjing University Jiang Su Province Nanjing China

Wind power curve describes the relationship between wind speed and output power of wind turbine, which may be contaminated due to various unexpected factors. Following the idea of image segmentation in our previous work, in this paper, we propose a novel image-based abnormal data cleaning algorithm aiming to provide high quality data for wind power curve modeling and relevant applications. For image binarization, a multiscale space is built to determine the optimal scale at which the most stable as well as informative shape representations are extracted. The Active Shape Model is utilized to model the power curve shape and generate a set of segmentation candidates for filtering abnormal data. To measure the cost of each candidate, the Class Uncertainty map based on dual feature images is built. And eventually the optimal segmentation boundary is determined by the optimization in the energy space. A series of comparative experiments conducted over the real-world datasets of 37 wind turbines have validated the superior performance of the proposed algorithm. © 2022, The Authors. All rights reserved.

关键词： Wind power

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TransHD: Spatial Transformer Features Extraction for HDC Synergetic Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial intelligence 2025年

作者： Hassan, Eman Bettayeb, Meriem Halawani, Yasmin Genssler, Paul R. Tesfai, Huruy Zweiri, Yahya Amrouch, Hussam Hadjileontiadis, Leontios J. Saleh, Hani Mohammad, Baker Khalifa University System on Chip Lab Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates Abu Dhabi University Department of Computer Science and Information Technology College of Engineering Abu Dhabi United Arab Emirates University of Dubai College of Engineering and IT Dubai United Arab Emirates Munich Institute of Robotics and Machine Intelligence Technical University of Munich TUM School of Computation Information and Technology Chair of AI Processor Design Munich Germany Abu Dhabi United Arab Emirates Stuttgart Germany Khalifa University of Science and Technology Department of Biomedical Engineering Abu Dhabi United Arab Emirates Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki Greece

Artificial intelligence (AI) relies on pattern recognition and classification algorithms to achieve accurate and efficient decision-making. Convolutional neural networks (CNNs) are the state-of-the-art for processing 2D image data, but their high computational and data requirements limit their use in resource-constrained environments. Hyperdimensional computing (HDC) offers a lightweight alternative, excelling in 1D tasks, but struggles to achieve competitive accuracy for 2D image classification. Hybrid frameworks combining HDC with CNNs have been proposed to improve accuracy but inherit the computational demands of deep learning, making them unsuitable for edge and IoT devices. To address this, we propose TransHD, a framework integrating lightweight spatial transformer networks (STNs) with HDC to enhance image classification performance while maintaining efficiency. TransHD achieves up to 9% higher accuracy than base HDC models on MNIST and Fashion-MNIST using only 30% of the training data, and reduces computational complexity by 2.5x through optimized STN feature map usage. On resource-constrained platforms like the Raspberry Pi 4, TransHD accelerates inference times by 3.4x and improves energy efficiency by 3.2x, with an accuracy trade-off of approximately 3% compared to CNNs. This study demonstrates the potential of combining HDC with STNs to develop efficient AI solutions for IoT and edge computing, where low energy consumption and computational efficiency are critical. © 2020 IEEE.

关键词： Convolutional neural networks

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UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection

arXiv

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arXiv 2021年

作者： Abdar, Moloud Salari, Soorena Qahremani, Sina Lam, Hak-Keung Karray, Fakhri Hussain, Sadiq Khosravi, Abbas Acharya, U. Rajendra Makarenkov, Vladimir Nahavandi, Saeid Deakin University Australia The Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States The Department of Electrical Engineering Sharif University of Technology Tehran Iran The Centre for Robotics Research Department of Engineering King’s College London London United Kingdom The Centre for Pattern Analysis and Machine Intelligence Department of Electrical and Computer Engineering University of Waterloo WaterlooON Canada The Department of Machine Learning Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates The System Administrator Dibrugarh University Dibrugarh India The Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi Singapore The Department of Computer Science University of Quebec in Montreal MontrealQC Canada

The COVID-19 (Coronavirus disease 2019) pandemic has become a major global threat to human health and well-being Thus, the development of computer-aided detection (CAD) systems that are capable to accurately distinguish COVID-19 from other diseases using chest computed tomography (CT) and X-ray data is of immediate priority Such automatic systems are usually based on traditional machine learning or deep learning methods Differently from most of existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a simple but efficient deep learning feature fusion model, called UncertaintyFuseNet, which is able to classify accurately large datasets of both of these types of images We argue that the uncertainty of the model’s predictions should be taken into account in the learning process, even though most of existing studies have overlooked it We quantify the prediction uncertainty in our feature fusion model using effective Ensemble MC Dropout (EMCD) technique A comprehensive simulation study has been conducted to compare the results of our new model to the existing approaches, evaluating the performance of competing models in terms of Precision, Recall, F-Measure, Accuracy and ROC curves The obtained results prove the efficiency of our model which provided the prediction accuracy of 99.08% and 96.35% for the considered CT scan and X-ray datasets, respectively Moreover, our UncertaintyFuseNet model was generally robust to noise and performed well with previously unseen data The source code of our implementation is freely available at: https://***/moloud1987/UncertaintyFuseNet-for-COVID-19-Classification. Copyright © 2021, The Authors. All rights reserved.

关键词： Coronavirus

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Mechanics of Soft-Body Rolling Motion without External Torque

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Physical Review Letters 2025年第19期134卷 198401-198401页

作者： Xudong Liang Yimiao Ding Zihao Yuan Yuxuan Han Yutang Zhou Junqi Jiang Zongling Xie Peng Fei Yixuan Sun Pan Jia Guoying Gu Zheng Zhong Feifei Chen Guangwei Si Zhefeng Gong School of Science Harbin Institute of Technology Shenzhen 518055 China Department of Neurology of the Fourth Hospital and School of Brain Science and Brain Medicine Zhejiang University School of Medicine Hangzhou 310058 China Robotics Institute and State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of Cognitive Science and Mental Health Institute of Biophysics Chinese Academy of Sciences 15 Datun Road Beijing 100101 China University of Chinese Academy of Sciences 19A Yuquan Road Beijing 100049 China Huazhong University of Science and Technology Optical and Electronic Information Wuhan 430074 China Zhejiang Lab Hangzhou 311121 China Shanghai Key Laboratory of Intelligent Robotics Meta Robotics Institute Shanghai Jiao Tong University Shanghai 200240 China Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-machine Integration State Key Laboratory of Brain-machine Intelligence Zhejiang University 1369 West Wenyi Road Hangzhou 311121 China NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang University Hangzhou 310058 China

The Drosophila larva, a soft-body animal, can bend its body and roll efficiently to escape danger. However, contrary to common belief, this rolling motion is not driven by the imbalance of gravity and ground reaction forces. Through functional imaging and ablation experiments, we demonstrate that the sequential actuation of axial muscles within an appropriate range of angles is critical for generating rolling. We model the interplay between muscle contraction, hydrostatic skeleton deformation, and body-environment interactions, and systematically explain how sequential muscle actuation generates the rolling motion. Additionally, we construct a pneumatic soft robot to mimic the larval rolling strategy, successfully validating our model. This mechanics model of soft-body rolling motion not only advances the study of related neural circuits, but also holds potential for applications in soft robotics.

关键词： Creatinine

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Identification of Human Shoulder-Arm Kinematic and Muscular Synergies During Daily-Life Manipulation Tasks

Identification of Human Shoulder-Arm Kinematic and Muscular ...

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IEEE International Conference on Biomedical robotics and Biomechatronics

作者： Tingli Hu Johannes Kuehn Sami Haddadin Munich School of Robotics and Machine Intelligence Technical University of Munich

ISBN: (纸本)9781538681848

Understanding the inherent synergistic nature of human neuromuscular control has already successfully contributed to the development of human-centered robotic systems. To further extend this line of research, a thorough understanding of the synergy spaces in which human movements are planed and executed is necessary. In this paper, the shoulder-arm kinematic and muscular synergies for typical 30 daily-life tasks are identified. For this, an experimental dataset of multi-joint motion trajectories and surface electromyograms of 6 healthy male subjects was created. Based on this, the synergies are identified by applying state-of-the-art machine learning techniques. The identification results suggest 1) synergy space dimensionality correlates with task complexity, 2) 3-D synergy spaces are sufficient to explain {approx.>} 95% variance, 3) presumably, each task is mainly executed in its own kinematic and muscular synergy space, and 4) the similarity in synergy coordinates is indicated to correlate to similarity in joint space. Subsequently, these synergy spaces shall be integrated into human-inspired controller design of robotic systems for improved rehabilitation, assistance, and more human-like prosthetic devices.

关键词： Synergism muscles kinematics Prosthesis task complexity Human Religious Missions

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An Image segmentation method by multi-scale local thresholding based on class uncertainty theory

An Image segmentation method by multi-scale local thresholdi...

引用

2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Zhou, Likai Jiang, Yuyang Liang, Guoyuan Wu, Xinyu Zhu, Jiafeng Xiang, Huaikun Shenzhen Institutes of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences China School of Automotive and Transportation Engineering Shenzhen Polytechnic Shenzhen Guangdong Province China School of Math Sun Yat-sen University Guangzhou Guangdong Province China

ISBN: (纸本)9781728163215

Image segmentation is one of the most important core technologies in the field of image processing and computer vision, which has lots of applications specifically in medical image analysis. Due to medical imaging mechanism, medical images usually suffer from heavy noises, uneven intensity distribution and fuzzy boundaries between biological tissues. That makes classical segmentation methods based on global or local thresholding difficult to get accurate results. This paper proposes an image segmentation method by multi-scale local thresholding based on class uncertainty theory. Firstly, the original image is divided into a set of sub-regions with different scales by using a multi-layer pyramid structure. Secondly, the energy function with inequality constraints is constructed based on class uncertainty and region uniformity. Then by an iterative process, the optimal local threshold for each sub-region are calculated by an optimization algorithm for each layer until the final optimal threshold mask is determined for image segmentation. Experimental results verify that the proposed method can effectively eliminate the interferences caused by image noises, intensity unevenness and fuzzy boundaries, and preserve details of object structure while segmenting the object from the background. Segmentation results demonstrate the superior performance of the proposed method by comparison with supervised range-constrained thresholding methods (RCOtsu), classical global/local Otsu method, and the minimization of homogeneity- and uncertainty-based energy method (MHUE). © 2019 IEEE.

关键词： Image segmentation

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Movement Control and Attitude Adjustment of Climbing Robot on Flexible Surfaces

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2018年第3期65卷 2618-2628页

作者： Wu, Xinyu Liu, Jia Zhou, Yimin Lv, Qin Hu, Chaofang CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China School of Electrical and Information Engineering Tianjin University Tianjin China

In this paper, a series of control algorithms have been designed for the climbing cloth robot, "Clothbot" to climb vertically. The developed algorithm consists of three parts: a sliding mode control with integral action (SMC with integral action), a linear quadratic regulator, and dead reckoning for semi-autonomous navigation. First, the SMC with integral action is developed for high-precision position tracking and speed tracking of the permanent magnet direct current (PMDC) motor installed on the robot. The error between the expected velocity and the actual velocity of the PMDC motor is adopted to design the sliding surface. In order to compensate the fluctuation of the load torque due to the varied friction during climbing, an observer is formulated to estimate the load torque in real-time. Second, the LQR is employed for attitude adjustment by an active torque so as to prevent the robot from falling or being jammed during the movement. Third, due to the onboard-limited energy, the robot is just equippedwith semi-autonomous navigation named dead reckoning carrying on primary localization and primary path planning. Experiments are performed to verify the efficacy of the proposed control algorithms.

关键词： Climbing robot flexible surface linear quadratic regulator load observer sliding mode control with integral action

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