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SSRN 2025年

作者： Hu, Yang Monticolo, Davy Ghadimi, Pezhman Laboratory for Advanced Manufacturing Simulation and Robotics School of Mechanical & Materials Engineering University College Dublin Belfield Dublin Ireland Université de Lorraine ERPI Nancy France

Persistent quality problems with medical devices and the associated recall present potential health risks to patients and users, bringing extra costs to manufacturers and disturbances to the entire supply chain (SC). Conventional medical device recall strategy neglects the significance of the failure detection process in the premarket phase, increasing the risk of medical device recall events. Insufficient research on developing an effective methodology for addressing medical device recall failure detection leads to economic loss and health threats. To address these problems, this research first reconsiders the structure of the conventional medical device recall strategy theory from the supply chain risk management and resilience view and reinforces the importance of failure detection and quality inspection work in the premarket stage. Then, we developed a machine learning-based medical device recall initiator prediction framework with five algorithms to conduct proactive failure detection based on the practical industrial case. In the case study, an accuracy rate of 88.18% is achieved, which indicates the potential of the proposed framework in assisting manufacturers with asset predictive failure detection for reducing recall. A comparative analysis of prediction performance between our framework and the most similar research was presented. The comparison results showed that our distinctive design in the dataset, clustering method, and key input features chosen are valid and efficient. The proposed predictive framework obtains higher accuracy, scalability, and practical values. The main purpose of the proposed predictive framework is to build a sustainable and resilient supply chain using an intelligent decision-making support tool. © 2025, The Authors. All rights reserved.

关键词： Outages

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Simplifying Autonomous Aerial Operations: LUCAS, a Lightweight Framework for UAV Control and Supervision

Simplifying Autonomous Aerial Operations: LUCAS, a Lightweig...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： J.I. Murillo M.A. Montes R. Zahinos M.A. Trujillo A. Viguria G. Heredia Calle Wilbur y Orville Wright CATEC (Advanced Center for Aerospace Technologies) Sevilla Spain Robotics Vision and Control Group School of Engineering University of Seville Seville Spain

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

This work introduces LUCAS 3 3 https://***/catec/lucas, an open-source framework designed to control and monitor highly autonomous UAV systems. This framework is composed of two modules, the Control Manager Finite-State-Machine (FSM), an efficient and easily extensible state machine that controls the behavior of the robot during the mission, and the Cascade Controller, which handles the commands to the low-level autopilot. The methodology followed to develop the framework has been the use of C++ and a modularized implementation, to separate communications from core functionality and allow the use of diverse middlewares, in this case, ROS and ROS2. The system can be integrated with other software nodes to form a complete autonomous setup, which has been successfully tested in a simulated environment and in a real scenario, where a quad-rotor has to perform an indoor inspection in a building under construction.

关键词： Buildings Inspection Autonomous aerial vehicles Aircraft navigation Libraries Autopilot Trajectory Middleware Monitoring Robots

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NavG: Risk-Aware Navigation in Crowded Environments Based on Reinforcement Learning with Guidance Points

arXiv

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arXiv 2025年

作者： Zhang, Qianyi Luo, Wentao Liu, Boyi Zhang, Ziyang Wang, Yaoyuan Liu, Jingtai Institute of Robotics and Automatic Information System Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin China Advanced Computing and Storage Lab Huawei 2012 Lab China Department of Electronic and Computer Engineering The Kong Kong University of Science and Technology Hong Kong

Motion planning in navigation systems is highly susceptible to upstream perceptual errors, particularly in human detection and tracking. To mitigate this issue, the concept of guidance points—a novel directional cue within a reinforcement learning-based framework—is introduced. A structured method for identifying guidance points is developed, consisting of obstacle boundary extraction, potential guidance point detection, and redundancy elimination. To integrate guidance points into the navigation pipeline, a perception-to-planning mapping strategy is proposed, unifying guidance points with other perceptual inputs and enabling the RL agent to effectively leverage the complementary relationships among raw laser data, human detection and tracking, and guidance points. Qualitative and quantitative simulations demonstrate that the proposed approach achieves the highest success rate and near-optimal travel times, greatly improving both safety and efficiency. Furthermore, real-world experiments in dynamic corridors and lobbies validate the robot’s ability to confidently navigate around obstacles and robustly avoid pedestrians. © 2025, CC BY.

关键词： Travel time

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Enhanced Detection of Demagnetization in Direct-Drive Wind Turbine Generators Using eMIST and Machine Learning

Enhanced Detection of Demagnetization in Direct-Drive Wind T...

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IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD)

作者： Qiuyi Chen Panagiotis Tsilifis Jaime Renedo Anglada Department of Mechanical Engineering University of Maryland College Park MD USA Robotics AI & Computer Vision Group GE Vernova Advanced Research Niskayuna NY USA Electrical Systems GE Vernova Advanced Research Niskayuna NY USA

ISBN: (数字)9798331520748

ISBN: (纸本)9798331520755

The rapid growth of offshore wind energy needs robust condition monitoring to ensure the reliability and efficiency of direct-drive permanent magnet synchronous generators (PMSGs). Demagnetization, a critical fault in these generators, can severely impact performance and increase operational costs, especially in harsh marine environments. Traditional diagnostic techniques often struggle to detect subtle magnetic property changes under varying load conditions. This paper presents an innovative framework for demagnetization detection in PMSGs, leveraging the Electrical Multi-phase Imbalance Separation Technique (eMIST) combined with machine learning models. Using a dataset from offshore wind turbines, including simulated faulty conditions, the proposed methodology enhances anomaly detection by isolating imbalance signatures and correlating them with key operational parameters. Gaussian Process Regression (GPR) is employed to predict anomalies, offering high accuracy and confidence in fault classification. Results demonstrate the approach's ability to identify early-stage demagnetization, enabling predictive maintenance, reducing downtime, and improving the economic viability of offshore wind farms.

关键词： Costs Accuracy Wind energy Machine learning Gaussian processes Wind farms Generators Wind turbines Demagnetization Anomaly detection

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Understanding Connection between PMP and HJB equations from the Perspective of Hamilton Dynamics

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Zhang, Xiangteng Lyu, Yao Li, Shengbo Eben Duan, Jingliang Zhan, Guojian Liu, Chang Cheng, Bo Li, Keqiang Tsinghua University School of Vehicle and Mobility Beijing100084 China University of Science and Technology Beijing School of Mechanical Engineering China Peking University Department of Advanced Manufacturing and Robotics Beijing100871 China Peking University College of Engineering Beijing100871 China

Reinforcement learning (RL) is widely used to tackle optimal control problems, with optimality conditions as the principle of algorithm design. Two key optimality conditions are the Pontryagin Maximum Principle (PMP) and the Hamilton-Jacobi-Bellman (HJB) equation. The relationship between these conditions is vital for developing effective policy learning algorithms. Existing studies mainly focus on their relationship in the open-loop optimality, but this property cannot be directly extended to nonoptimal or closed-loop cases due to the absence of extreme condition and the existence of non-zero partial derivative term. This paper unifies the relationship between PMP and HJB equations in all cases by considering optimal control problems as nonholonomic Lagrange systems, and proves the intrinsic equivalence between value function and costate variable from the perspective of Hamilton dynamics. We redefine costate variable as Legendre transformation of state derivative in nonholonomic Lagrange systems, where the Weierstrass condition is selected as a constraint for optimal cases while fixed policy condition is chosen for nonoptimal cases. By utilizing the anti-symmetric property of canonical equations, we identify conservation properties in optimal control where symplectic form remains invariant across all cases. Additionally, we prove that the costate variable has the identical differential equation and boundary conditions as the partial derivative of value function with respect to state in both optimal and nonoptimal, open-loop and closed-loop cases. Numerical experiments are conducted to verify our theoretical results. This discovery can establish more readily available conservation conditions, thereby providing a high-level view angle on algorithm designs. © 2020 IEEE.

关键词： Lagrange multipliers

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Visual Sensor-Based Trajectory Multimodality Prediction via Anchor-Free Query Attention Network

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Wang, Ruiping Cheng, Jun Yu, Junzhi Nanyang Technological University School of Computer Science and Engineering Jurong West Singapore639798 Singapore Peking University College of Engineering Beijing100871 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China

As a core component of intelligent surveillance and autonomous driving systems, visual sensor-based trajectory multimodality prediction can significantly improve their perception and decision-making capabilities. Numerous existing trajectory prediction methods are dedicated to enhance prediction performance. However, they still fail to effectively model the complex spatiotemporal interactions among pedestrians and trajectory multimodality. To address the above challenges, this article presents one new trajectory multimodality prediction method via anchor-free query-based attention network, which can more effectively capture spatiotemporal interactions and model trajectory multimodality. First, the initial trajectory proposal module employs target-centered query-based attention to capture complex spatiotemporal interactions and generate multiple trajectory proposals corresponding to the multiple modes of the future trajectory, which allows the model to use different interaction information when decoding trajectory points at different time steps. Second, the novel trajectory refinement module utilizes the learnable trajectory proposals to construct proposal-level interaction mechanisms to refine future trajectories. Experimental results conducted on ETH and UCY datasets demonstrate that the proposed model can provide the state-of-the-art prediction performance. © 1963-2012 IEEE.

关键词： Prediction models

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Design and evaluation of hybrid upper limb exoskeleton considering human–robot interaction force

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Intelligent Service robotics 2025年 1-15页

作者： Xu, Pengpeng Xia, Dan Cheng, Jun Pang, Jianxin Xie, Longhan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China College of Information Science and Engineering Hohai University Changzhou China Research Institute of UBTech Robotics Shenzhen China School of Intelligent Engineering South China University of Technology Shien-Ming Wu Guangzhou China

The efficacy of upper limb exoskeleton-assisted motion training is critical for the rehabilitation of motor impairments resulting from neural injuries. Despite technological advancements, these devices continue to face substantial limitations in joint safety design, particularly in achieving optimal alignment between the human shoulder joint and the exoskeleton, which is essential for effective and safe rehabilitation. To address these challenges, this study introduces a novel hybrid shoulder rehabilitation exoskeleton that incorporates a follower mechanism and an adaptive regulation algorithm for shoulder redundant force perception. Theoretical analyses reveal that, compared to conventional exoskeleton structures, this mechanism significantly enhances human-robot interaction (HRI) compatibility by improving joint alignment and reducing mechanical constraints. Experimental results demonstrate that the activation of the follower mechanism reduces redundant force at the shoulder joint to 50.1% of those observed in its absence. These findings provide robust evidence that the proposed design not only mitigates shoulder redundant forces but also supports a wide range of rehabilitation training modalities. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Comfort Evaluation Human–robot interaction forces Hybrid mechanism Shoulder Exoskeleton

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Novel method for robust bilateral filtering point cloud denoising

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Alexandria engineering Journal 2025年 127卷 573-585页

作者： Yang, Huan Wang, Wei Wang, Yue Wang, Peng College of Computer Science and Technology Chongqing University of Posts and Telecommunications No. 2 Chongwen Road Chongqing Nan'an400065 China School of Advanced Manufacturing Engineering Chongqing University of Posts and Telecommunications No. 2 Chongwen Road Chongqing Nan'an400065 China Sevnce Robotics Co. Ltd Building 7 Phase II Internet Industrial Park Chongqing Liangjiang401123 China Big Data Institute BaoShan University No. 16 Yuanzheng Road Longyang District Yunnan Baoshan678000 China

Bilateral filtering for point cloud denoising relies on the normal of the points. However, normals are not inherent attributes of point clouds but are estimated using algorithms such as principal component analysis (PCA). Due to inaccuracies in the normals, bilateral filtering may lead to unexpected results, such as smoothing in sharp regions. This study addressed this issue by improving the accuracy of normals. Initially, PCA was employed to estimate the initial normals of the point cloud. The points were then categorized into edge and planar points by fitting a three-dimensional sphere. Subsequently, iterative weighted PCA was applied to refine the normals of the edge points. Finally, the improved normals were integrated into bilateral filtering to achieve point cloud denoising. Additionally, a robust estimator was used instead of a Gaussian function to compute the weights in the bilateral filtering. To assess and compare the performance of this algorithm with related ones, the mean square angular error (MSAE) and angular error distribution (AE) were employed to evaluate the accuracy of normal estimation, while the mean square error (MSE) and signal-to-noise ratio (SNR) were employed to assess the denoising performance. Results indicate that the proposed method yielded the most accurate normals compared to PCA, Jet, and VCM normal estimation algorithms. Moreover, when compared to algebraic point set surfaces (APSS), robust implicit moving least squares (RIMLS), anisotropic weighted locally optimal projection (AWLOP), bilateral filtering, and guided filtering point cloud denoising algorithms, the proposed method consistently achieved the smallest MSE and the highest SNR in most cases on the dataset used in this study. © 2025 The Authors

关键词： Principal component analysis

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Ultrahigh interfacial thermal conductance for cooling gallium oxide electronics using cubic boron arsenide

arXiv

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arXiv 2025年

作者： Zhou, Wenjiang Liang, Nianjie Xiao, Wei Tong, Zhaofei Tian, Fei Song, Bai Department of Energy and Resources Engineering Peking University Beijing100871 China School of Advanced Engineering Great Bay University Dongguan523000 China School of Materials Science and Engineering Sun Yat-sen University Guangdong Guangzhou510006 China Department of Advanced Manufacturing and Robotics Peking University Beijing100871 China National Key Laboratory of Advanced MicroNanoManufacture Technology Beijing100871 China

Gallium oxide (Ga2O3) has attracted significant interest for its unique potential especially in power electronics. However, its low and anisotropic thermal conductivity poses a major challenge for heat dissipation. Here, we explore an effective cooling strategy centering on the heterogeneous integration of β-Ga2O3 devices with cubic boron arsenide (cBAs), an emerging material with an ultrahigh thermal conductivity κ of ~1300 Wm-1K-1. Machine-learned potentials for representative β-Ga2O3/cBAs interfaces are trained, enabling accurate and efficient calculation of the interfacial thermal conductance G via nonequilibrium molecular dynamics. At 300 K, remarkable G values of 749±33 MWm-2K-1 and 824±35 MWm-2K-1 are predicted for Ga-As and O-B bonding across the interface, respectively, which are primarily attributed to the well-matched phonon density of states considering the similar Debye temperatures of β-Ga2O3 and cBAs. Moreover, finite-element simulations directly show a notable device temperature reduction when comparing cBAs with other substrates. The simultaneously ultrahigh κ and G highlight cBAs as an ideal substrate for Ga2O3 electronics. Copyright © 2025, The Authors. All rights reserved.

关键词： Computer resource management

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Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding

arXiv

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arXiv 2025年

作者： Li, Jinze Xu, Yixing Huang, Haiduo Yin, Xuanwu Li, Dong Ngai, Edith C.H. Barsoum, Emad Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Advanced Micro Devices Inc. Beijing China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China

Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Some approaches employ a draft model with multiple heads to predict a sequence of future tokens, where each head handles a token in the sequence. The target LLM verifies the predicted sequence and accepts aligned tokens, enabling efficient multi-token generation. However, existing methods assume that all tokens within a sequence are equally important, employing identical head structures and relying on a single-generation paradigm, either serial or parallel. To this end, we theoretically demonstrate that initial tokens in the draft sequence are more important than later ones. Building on this insight, we propose Gumiho, a hybrid model combining serial and parallel heads. Specifically, given the critical importance of early tokens, we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy. For later tokens, we utilize multiple lightweight MLP heads operating in parallel to enhance efficiency. By allocating more advanced model structures and longer running times to the early heads, Gumiho achieves improved overall performance. The experimental results demonstrate that our method outperforms existing approaches, fully validating its effectiveness. © 2025, CC BY.

关键词： Decoding

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