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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Mu, Fengjun Zhang, Jingting Zou, Chaobin Huang, Zonghai Huang, Rui Cheng, Hong School of Automation Engineering University of Electronic Science and Technology of China Center for Robotics Chengdu China

ISBN: (纸本)9781665481090

Monitoring abnormal attention has a wide range of applications in Human-Robot Interaction systems, such as autonomous driving, virtual reality, and remote operation. Due to factors like fatigue, drowsiness, and distraction, human attention can deviate from tasks that require focus, resulting in abnormal attention during interactions. Monitoring for abnormal attention is essential to ensure the safety of human-robot interactions. However, existing methods often rely on un-explainable algorithms such as neural networks, making them difficult to trust in scenarios like autonomous driving, where explicit decision-making processes and mechanisms are required. This paper presents EyeAAD, an abnormal attention detection method based on dynamic system theory. This method uses gaze signals from a serious game designed to simulate human-robot interaction. We developed several attention-related features for interpretable modeling using a deterministic learning method. By analyzing the matching relationship between the gaze signal and the predicted status from the learned dynamic models, we can detect abnormal human attention patterns. To validate our approach, we designed a serious game for eye movement data acquisition in human-robot interaction. Our analysis showed that EyeAAD enables efficient, real-time monitoring of human attention. © 2024 IEEE.

关键词： Serious games

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Enhancing Dynamic Parameter Identification for Supernumerary Robotic Limbs through Uncertainty Compensation

Enhancing Dynamic Parameter Identification for Supernumerary...

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2024 China automation Congress, CAC 2024

作者： Chen, Lufeng Lan, Jingwen Zhan, Qi'ao Peng, Zhinan Li, Wenjiang Qiu, Jing Center for Robotics School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Center for Robotics School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350368604

Accurate dynamic modeling is fundamental for wearable robotics, such as Supernumerary Robotic Limbs (SRLs), to ensure precise control and effective interaction with the environment. This paper presents an enhanced dynamic model for an SRL, incorporating uncertainty compensation to improve accuracy and robustness. The SRL dynamics are streamlined by linearizing the system and identifying a minimal set of inertia parameters. Using the Coot optimization algorithm, we refine the Fourier excitation trajectory parameters to optimize the condition number of the observation matrix, followed by parameter identification through the least squares method. To further reduce discrepancies between predicted and actual joint torques, an uncertainty term is introduced, validated through a neural network designed to predict torque variations. The method not only addresses but also compensates for uncertainties across various motion states of the SRL. Experimental results demonstrate that the proposed model improves prediction accuracy and robustness for our developed 6-DoF SRL compared to traditional methods. © 2024 IEEE.

关键词： Least squares approximations

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Dynamics Modeling and Parameter Identification for a Coupled-Drive Dual-Arm Nursing Robot

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Chinese Journal of Mechanical engineering 2024年第4期37卷 243-257页

作者： Hao Lu Zhiqiang Yang Deliang Zhu Fei Deng Shijie Guo College of Electronic Information and Automation Tianjin University of Science and TechnologyTianjin 300222China Academy for Engineering&Technology Fudan UniversityShanghai 200433China Shanghai Engineering Research Center of AI&Robotics Shanghai 200433China School of Mechanical Engineering Hebei University of TechnologyTianjin 300130China

A dual-arm nursing robot can gently lift patients and transfer them between a bed and a *** its lightweight design,high load-bearing capacity,and smooth surface,the coupled-drive joint is particularly well suited for these ***,the coupled nature of the joint disrupts the direct linear relationship between the input and output torques,posing challenges for dynamic modeling and practical *** study investigated the transmission mechanism of this joint and employed the Lagrangian method to construct a dynamic model of its internal *** on this foundation,the Newton-Euler method was used to develop a dynamic model for the entire robotic arm.A continuously differentiable friction model was incorporated to reduce the vibrations caused by speed transitions to *** experimental method was designed to compensate for gravity,inertia,and modeling errors to identify the parameters of the friction *** method establishes a mapping relationship between the friction force and motor *** addition,a Fourier series-based excitation trajectory was developed to facilitate the identification of the dynamic model parameters of the robotic *** tracking experiments were conducted during the experimental validation phase,demonstrating the high accuracy of the dynamic model and the parameter identification method for the robotic *** study presents a dynamic modeling and parameter identification method for coupled-drive joint robotic arms,thereby establishing a foundation for motion control in humanoid nursing robots.

关键词： Nursing-care robot Coupled-drive joint Dynamic modeling Parameter identification

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Correcting of the unexpected localization measurement for indoor automatic mobile robot transportation based on a neural network

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Transportation Safety and Environment 2024年第2期6卷 24-35页

作者： Jiahao Huang Steffen Jung inger Hui Liu Kerstin Thurow Center for Life Science Automation(CELISCA) University of RostockRostock 18119Germany Institute of Automation University of RostockRostock 18119Germany Institute of Artificial Intelligence&Robotics(IAIR) School of Traffic&Transportation EngineeringCentral South UniversityChangsha 410075HunanChina

The increasing use of mobile robots in laboratory settings has led to a higher degree of laboratory ***,when mobile robots move in laboratory environments,mechanical errors,environmental disturbances and signal interruptions are *** can compromise the accuracy of the robot’s localization,which is crucial for the safety of staff,robots and the laboratory.A novel time-series predicting model based on the data processing method is proposed to handle the unexpected localization measurement of mobile robots in laboratory *** proposed model serves as an auxiliary localization system that can accurately correct unexpected localization errors by relying solely on the historical data of mobile *** experimental results demonstrate the effectiveness of this proposed method.

关键词： mobile robots laboratory automation indoor localization neural network

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Evaluation of different crosslinking methods in altering the properties of extrusion-printed chitosan-basedmulti-material hydrogel composites

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Bio-Design and Manufacturing 2023年第2期6卷 150-173页

作者： Suihong Liu Haiguang Zhang Tilman Ahlfeld David Kilian Yakui Liu Michael Gelinsky Qingxi Hu Centre for Translational Bone Joint and Soft Tissue ResearchFaculty of Medicine and University Hospital Carl Gustav CarusTechnische Universität Dresden01307 DresdenGermany Rapid Manufacturing Engineering Center School of Mechatronic Engineering and AutomationShanghai UniversityShanghai 200444China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and AutomationShanghai UniversityShanghai 200444China National Demonstration Center for Experimental Engineering Training Education Shanghai UniversityShanghai 200444China

Three-dimensional printing technologies exhibit tremendous potential in the advancing fields of tissue engineering and regenerative medicine due to the precise spatial control over depositing the *** their widespread utilization and numerous advantages,the development of suitable novel biomaterials for extrusion-based 3D printing of scaffolds that support cell attachment,proliferation,and vascularization remains a ***-material composite hydrogels present incredible potential in this ***,in this work,a multi-material composite hydrogel with a promising formulation of chitosan/gelatin functionalized with egg white was developed,which provides good printability and shape *** addition,a series of comparative analyses of different crosslinking agents and processes based on tripolyphosphate(TPP),genipin(GP),and glutaraldehyde(GTA)were investigated and compared to select the ideal crosslinking strategy to enhance the physicochemical and biological properties of the fabricated *** of the results indicate that the composite hydrogel and the resulting scaffolds utilizing TPP crosslinking have great potential in tissue engineering,especially for supporting neo-vessel growth into the scaffold and promoting angiogenesis within engineered tissues.

关键词： Multi-material composite hydrogel Crosslinking mechanism Chitosan Gelatin Egg white 3D printing

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Uncertain Pushing Adaptive Coordinated Control for the Human-Exoskeleton-Walker System

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IEEE robotics and automation Letters 2025年第7期10卷 7086-7093页

作者： Xinhao Zhang Chen Yang Jingting Zhang Chaobin Zou Guangkui Song Rui Huang Muhammad Umar Farooq Hong Cheng Center for Robotics School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

Lower Limb Exoskeletons are potential in the gait training for patients with gait disorders. For patients in the early rehabilitation stages with weak upper limb strength, it is challenge to keep balance by themselves only. A mobile robotic walker is helpful to maintain the walking balance, with the help of a physician pushing behind to keep a forward walking associated with the exoskeleton. However, since the gait patterns are varying with different training tasks, how to ensure a coordinated movement between the exoskeleton and the mobile robotic walker is challenge, especially with the uncertain pushing applied by the physician. In this letter, the Uncertain Pushing Adaptive Coordinated Control (UP-ACC) approach is proposed to solve the problem, which consists of two contributions. Based on the decoupled simplified dynamics, the center of mass trajectories and footstep placements are generated through linear model predictive control. In addition, the optimal footstep placement is used for the human-like gait planning by introducing the phases of heel-strike and toe-off. The proposed method has been verified in the robot simulation platform CoppeliaSim, and the experimental results indicate its effectiveness in generating coordinated motion and human-like gait patterns for the human-exoskeleton-walker system with the external pushing from 0 to 150 N and walking speed from 0.2 m/s to 0.6 m/s.

关键词： Legged locomotion Robot kinematics Mathematical models Exoskeletons Training Kinematics Dynamics Trajectory Planning Medical services

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AI-Powered Real-Time Detection of Wheel Defects in Railways Using Yolov8 7

AI-Powered Real-Time Detection of Wheel Defects in Railways ...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Shaikh, Muhammad Zakir Baro, Enrique Nava Baloch, Yaqoob Ali Hussain, Tanweer Uqaili, Mohammad Aslam Chowdhry, Bhawani Shankar School of Industrial Engineering University of Malaga Malaga Spain Jamshoro Pakistan Universidad de Malaga Departamento de Ingeniería de Comunicaciones Campus de Teatinos Malaga Spain National Center of Robotics and Automation Cms Lab Jamshoro Pakistan Mehran University of Engineering and Technology National Center of Robotics and Automation Cms Lab Department of Electrical Engineering Jamshoro Pakistan

ISBN: (纸本)9798331534271

Railway wheels are one of the most critical components of the railway infrastructure, functioning as the load carrier. They are exposed to various forms of damage, caused due to intense sliding friction and inadequate inspection procedures. This research introduces a real-time wheel fault detection system using YOLOv8, leveraging computer vision and AI-based object detection. The main contribution of this research is the utilization of a custom data acquisition setup for capturing wheel images and detecting faults with high accuracy and speed. Our results demonstrate an overall F1 score of 0.80, with precision and recall curves indicating strong wheel detection and moderate defect detection capabilities. This approach aims the reduction of inspection costs and the improvement of railway safety through automation and digitization. © 2024 IEEE.

关键词： Railroads

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Predictive Maintenance in Urban Railway Systems Using Machine Learning Models 7

Predictive Maintenance in Urban Railway Systems Using Machin...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Shaikh, Muhammad Zakir Ali, Sahil Ali, Sara Baro, Enrique Nava Baloch, Yaqoob Ali Chowdhry, Bhawani Shankar Jamshoro Pakistan School of Industrial Engineering University of Malaga Malaga Spain National Center of Robotics and Automation Cms Lab Jamshoro Pakistan Universidad de Malaga Departamento de Ingeniería de Comunicaciones Campus de Teatinos Malaga Spain National Center for Robotics Automation and Artificial Intelligence Jamshoro Pakistan

ISBN: (纸本)9798331534271

The Air Production Unit (APU) is responsible for managing the load in the metro train by making sure that the load is evenly distributed on the wheels despite passenger congestion. The failure of APU results in a complete halt of operations. Therefore, it is pivotal to timely maintain the APU system. In this paper, a predictive maintenance (PdM) approach has been suggested using multiple state-of-the-art machine learning algorithms to predict failure in the APU. Sensor parameters that lead to failures in APUs of the electric trains were identified from the MetroPT train dataset by employing a variety of baseline models including linear regression (LR), decision tree regressor (DTR), random forest regressor (RFR), gradient boosting regressor (GBR) and XGBoost Regressor (XGBR) models. This will significantly reduce both the operational cost as well as the downtime of the trains. It will also help in the identification of faulty parts at a faster rate and reduce the failure rate of urban transportation systems. © 2024 IEEE.

关键词： Predictive maintenance

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UAV Wheat Rust Detection based on FasterNet-YOLOv8

UAV Wheat Rust Detection based on FasterNet-YOLOv8

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Liu, Huaiyu Zhang, Yueyuan Liu, Shuguang Zhao, Mengyang Sun, Lining Soochow University School of Mechanical and Electrical Engineering Department of Automation Engineering Suzhou215137 China Soochow University Robotics and Microsystems Center School of Mechanical and Electrical Engineering Suzhou215137 China

ISBN: (纸本)9798350325706

This paper introduces a UAV-based wheat rust detection system employing deep learning techniques. To address the limitations of traditional wheat rust disease detection methods, such as time and labor-intensive processes, lack of real-time monitoring, and excessive pesticide usage, In this paper, the YOLOv8 algorithm, representing the forefront of object detection methods is chosen for UAV wheat rust detection. We train the model on a prepared dataset. Subsequently, we evaluated 5 models, including YOLOv5, and YOLOv8, at two different scales (n, s) based on average precision (mAP@.5), precision, recall, and FPS. The experimental outcomes establish the superiority of the enhanced FasterNet-YOLOv8 model over YOLOv8 and YOLOv5 across all metrics, encompassing detection accuracy and processing speed. These findings affirm the viability of our suggested model within the UAV wheat rust monitoring system and underscore the efficacy of the model enhancements. © 2023 IEEE.

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Submegahertz Nucleation of Plasmonic Vapor Microbubbles near a Solid Vertical Boundary

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Physical Review Letters 2024年第6期133卷 064001-064001页

作者： Fulong Wang (王福龙) Zhibin Hu Binglin Zeng Chenliang Xia Lihua Dong Huimin Wang Lijun Yang Yuliang Wang (王玉亮) Robotics Institute School of Mechanical Engineering and Automation National Center for Materials Service Safety School of Astronautics Ningbo Institute of Technology

Laser triggered and photothermally induced vapor bubbles have emerged as promising approaches to facilitate optomechanical energy conversion for numerous applications in microfluidics and nanofluidics. Here, we report an observation of spontaneously triggered periodic nucleation of plasmonic vapor bubbles near a rigid sidewall with readily tuned nucleation frequency from 0.8 kHz to over 200 kHz. The detailed collapsing process of the vapor bubbles was experimentally and numerically investigated. We find that the lateral migration of residual bubbles toward the sidewall refreshes the laser spot area, terminates the subsequent steady bubble growth, and leads to the repeatable bubble nucleation. A mathematic model regarding the Kelvin impulses was derived. It shows that the competition between the rigid boundary induced Bjerknes force and laser irradiation caused thermal Marangoni force on collapsing bubbles governs the process. The model also leads to a criterion of γζ<0.34 for repeatable bubble nucleation, where γ is the normalized distance and ζ thermal Marangoni coefficient. This study demonstrates nucleation of violent vapor bubbles at extreme high frequencies, providing an approach to remotely realize strong localized flows in microfluidics and nanofluidics.

关键词： Bubble dynamics Drop or bubble formation Drops & bubbles Laser-induced cavitation Lasers Metals Microfluidic devices Nanofluidic devices Nanoparticles Water

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