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arXiv 2024年

作者： Nohooji, Hamed Rahimi Voos, Holger Automation Robotics Research Group University of Luxembourg Luxembourg Department of Engineering University of Luxembourg Luxembourg

This paper introduces a novel Nussbaum function-based PID control for robotic manipulators. The integration of the Nussbaum function into the PID framework provides a solution with a simple structure that effectively tackles the challenge of unknown control directions. Stability is achieved through a combination of neural network-based estimation and Lyapunov analysis, facilitating automatic gain adjustment without the need for system dynamics. Our approach offers a gain determination with minimum parameter requirements, significantly reducing the complexity and enhancing the efficiency of robotic manipulator control. The paper guarantees that all signals within the closed-loop system remain bounded. Lastly, numerical simulations validate the theoretical framework, confirming the effectiveness of the proposed control strategy in enhancing robotic manipulator control. © 2024, CC BY.

关键词： Three term control systems

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Smart Manufacturing Integration of Heterogeneous Information Systems via OPC UA. A Case Study

Smart Manufacturing Integration of Heterogeneous Information...

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2024 International Conference Automatics and Informatics, ICAI 2024

作者： Vasilev, Plamen Boneva, Ani Ivanova, Veronika Tsvetanov, Tsetso University of Chemical Technology and Metallurgy Department of Industrial Automation Sofia Bulgaria Institute of Information and Communication Technologies Bulgarian Academy of Sciences Department of Communication and Computer Systems Sofia Bulgaria Institute of Robotics Bulgaria Academy of Sciences Department of Robotized and Mechatronics Intelligent Systems Sofia Bulgaria

ISBN: (纸本)9798350353907

Modern automation, digitalization, virtualization, and intelligent production impose new criteria and approaches to process management. The operation and performance of any system and device depend on how it is managed. The article describes Smart Manufacturing Integration of Heterogeneous Information Systems via OPC UA. The main goal is the development of heterogeneous information systems with real applications, with which reliable and up-to-date information can be exchanged between systems of different hierarchical levels. The goal was achieved with the following steps i) building the research infrastructure ii) analyzing the information compatibility and challenges in integrating systems from different hierarchical levels, and iii) implementing the integration by means of the OPC-UA standard communication protocol. The setup has been tested under laboratory conditions. The obtained results are shown. © 2024 IEEE.

关键词： Smart manufacturing

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Real-Time Geometric Joint Uncertainty Tracking for Surgical automation on the dVRK System

Real-Time Geometric Joint Uncertainty Tracking for Surgical ...

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IEEE International Conference on robotics and Biomimetics

作者： Bin Li Hongbin Lin Fangxun Zhong Yunhui Liu The Department of Mechanical and Automation Engineering T Stone Robotics Institute The Chinese University of Hong Kong

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Accurate control of surgical robotics under uncertainty is crucial for achieving surgical autonomy. This uncertainty arises from sensor or model inaccuracies in surgical platforms, e.g., dVRK system, where joint bias in positioning and complex transmission effects caused by backlash and cable stretch. Previous approaches usually rely on depth sensors or additional offline calibration steps, making them difficult to deploy in real-world laparoscopy surgeries. In this paper, we propose a real-time geometric approach for calibrating joint errors on-the-fly combined with geometric features. An efficient visual detector is employed to identify the shaft mask and wrist keypoints. Based on the extracted features, we introduce a geometric model to recover the shaft axis pose and determine the first two joints uncertainty. Additionally, we develop a geometric model for wrist joints in projection space, calibrating remaining joint uncertainty through spatial geometry and the analytical structure of dVRK platform. Experimental results in simulation show that our approach significantly reduces joint error from 15° to 0.02°, with end-effector pose accuracy improving from centimeter to sub-millimeter level. This greatly enhances the accuracy and success rate of surgical automation. We also demonstrate robust control performance in diverse surgical tasks, highlighting the effectiveness of our geometric model in achieving accurate control despite joint offsets.

关键词： Shafts Wrist Uncertainty Accuracy automation Geometric modeling Robot sensing systems Feature extraction Real-time systems Calibration

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Advances in self-powered sports monitoring sensors based on triboelectric nanogenerators

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Journal of Energy Chemistry 2023年第4期79卷 477-488页

作者： Fengxin Sun Yongsheng Zhu Changjun Jia Tianming Zhao Liang Chu Yupeng Mao Physical Education Department Northeastern UniversityShenyang 110819LiaoningChina Institute of Carbon Neutrality and New Energy&School of Electronics and Information Hangzhou Dianzi UniversityHangzhou 310018ZhejiangChina School of Strength and Conditioning Training Beijing Sport UniversityBeijing 100084China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of ScienceShenyang 110016LiaoningChina Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110016LiaoningChina

The new era of the internet of things brings great opportunities to the field of intelligent *** collection and analysis of sports data are becoming more intelligent driven by the widely-distributed sensing network *** nanogenerators(TENGs)can collect and convert energy as selfpowered sensors,overcoming the limitations of external power supply,frequent power replacement and high-cost ***,we introduce the working modes and principles of TENGs,and then summarize the recent advances in self-powered sports monitoring sensors driven by TENGs in sports equipment facilities,wearable equipment and competitive sports *** discuss the existing issues,i.e.,device stability,material sustainability,device design rationality,textile TENG cleanability,sports sensors safety,kinds and manufacturing of sports sensors,and data collection comprehensiveness,and finally,propose the *** work has practical significance to the current TENG applications in sports monitoring,and TENG-based sensing technology will have a broad prospect in the field of intelligent sports in the future.

关键词： Triboelectric nanogenerator Wearable energy sensors Sport monitoring

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Artificial Intelligence Application Solutions to Improve the Quality of Heating, Ventilation, and Air Conditioning System

Artificial Intelligence Application Solutions to Improve the...

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IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW)

作者： Duc M. Nguyen Mikhail P. Belov Alexander M. Belov Department of Robotics and Industrial Automation Saint-Petersburg Electrotechnical University “LETI” Saint Petersburg Russia

Artificial intelligence (AI) is increasingly developing and being widely applied in all areas of life, such as: education, healthcare, transportation, production. The content of this article refers to the application of AI to improve the quality and efficiency of the control and operation of the Heating, Ventilation, and Air Conditioning (HVAC) system while also helping to reduce the system’s energy consumption.

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Deep Learning and Ensemble Models for Accurate Heart Rate Estimation from PPG

Deep Learning and Ensemble Models for Accurate Heart Rate Es...

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IEEE India Conference (INDICON)

作者： Dhatri Shankari T M Rashmi Ugarakhod Department of ECE Centre for Robotics Automation and Intelligent Systems (cRAIS) PES University Bangalore India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Photoplethysmography (PPG) is a non-invasive, optical technique for measuring blood volume changes in peripheral tissues. In this paper, deep learning and ensemble methods are explored for an accurate estimation of heart rate directly from the PPG signals. Different models such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Hybrid CNN-LSTM, Siamese network, Extreme Gradient Boosting Trees (XGBoost), and hybrid models of XGBoost-CNN-LSTM, have been evaluated for their performance using the PPG-DaLiA dataset. The performance of these models were evaluated using mean absolute error (MAE), standard deviation (SD) and the Percentage of Data within an Error range (PDE). The results show that the proposed CNN-LSTM architecture is effective for practical heart rate prediction. The paper also provides valuable insights about the strengths and weaknesses of these models and their possible applications in wearable health monitoring.

关键词： Heart rate Deep learning Accuracy Estimation Predictive models Optical variables measurement Photoplethysmography Convolutional neural networks Ensemble learning Reliability

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Safe and Individualized Motion Planning for Upper-limb Exoskeleton Robots Using Human Demonstration and Interactive Learning

Safe and Individualized Motion Planning for Upper-limb Exosk...

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IEEE International Conference on robotics and automation (ICRA)

作者： Yu Chen Gong Chen Jing Ye Xiangjun Qiu Xiang Li Department of Automation Tsinghua University China Shenzhen MileBot Robotics Co. Ltd China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

A typical application of upper-limb exoskeleton robots is deployment in rehabilitation training, helping patients to regain manipulative abilities. However, as the patient is not always capable of following the robot, safety issues may arise during the training. Due to the bias in different patients, an individualized scheme is also important to ensure that the robot suits the specific conditions (e.g., movement habits) of a patient, hence guaranteeing effectiveness. To fulfill this requirement, this paper proposes a new motion planning scheme for upper-limb exoskeleton robots, which drives the robot to provide customized, safe, and individualized assistance using both human demonstration and interactive learning. Specifically, the robot first learns from a group of healthy subjects to generate a reference motion trajectory via probabilistic movement primitives (ProMP). It then learns from the patient during the training process to further shape the trajectory inside a moving safe region. The interactive data is fed back into the ProMP iteratively to enhance the individualized features for as long as the training process continues. The robot tracks the individualized trajectory under a variable impedance model to realize the assistance. Finally, the experimental results are presented in this paper to validate the proposed control scheme.

关键词： Training Tracking Shape Exoskeletons Trajectory Safety Planning

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Developing a Neural Network Structure in Controlling Air Conditioning Systems

Developing a Neural Network Structure in Controlling Air Con...

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International Conference on Soft Computing and Measurements (SCM)

作者： Duc M. Nguyen Mihail P. Belov Alexander M. Belov Department of Robotics and Industrial Automation Saint-Petersburg Electrotechnical University “LETI” Saint Petersburg Russia

ISBN: (数字)9798350363708

ISBN: (纸本)9798350363715

The air conditioning system is a stochastic, non-stationary, multi-connected, nonlinear industrial system. Using traditional management methods for such an object do not provide the required quality of regulation, as maintaining microclimate parameters (temperature, humidity in a certain space) with high precision. To control the multi-connected output parameters of the air conditioning system, an artificial neural network (ANN) is proposed. The type of neural network is Multi-Layer Perceptron (MLP). The neural network control algorithm is based on correction of weight coefficients using the gradient descent and back propagation algorithm.

关键词： Air conditioning Backpropagation HVAC Process control Artificial neural networks Humidity Aerospace electronics

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Preface: Third International Conference on Advancements in automation, robotics and Sensing (ICAARS 2022)

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AIP Conference Proceedings 2024年第1期3035卷 010001-010001页

作者： Vinod, B. Subashini, G. Suresh, M. Department of Robotics and Automation Engineering PSG College of Technology Coimbatore India

Preface: Third International Conference on Advancements in automation, robotics and Sensing (ICAARS 2022), AIP Conference Proceedings, Volume 3035, Issue 1, 4 M

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Lung Sound Based Pulmonary Disease Classification Using Deep Learning

Lung Sound Based Pulmonary Disease Classification Using Deep...

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International Conference on Futuristic Technologies (INCOFT)

作者： Madhu R. Koravanavar C. B. Kolanur Department of Automation and Robotics KLE Technological University Hubballi Karnataka India

Respiratory infections are disorders that damage the lungs and airways and make it difficult for patients to breathe. Any part of the respiratory system has the potential to become infected or sick, which could have a variety of negative effects. Early detection of lung disorders results in a person's successful treatment. Using electronically recorded lung sounds, pulmonary diseases can be diagnosed. MelFrequency Cepstral Coefficient (MFCC), one of the Librosa machine learning library features, is used to check that such a brief speech segment is sufficiently steady to permit effective modeling. In this research, Librosa is one of the better preprocessing techniques used when working with audio data. The CNN model is applied to the collected dataset and obtained a train accuracy of 95% and a test accuracy of 85% when trained on extracted MFCC features. And also used a number of performance evaluation indicators, including as F1score, accuracy, precision, and recall. The outcomes of this research enable the use of deep learning models in clinical settings to improve doctors' judgment when identifying lung disorders.

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