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检索条件"任意字段=25th International Conference on Methods and Models in Automation and Robotics"

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Atr loitmBaeo Deluy Trangulation and Its Improvement

Atr loitmBaeo Deluy Trangulation and Its Improvement

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IEEE international conference on Communication and Information Systems (ICCIS)

作者： Wenyu Li Senchun Chai School of Automation Beijing Institute of Technology Beijing China

ISBN: (数字)9798331531171

ISBN: (纸本)9798331531188

Path planning is an important part of robotics research. A * algorithm and RRT algorithm are very common algorithms in path planning. Each of these algorithms has its own advantages, but there is still room for improvement in planning time, path cost, and adaptability to different environment. In this paper, we propose a map processing method based on Delaunay triangulation, while applying the strategy of obstacle expansion and compensated sampling, and the A * algorithm is used on this basis. Finally, we have improved the path given by the A * algorithm with straight-first strategy. Simulations show that the A * algorithm based on Delaunay triangulation can plan paths efficiently in different environments.

关键词： Adaptation models Costs Simulation Optimization methods Path planning Planning Robots Information systems

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Network Parameter Control in Cellular Networks through Graph-Based Multi-Agent Constrained Reinforcement Learning

Network Parameter Control in Cellular Networks through Graph...

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IEEE international conference on automation Science and Engineering (CASE)

作者： Albin Larsson Forsberg Alexandros Nikou Aneta Vulgarakis Feljan Jana Tumova Ericsson Research Research Area Artificial Intelligence (AI) Stockholm Sweden Department of Electrical Engineering and Computer Science Division of Robotics Perception and Learning KTH Stockholm Sweden

Cellular networks are growing in complexity at increasing speed and the geographical locations in which they are deployed in are getting denser. Traditional control methods fall short in providing a scalable and dynamic way of adapting the network to new conditions. Distributed multiagent reinforcement learning successfully addresses scalability problems seen in centralized approaches. the question of achieving learning with constraint satisfaction in distributed systems is still left unanswered in the state-of-the-art. In this work, we aim to perform distributed multi-agent constrained reinforcement learning in order to learn a policy online while satisfying imposed constraints. We use a coordination graph to model the interactions between agents and decompose the global value function. A conservative safety critic is trained in parallel to evaluate the safety of proposed actions. Our method allows for separate training of both the critic and the value network independently of each other, and hence offers flexibility in how and when to train the different models. the results are compared to a baseline using no safety critic. Simulations show that the agents succeed in learning a policy that can satisfy the constraints, while still maximizing the objective.

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Measuring and Modelling Delays in Robot Manipulators for Temporally Precise Control Using Machine Learning

Measuring and Modelling Delays in Robot Manipulators for Tem...

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international conference on Machine Learning and Applications (ICMLA)

作者： thomas Timm Andersen Heni Ben Amor Nils Axel Andersen Ole Ravn Department of Automation and Control DTU Electrical Engineering Technical University of Denmark Lyngby Denmark College of Computing Georgia Tech Institute for Robotics and Intelligent Machines Atlanta GA USA

Latencies and delays play an important role in temporally precise robot control. During dynamic tasks in particular, a robot has to account for inherent delays to reach manipulated objects in time. the different types of occurring delays are typically convoluted and thereby hard to measure and separate. In this paper, we present a data-driven methodology for separating and modelling inherent delays during robot control. We show how both actuation and response delays can be modelled using modern machine learning methods. the resulting models can be used to predict the delays as well as the uncertainty of the prediction. Experiments on two widely used robot platforms show significant actuation and response delays in standard control loops. Predictive models can, therefore, be used to reason about expected delays and improve temporal accuracy during control. the approach can easily be used on different robot platforms.

关键词： Delays Robot sensing systems Predictive models Robot control Solid modeling Service robots

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Research Progress in Cell Classification Detection Based on Deep Learning

Research Progress in Cell Classification Detection Based on ...

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IEEE international conference on Cyber Technology in automation, Control, and Intelligent Systems

作者： Dongze Gai Chuang Zhang Yiwei Zhang Jiaduo Guo Qi Zhang Hengshen Qin Dan Dang School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China School of Mechanical Engineering Shenyang Jianzhu University Shenyang China School of Science Shenyang Jianzhu University Shenyang China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

this paper reviews the application of deep learning in cell classification detection, highlights the importance of convolutional neural networks in image analysis, and discusses the application of image preprocessing, feature extraction and deep learning models. Despite the progress made by deep learning techniques in improving cell classification accuracy, challenges such as data quality, model generalization and interpretability remain. this article suggests future research directions, including combining medical knowledge with deep learning and developing multimodal learning to improve the accuracy and efficiency of cell detection. Meanwhile, this article also explores the potential of label-free cell classification methods combining optically induced optically-induced dielectrophoresis (ODEP) techniques with deep learning to achieve cell classification without labeling, providing a new perspective on cell analysis. optically induced.

关键词： Deep learning YOLO Biomedical optical imaging Accuracy Image analysis Optical imaging Feature extraction Labeling Image preprocessing Medical diagnostic imaging

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Exploring Innovations in Autonomous robotics and Mechatronics: Application in Manufacturing and Healthcare

Exploring Innovations in Autonomous Robotics and Mechatronic...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： T.Grace Shalini L. Ganeshabu Priyanka Chemudugunta Nagendar Yamsani Ch.Ashok Kumar A. Athiraja Department of Computational Intelligence School of Computing SRM Institute of Science and Technology (SRMIST)Kattankulathur Chengalpattu Tamil Nadu India Department of Robotics and Automation Rajalakshmi Engineering College Rajalakshmi Nagar Thandalam Mevalurkuppam Chennai Tamil Nadu India Department of Robotics and Automation Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India Department of School of Computer Science and Artificial Engineering SR University Warangal India Department of Mechanical Engineering Malla Reddy Engineering College Main Campus Maisammaguda Hyderabad Telangana India Department of CSE (AIML) Malla Reddy College of Engineering Secunderabad Telangana India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Autonomous robotics and mechatronics have drastically changed the manufacturing and healthcare sectors by increasing productivity, precision, and flexibility. this work addresses the pressing need for new methods to advance these sectors by merging intelligent algorithms with autonomous systems. A hybrid strategy was employed, combining Deep Reinforcement Learning (DRL) and Adaptive Control Systems (ACS), to develop and optimize robotic operations. Extensive studies were conducted to assess the success of this strategy, which demonstrated a 25% reduction in operating expenses and a 30% increase in production efficiency in manufacturing settings. the method produced a precision rate of about 99% in the healthcare industry, which is significantly better than the 93% average of current systems. It also reduced patient recovery times by an estimated 20%. these results highlight the improved performance and promise of ACS with DRL combination in autonomous robotics, which may have revolutionary implications for the manufacturing and healthcare industries. the success, highlighted by economic and medical benefits, demonstrates the efficacy of our methodology and provides a strong platform for future developments in autonomous robotics and mechatronics.

关键词： Industries Technological innovation Mechatronics Service robots Scalability Medical services Manufacturing Adaptive control Tuning Testing

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international conference on Control, automation and Systems ( ICCAS)

作者： Jinhoon Wang Woonghee Lee I Made Putra Arya Winata Junghyun Oh Department of Robotics Kwangwoon University Seoul Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

Active 3D reconstruction is a research field that focuses on planning paths to actively acquire data efficiently for 3D reconstruction. Among these, Next-Best-View (NBV) based path planning studies methods to select best viewpoints for 3D reconstruction. Recently, realistic modeling techniques such as Neural Radiance Fields and Gaussian Splatting have emerged, leading to active research on applying NBV to new 3D modeling methods. However, recent NBV techniques are designed to consider only uncertainty when selecting the best image from candidate images, which limits their efficiency in data acquisition. To overcome this limitation, we propose a similar image avoidance policy using SSIM, which avoids selecting images similar to those used in training. this approach prevents overfitting and effectively reduces the number of data used in training while acquiring the overall shape and surface information of the model with a small amount of data. We then verify the performance of the proposed method by comparing it to 3D models generated with random data and FisherRF using image quality metrics.

关键词： Training Image quality Solid modeling Surface reconstruction three-dimensional displays Uncertainty Shape Data models Image reconstruction Overfitting

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Temporal-Spectral Generative Adversarial Network based Multi-Level Consistency Learning for Epileptic Seizure Prediction

Temporal-Spectral Generative Adversarial Network based Multi...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Zhaoliang Xu Weigang Cui Yang Li Department of Automation Science and Electrical Engineering Beihang University Beijing China School of Engineering Medicine Beihang University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

Recently, deep learning techniques have been extensively explored and offer an efficient means for predicting epileptic seizures. However, due to the variability between different domains in the features of electroencephalogram (EEG) data, most of the existing methods only use the limited training data of the target subject, which is difficult to fully cover the data distribution and thus degrades the model performance. To solve this problem, a novel temporal-spectral generative adversarial network based multi-level consistency learning (TSGCN-MLCL) strategy is introduced in this paper. Specifically, the multi-level consistency learning (MLCL) strategy optimizes the seizure prediction model by training from three levels, i.e., inter-domain, intra-domain and inter-sample, which realizes the data distribution alignment of the source and target domain from multiple views. Second, considering the problem of limited labeled data in the target domain, we introduce the temporal-spectral generative adversarial network (TSGCN) to augment new training data, which generates high-quality simulated EEG samples according to the temporal-spectral distribution of real signals, and improves the finetuning process on the target domain. Our proposed approach attains a sensitivity of 96.7%, accompanied by a false prediction rate of 0.072 per hour, as demonstrated on the publicly available CHB-MIT epileptic seizure dataset. this performance surpasses that of current state-of-the-art studies, affirming the effectiveness of our methodology.

关键词： Training Sensitivity Service robots Training data Predictive models Generative adversarial networks Brain modeling Electroencephalography Data models Overfitting

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Research on Testing Method for Broken Rail Monitoring Equipment Based on Ensemble Learning

Research on Testing Method for Broken Rail Monitoring Equipm...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Qi Wang Congcong Liu Yulong Xing Yu Zhao Standards & Metrology Research Institute China Academy of Railway Sciences Corporation Limited China Railway Test & Certification Center Limited Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

Broken rail monitoring equipment is essential for ensuring the safety of train operations, and it is necessary to conduct functional and performance testing before the equipment is put into operation. Considering the safety and order of line operations, laboratory testing methods are typically used, employing simulation boards to mimic actual rails. Since the rail is an inductive element, its impedance characteristics are closely related to the frequency of the electrical signal. Accurately simulating the impedance transmission characteristics of the rail under different scenarios and conditions is vital to implementing these testing. To achieve this, we utilize real measured data of rail impedance under different scenarios and frequencies, and establish an equivalent model of rail impedance characteristics using ensemble learning algorithms. this model takes the application conditions of the broken rail monitoring equipment as input and determines the impedance parameters of the signal during rail transmission. Based on this, a rail simulation board can be designed, and a track circuit and broken rail monitoring equipment testing platform can be set up, enabling scientific and efficient testing in the laboratory.

关键词： Rails Data models Safety Frequency measurement Temperature control Impedance Ensemble learning Integrated circuit modeling Monitoring Testing

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Sleep Stage Classification Using EEG Signals: A Empirical Wavelet Transform-Based Decomposition Approach

Sleep Stage Classification Using EEG Signals: A Empirical Wa...

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international conference on Control, automation, robotics and Vision (ICARCV)

作者： Rajveer Singh Lalawat Varun Bajaj Prabin Kumar Padhy Department of Electronics and Communication Engineering Indian Institute of Information Technology Design and Manufacturing Jabalpur Jabalpur India Department of Electronics and Communication Maulana Azad National Institute of Technology Bhopal Bhopal India

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

the objective of signal processing using transformation techniques, is to accurately detect the frequency decomposition and extract small variations within the signals. Previous studies have explored various algorithms aiming to decompose signals and classify sleep stages based on EEG signals, offering valuable insights. However, there is still scope for improvement in handling highly non-stationary signals across all sleep stages. the article proposes employing spectrum partitioning from six sleep phases and the Empirical Wavelet Transform (EWT) to properly decompose extremely non-stationary EEG data. this approach uses an adaptive filter bank to control dynamic data in the frequency domain to accurately distinguish sleep stages. Our experiments demonstrate that, the Support Vector Machines (SVM) classifier outperforms Random Forest (RF) and k-Nearest Neighbors (KNN) classifiers, and it achieves notable classification accuracy for all sleep stages. this method enhances the performance of medical devices and benefits for their use in healthcare.

关键词： Support vector machines Wavelet transforms Sleep Signal processing algorithms Nearest neighbor methods Feature extraction Brain modeling Electroencephalography Data models Classification algorithms

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OPC UA Realization Of Cloud Cyber-Physical System

OPC UA Realization Of Cloud Cyber-Physical System

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IEEE international conference on Industrial Informatics (INDIN)

作者： Václav Jirkovský Petr Kadera Marek Obitko Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague Czech Republic Rockwell Automation Prague Czech Republic

Cloud cyber-physical systems are the natural evolution of embedded systems based on trends which may be observed in various domains. Cloud-based systems may be understood as a transformation of legacy system hardware and software architecture into more flexible, robust, powerful, and reliable solution. Cloud cyber-physical system (CPS) is the extension of the well-known CPS concept where complex computations may be moved to a cloud platform. We proposed the solution which benefits from utilization of OPC UA standard. this standard provides rich expressivity for data model design as well as for secure communication, data subscription, and triggering methods. the overall approach is demonstrated on proposed and implemented Semantic Big Data Historian which forms together with a cyber-physical system the implementation of cloud CPS concept.

关键词： Cyber-physical systems Sensors Actuators Cloud computing Memory Standards Data models

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