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IEEE International Workshop on intelligent Robots and Systems (IROS)

作者： I.H. Suh H.J. Yeo J.H. Kim J.S. Ryoo S.R. Oh C.W. Lee B.H. Lee Intelligent Control and Robotics Laboratory Department of Electronics Engineering Hanyang University Ansan Kyunggi South Korea Advanced Robotics Research Center KIST Seoul South Korea Department of Control and Instrumentation Engineering Seoul National University South Korea

This paper presents a design experience of a supervisory control system for coordination of multiple robotic devices. To effectively program job commands, a Petri net-type graphical robot language(PGRL) is proposed, where some functions for coordination among tasks such as concurrency and synchronization, can be easily programmed. Each task of PGRL is described by employing formal model languages, which are composed of three modules, sensory, data handling, and action module. It is expected that by using our proposed PGRL and formal model languages, one can efficiently describe a job or task, and hence can easily operate a complex real-time concurrent system. The proposed control system has been implemented by using VME-based 32-bit microprocessor boards and a real-time multitasking operating system (VxWorks), and is shown to successfully work for robotic jobs.

关键词： Supervisory control Robot kinematics Robot sensing systems Real time systems Concurrent computing Data handling control systems Microprocessors Multitasking Operating systems

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Anomaly Detection of Marine Diesel Engine Valve System based on Mahalanobis Distance

Anomaly Detection of Marine Diesel Engine Valve System based...

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2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020

作者： Sun, Yue Xu, Aidong Wang, Kai Jia, Shuli Guo, Haifeng Han, Xiaojia Laboratory of Industrial Control Network and System State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Shenyang China Automation Engineering Department Shanghai Marine Diesel Engine Research Institute Shanghai China

ISBN: (纸本)9781728159454

Diesel engine is an important energy equipment. The valve system is the core component of diesel engine with high failure rate. It is of great significance to study the abnormal detection method of the diesel engine valve system and realize the failure warning before the failure occurs for avoiding the unexpected shutdown of the diesel engine. In this paper, an anomaly detection method of marine diesel engine valve system based on Mahalanobis distance (MD) is proposed. The failure mode of diesel engine system is analyzed. Three thermal parameters are selected as monitoring parameters for the failure mode of roller fragmentation. Fault features are extracted by MD method and the failure threshold is determined by Box-Cox transformation. Finally, the effectiveness of the proposed method is verified by the durability test of diesel engine. © 2020 IEEE.

关键词： Diesel engines

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Cognitive Granular-Based Path Planning and Tracking for intelligent Vehicle with Multi-Segment Bezier Curve Stitching

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intelligent Automation & Soft Computing 2023年第7期37卷 385-400页

作者： Xudong Wang Xueshuai Qin Huiyan Zhang Luis Ismael Minchala Chongqing Key Laboratory of Manufacturing Equipment Mechanism Design and Control Chongqing Technology and Business UniversityChongqing400067China National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business UniversityChongqing400067China Department of Electrical Electronics and Telecommunications Engineering University of CuencaCuencaEcuador

Unmanned vehicles are currently facing many difficulties and challenges in improving safety performance when running in complex urban road traffic environments,such as low intelligence and poor comfort perfor-mance in the driving *** real-time performance of vehicles and the comfort requirements of passengers in path planning and tracking control of unmanned vehicles have attracted more and more *** this paper,in order to improve the real-time performance of the autonomous vehicle planning module and the comfort requirements of passengers that a local granular-based path planning method and tracking control based on multi-segment Bezier curve splicing and model predictive control theory are ***,the maximum trajectory curvature satisfying ride comfort is regarded as an important constraint condition,and the corresponding curvature threshold is utilized to calculate the control points of Bezier *** using low-order interpolation curve splicing,the planning computation is reduced,and the real-time performance of planning is improved,com-pared with one-segment curve fitting ***,the comfort performance of the planned path is reflected intuitively by the curvature information of the ***,the effectiveness of the proposed control method is verified by the co-simulation platform built by MATLAB/Simulink and *** simulation results show that the path tracking effect of multi-segment Bezier curve fitting is better than that of high-order curve planning in terms of real-time performance and comfort.

关键词： intelligent vehicle data analysis techniques path planning tracking control

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Classification Accuracy of an Imagined-Movements Mental Task Set for Brain-Machine Interface

Classification Accuracy of an Imagined-Movements Mental Task...

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International Conference on Bioinformatics and Biomedical engineering (ICBBE)

作者： Michael Lochinvar S. Abundo Eliezer A. Marco Gino Francisco R. Mempin Marc Caesar R. Talampas Luis S. Sison Instrumentation Robotics and Control Laboratory Department of Electrical and Electronics Engineering University of Philippines Diliman Philippines

We study the classification accuracy of a neural network (NN) classifier for a Brain-Machine Interface (BMI) system that uses an imagined-movements mental task set and compare the performance of the classifier when the user is subjected to a non-moving (static) reference frame (SRF) and to a moving (dynamic) reference frame (DRF) while performing the mental tasks. We use the band powers and power differences of electroencephalogram (EEG) signals recorded from 8 surface electrodes. Results show that tasks involving imagined movements are the most immune to the SRF-DRF switch but may not be the most appropriate BMI protocol for BMI classification.

关键词： Neural networks Electroencephalography Mobile robots Brain modeling Electrodes Robot control control systems Biological neural networks Switches Protocols

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Telemetry Data-based Spacecraft Anomaly Detection Using Generative Adversarial Networks 1

Telemetry Data-based Spacecraft Anomaly Detection Using Gene...

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1st International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020

作者： Song, Yue Yu, Jinsong Tang, Diyin Han, Danyang Wang, Sen Beihang University School of Automation Science and Electrical Engineering Department Beijing China Beihang University School of Instrumentation Science and Opto-electronics Engineering Beijing China Beijing Aerospace Automatic Control Institute National Laboratory of Science and Technology on Aerospace Intelligent Control Beijing China

ISBN: (纸本)9781728192772

The telemetry data of spacecraft is an ultra-high dimensional time series used to indicate on-orbit operation status, and anomaly detection can effectively ensure safety and reliability. Aiming at the characteristics and complex correlation of high dimensional telemetry data, this paper proposes a novel anomaly detection method based on Generative Adversarial Networks (GAN) for telemetry data anomaly detection. Instead of treating each variable independently, our proposed method captures the latent representation amongst multi-dimensional time series. For normal data, the GAN-based anomaly detection method can obtain a reconstructed time series similar to the original time series, learning the probability distribution model of normal data. For abnormal data, the reconstructed time series deviates greatly from the original time series. In the GAN framework, we use Long Short-Term Memory (LSTM) as the network structure of generator for time series reconstruction and discriminator for calculating the probability of being the real time series, which can learn the temporal features of telemetry data. We also propose a novel anomaly score called GDScore, which comprehensively considers the reconstruction error of the generator and the output of the discriminator. We conduct experiments with two telemetry datasets, which verifies that our proposed GAN-based anomaly detection method can effectively detect outliers. © 2020 IEEE.

关键词： Time series

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Towards Efficient Path Finding and Moving Stability-Focused Trajectory Planning for Mobile Robots

Towards Efficient Path Finding and Moving Stability-Focused ...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Hou, Muyang Lei, Zhenya Hu, Haidong Wang, Zhong Wang, Yong Xie, Le Wang, Hesheng Shanghai Jiao Tong University Department of Automation Shanghai200240 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Beijing Institute of Control Engineering Beijing100190 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Department of Automation Shanghai200240 China

ISBN: (纸本)9781665481090

Nowadays, mobile robots play an important role in a variety of service scenarios. They need to plan and track trajectories to accomplish tasks such as delivery or guided tours. In such tasks, there are two remaining challenges: on one hand, the time and memory consumption of present path planners are still significant;on the other hand, service robots, which have a higher center of gravity, demand greater moving stability than that of current trajectory planners provide. To address these two challenges. Firstly, we propose a safety boundary first A∗ which fully utilizes environmental obstacle information to create a safety boundary and searches in it to quickly find an initial path. Secondly, we formulate the trajectory optimization problem as a nonlinear optimization problem, where the smoothness, safety, feasibility, and moving stability of the robot are taken into account. Furthermore, to constrain the robot's lateral acceleration, we design a time allocation algorithm based on non-uniform B-spline, enhancing the quality of the resulting trajectory. Simulations and real-world experiments demonstrate that our algorithms significantly improve path search efficiency, enhance moving stability, reduce the difficulty of tracking, and improve the quality of task completion. © 2024 IEEE.

关键词： Trajectories

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Learning Cooperative Object Pushing with Variable Contact Point

Learning Cooperative Object Pushing with Variable Contact Po...

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2007 IEEE/ASME International Conference on Advanced intelligent Mechatronics, Pages 805-1210

作者： Abdol Hossein Aminaiee Majid Nili Ahmadabadi Robotics and AI Laboratory Control and Intelligent Processing Center of Excellence Department of Electerical and Computer Engineering University of Tehran

ISBN: (纸本)9781424412631;1424412633

Learning in multi-agent environments where each agent's action directly affects other agents would be an important matter and a complicated task. To reduce the learning time and simplifying the learning process, it is suitable to learn individual skills and then provide cooperation and coordination utilizing the learned individual skills. In the approach proposed in this paper, agents benefit from their individual knowledge obtained in the individual learning phase to cooperate with other agents. Cooperative object pushing system is used as a testbed to our proposed method where cooperation and coordination between agents are needed in these systems. Agents independently learn to push the object to the target by using the proposed fuzzy reinforcement learning method. Agents attain their cooperative behaviors properly making use of the coded knowledge in the Q-table. Simulation and experimental results show that by interpreting the knowledge in the Q-table, agents can achieve high level behaviors with a high degree of cooperation.

关键词： Agents Learning Point contacts pushing variable contact

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Manufacturing Systems control Design 1

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丛书名： Advances in Industrial control

1000年

作者： Stjepan Bogdan Zdenko Kovačić Frank L. Lewis José Mireles

ISBN: (数字)9781846283345

ISBN: (纸本)9781852339821;9781849969895

The series Advances in Industrial control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. In some areas of manufacturing, the elements of a flexible manufacturing system form the key components of the process line. These key components are four-fold: a set of programmable robots and machines, an automated materia- handling system that allows parts to be freely routed and re-routed, a buffer storage system where parts and partly-assembled components can wait until required for further processing and assembly and finally, a supervisory control system. The technology employed to coordinate and control all these components as a working system is usually based on programmable logic controllers. The use of this automation hardware and software in manufacturing is designed to yield significant cost reductions and to enhance quality.

关键词： control and Systems Theory Industrial and Production engineering Manufacturing, Machines, Tools, Processes Simulation and Modeling Linear and Multilinear Algebras, Matrix Theory Industrial Organization

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells 32

Application of Deep Learning Method to Estimate Bottomhole P...

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32nd IEEE International Symposium on Industrial electronics, ISIE 2023

作者： Cheng, Haibo Li, Shichao Zeng, Peng Vyatkin, Valeriy State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Luleå University of Technology Electrical and Space Engineering Department of Computer Science Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350399714

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry. © 2023 IEEE.

关键词： Long short-term memory

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Explicit fuzzy force control of industrial manipulators with position servo drives

Explicit fuzzy force control of industrial manipulators with...

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IEEE International Workshop on intelligent Robots and Systems (IROS)

作者： I.H. Suh K.S. Eom H.J. Yeo B.H. Kang S.-R. Oh B.H. Lee Intelligent Control and Robotics Laboratory Department of Electrvnics Engineering Hanyang University South Korea Samsung Data System Company Limited South Korea Division of Electronics and Infonmation technology KIST Seoul South Korea Robotics and Intelligent Systems Laboratory Department of Control and Instrumentation Engineering Seoul National University South Korea

In this paper, a fuzzy force control algorithm is suggested for commercialized industrial robots equipped with the position servo drives, where control rules of the proposed fuzzy controller are changed according to the magnitude of environmental stiffness in such a way that good force response is maintained regardless of changes of environmental stiffness. Specifically, some fuzzy control rules are designed for several representative environmental stiffness values, and then a control action for a given arbitrary environmental stiffness value is decided by a fuzzy interpolation method. To show the validity of the proposed fuzzy controller, several experimental results are illustrated, where a 5-axis articulated robot manipulator equipped with the wrist force/torque sensor system and our prototype dual robot controller are employed.< >

关键词： Fuzzy control Force control Industrial control Manipulators Torque control control systems Robot sensing systems Commercialization Service robots Servomechanisms

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