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Volume reduction design of a surface-mounted permanent magnet synchronous motor for traction motor of an electric-powered wheelchair for the lower body disabled

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Journal of Institute of Control, robotics and Systems 2021年第10期27卷 762-768页

作者： Son, Ji-Chang Lim, Dong-Kuk Park, Jongwoo Seo, Hyeon-Uk Department of Electrical Electronic and Computer Engineering University of Ulsan Korea Republic of Department of Robotics & Mechatronics Korea Institute of Machinery Materials Korea Republic of

This article proposes a novel surface-mounted permanent magnet synchronous motor for independent activity support robots for the lower body disabled. An improved design through electromagnetic field analysis was conducted for the purpose of reducing the stacking length and the volume of the motor. Moreover, prototype motor was manufactured and tested to validate the proposed design. As a result of two rounds of design and manufacturing, optimal design with 37.5 % reduced stacking length and 23 % reduced volume could be obtained. © ICROS 2021.

关键词： Permanent magnets

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Multiplier analysis of Lurye systems with power signals 63

Multiplier analysis of Lurye systems with power signals

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Heath, William P. Carrasco, Joaquin Bangor University School of Computer Science and Engineering Dean St LL57 1UT United Kingdom Nancy Rothwell Building University of Manchester Control Systems and Robotics Group Department of Electrical and Electronic Engineering M13 9PL United Kingdom

ISBN: (纸本)9798350316339

Multipliers can be used to guarantee both the Lyapunov stability and input-output stability of Lurye systems with time-invariant memoryless slope-restricted nonlinearities. If a dynamic multiplier is used there is no guarantee the closedloop system has finite incremental gain. It has been suggested in the literature that without this guarantee such a system may be critically sensitive to time-varying exogenous signals including noise. We show that multipliers guarantee the power gain of the system to be bounded and quantifiable. Furthermore power may be measured about an appropriate steady state bias term, provided the multiplier does not require the nonlinearity to be odd. Hence dynamic multipliers can be used to guarantee Lurye systems have low sensitivity to noise, provided other exogenous systems have constant steady state. We illustrate the analysis with an example where the exogenous signal is a power signal with non-zero mean. © 2024 IEEE.

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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Search and Rescue Base of Operation Prioritization with Aerial Orthomosaics

Search and Rescue Base of Operation Prioritization with Aeri...

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IEEE International Workshop on Safety, Security, and Rescue robotics (SSRR)

作者： Alyssa Hargis Ananya Rao Howie Choset Electrical and Computer Engineering Dept. United States Air Force Academy Carnegie Mellon University Robotics Institute

ISBN: (数字)9798331510954

ISBN: (纸本)9798331510961

Efficiently and accurately determining the location of bases of operations (BOOs) is essential for maximizing the effectiveness of response teams in emergency situations. A BOO is a location from which assets are deployed and, in many cases, resources are used for some centralized purpose. Identifying where BOOs should be located can be quite challenging as many factors, such as travel time, size, and proximity to the disaster, must be taken into consideration. We present an algorithm to automatically determine BOO locations. Through image segmentation of aerial orthomosaics, potential BOOs can be identified and subsequently evaluated, identifying prioritized locations for operators to initialize robot or human operations for rescue. We annotated a dataset for image segmentation of disaster sites and trained a custom model to create annotated maps for use by operators. Using annotated maps, we developed an algorithm to prioritize BOOs based on factors gathered from operators. Our methodology was experimentally evaluated for BOO selection from previous disaster scenarios and evaluated by users for utility and accuracy. The result is a proof of concept to create annotated disaster aerial imagery with prioritized BOOs.

关键词： Image segmentation Accuracy Disasters Security Robots

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STAGE: Scalable and Traversability-Aware Graph based Exploration Planner for Dynamically Varying Environments

arXiv

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arXiv 2024年

作者： Patel, Akash Saucedo, Mario A.V. Kanellakis, Christoforos Nikolakopoulos, George Robotics & AI Team Department of Computer Electrical and Space Engineering Lulea University of Technology LuleaSE-97187 Sweden

In this article, we propose a novel navigation framework that leverages a two layered graph representation of the environment for efficient large-scale exploration, while it integrates a novel uncertainty awareness scheme to handle dynamic scene changes in previously explored areas. The framework is structured around a novel goal oriented graph representation, that consists of, i) the local sub-graph and ii) the global graph layer respectively. The local sub-graphs encode local volumetric gain locations as frontiers, based on the direct pointcloud visibility, allowing fast graph building and path planning. Additionally, the global graph is build in an efficient way, using node-edge information exchange only on overlapping regions of sequential sub-graphs. Different from the state-of-theart graph based exploration methods, the proposed approach efficiently re-uses sub-graphs built in previous iterations to construct the global navigation layer. Another merit of the proposed scheme is the ability to handle scene changes (e.g. blocked pathways), adaptively updating the obstructed part of the global graph from traversable to not-traversable. This operation involved oriented sample space of a path segment in the global graph layer, while removing the respective edges from connected nodes of the global graph in cases of obstructions. As such, the exploration behavior is directing the robot to follow another route in the global re-positioning phase through path-way updates in the global graph. Finally, we showcase the performance of the method both in simulation runs as well as deployed in real-world scene involving a legged robot carrying camera and lidar sensor. © 2024, CC0.

关键词： Global positioning system

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Explorative Study on Motor Interference During Synchronous Human and Robot Arm Movements Under Varied Presence of a Robot Head

Explorative Study on Motor Interference During Synchronous H...

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IEEE International Conference on robotics and Biomimetics

作者： Mertcan Kaya Kolja Kühnlenz Robotics Research Lab Department of Electrical Engineering and Computer Science Coburg University of Applied Sciences and Arts Coburg Germany

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

This paper investigates the influence of a static robot head on deviations of human hand movements from task direction (motor interference) during simultaneous human and robot arm movements using a collaborative robot arm. Synchronous vertical and horizontal human and robot arm movements are conducted in all combinations (within-subjects factors direction and congruency). The presence of the head (head/ no head) is varied as a between-subjects factor. In this first step, only its motionless presence is deliberately focused at, in order to exclude contributions by eye- or head-movement behavior or facial expressions being difficult variables to generalize from. Participants' hands are tracked optically marker-based and deviations from task-related vertical, respectively, horizontal movements are evaluated. In contrast to expectations, results do not show a significant impact of the robot head on motor interference. Thus, there is evidence, that appearance might not be an activating factor for motor interference in HRI.

关键词： Hands Tracking Neurons Humanoid robots Morphology Interference Motors Manipulators Mirrors Robots

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Leveraging Computation of Expectation Models for Commonsense Affordance Estimation on 3D Scene Graphs

arXiv

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arXiv 2024年

作者： Saucedo, Mario A.V. Stathoulopoulos, Nikolaos Patel, Akash Kanellakis, Christoforos Nikolakopoulos, George Robotics & AI Team Department of Computer Electrical and Space Engineering Luleå University of Technology LuleåSE-97187 Sweden

This article studies the commonsense object affordance concept for enabling close-to-human task planning and task optimization of embodied robotic agents in urban environments. The focus of the object affordance is on reasoning how to effectively identify object's inherent utility during the task execution, which in this work is enabled through the analysis of contextual relations of sparse information of 3D scene graphs. The proposed framework develops a Correlation Information (CECI) model to learn probability distributions using a Graph Convolutional Network, allowing to extract the commonsense affordance for individual members of a semantic class. The overall framework was experimentally validated in a real-world indoor environment, showcasing the ability of the method to level with human commonsense. For a video of the article, showcasing the experimental demonstration, please refer to the following link: https://***/BDCMVx2GiQE. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital elevation model

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Learning-Based Joint Resource Slicing and Scheduling in Space-Terrestrial Integrated Vehicular Networks

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Journal of Communications and Information Networks 2021年第3期6卷 208-223页

作者： Huaqing Wu Jiayin Chen Conghao Zhou Junling Li Xuemin(Sherman)Shen Department of Electrical and Computer Engineering University of WaterlooWaterloo.ON N2L 3G1Canada Department of Electrical and Computer Engineering the University of British ColumbiaVancouverBC V6T 1Z4Canada Shenzhen Institute of Artificial Intelligence and Robotics for Society(AIRS) and the Department of Electrical and Computer EngineeringUniversity of WaterlooCanada

In this paper,we investigate the resource slicing and scheduling problem in the space-terrestrial integrated vehicular networks to support both delay-sensitive services(DSSs)and delay-tolerant services(DTSs).Resource slicing and scheduling are to allocate spectrum resources to different slices and determine user association and bandwidth allocation for individual *** accommodate the dynamic network conditions,we first formulate a joint resource slicing and scheduling(JRSS)problem to minimize the long-term system cost,including the DSS requirement violation cost,DTS delay cost,and slice reconfiguration *** resource slicing and scheduling decisions are interdependent with different timescales,we decompose the JRSS problem into a large-timescale resource slicing subproblem and a small-timescale resource scheduling *** propose a two-layered reinforcement learning(RL)-based JRSS scheme to find the solutions to the *** the resource slicing layer,spectrum resources are pre-allocated to different slices via a proximal policy optimization-based RL *** the resource scheduling layer,spectrum resources in each slice are scheduled to individual vehicles based on dynamic network conditions and service requirements via matching-based *** conduct extensive trace-driven experiments to demonstrate that the proposed scheme can effectively reduce the system cost while satisfying service quality requirements.

关键词： space-terrestrial integrated vehicular networks LEO satellite communication resource slicing and scheduling reinforcement learning matching-based optimization

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Semi-Autonomous Interaction Framework for Contact-Based Operations with Commercial UAVs IN GNSS-Denied Environments

Semi-Autonomous Interaction Framework for Contact-Based Oper...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： A. González-Morgado Q. Zhang G. Damigos E. Cuniato T. Hui E. Sahin G. Nikolakopoulos R. Siegwart M. Fumagalli A. Ollero G. Heredia GRVC Robotics Labs University of Seville Spain 3D Media Group Tampere University Finland Department of Computer Electrical and Space Ericsson Research and the Robotics and AI Group Autonomous Systems Lab (ASL) at ETH Zurich Switzerland Department of Electrical and Photonics Engineering Technical University of Denmark Denmark Department of Computer Electrical and Space Robotics and AI Group

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

Unmanned aerial vehicles (UAVs) are being increasingly established for autonomous contacted-based inspection of industrial assets, reducing the risk of errors by human operators. However, challenges remain in the navigation under unreliable Global Navigation Satellite System (GNSS) signals, or in the detection and interaction with the inspection surface, which limits the autonomy level of current UAV industrial technologies. This paper presents a semi-autonomous framework which combines automated target detection and interaction in GNSS-denied environments, with supervision commands by a human operator, to increase both safety and reliability. Our framework is composed of: (i) a camera-based onboard odometry solution for positioning the UAV in GNSSdenied conditions, (ii) a target-detection and filtering algorithm which estimates the orientation and position of the interaction target and (iii) a visual servoing strategy for approaching and contacting the target. The proposed framework is developed completely in ROS, and can be used with any commercial UAV. The framework is validated through outdoor flights, where a UAV detects and contacts a target on a vertical pipe.

关键词： Vibrations Global navigation satellite system Three-dimensional displays Switches Inspection Autonomous aerial vehicles Visual servoing Safety Odometry Reliability

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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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