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

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Design Considerations for a Steerable Needle Robot to Maximize Reachable Lung Volume

Design Considerations for a Steerable Needle Robot to Maximi...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Fried, Inbar Hoelscher, Janine Fu, Mengyu Emerson, Maxwell Ertop, Tayfun Efe Rox, Margaret Granna, Josephine Kuntz, Alan Akulian, Jason A. Webster, Robert J., III Alterovitz, Ron Univ North Carolina Chapel Hill Dept Comp Sci Chapel Hill NC 27599 USA Univ N Carolina Sch Med Chapel Hill NC 27599 USA Vanderbilt Univ Dept Mech Engn Nashville TN 37235 USA Univ Utah Sch Comp Salt Lake City UT 84112 USA Univ Utah Robot Ctr Salt Lake City UT 84112 USA Univ N Carolina Div Pulm Dis & Crit Care Med Sch Med Chapel Hill NC 27514 USA

ISBN: (纸本)9781728190778

Steerable needles that are able to follow curvilinear trajectories and steer around anatomical obstacles are a promising solution for many interventional procedures. In the lung, these needles can be deployed from the tip of a conventional bronchoscope to reach lung lesions for diagnosis. The reach of such a device depends on several design parameters including the bronchoscope diameter, the angle of the piercing device relative to the medial axis of the airway, and the needle's minimum radius of curvature while steering. Assessing the effect of these parameters on the overall system's clinical utility is important in informing future design choices and understanding the capabilities and limitations of the system. In this paper, we analyze the effect of various settings for these three robot parameters on the percentage of the lung that the robot can reach. We combine Monte Carlo random sampling of piercing configurations with a Rapidly-exploring Random Trees based steerable needle motion planner in simulated human lung environments to asymptotically accurately estimate the volume of sites in the lung reachable by the robot. We highlight the importance of each parameter on the overall system's reachable workspace in an effort to motivate future device innovation and highlight design trade-offs.

关键词： Analytical models Visualization Technological innovation Monte Carlo methods Lung Needles Trajectory

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YogurtNet: Enhanced machine learning approach for voltage drop prediction 3

YogurtNet: Enhanced machine learning approach for voltage dr...

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2024 3rd international conference on Electronics and Integrated Circuit Technology, EICT 2024

作者： Liang, Longxi Xian, Yuxiang Guo, Shaoshan Xie, Zhuoming School of Automation Guangdong University of Technology Guangdong Guangzhou510006 China School of Integrated Circuits Guangdong University of Technology Guangdong Guangzhou510006 China

The aim of this study is to design and implement YogurtNet, a machine learning based static voltage drop (IR Drop) prediction system for SoC power networks. The system employs class image processing techniques, unsupervised learning clustering methods and shallow neural network techniques. Important algorithmic components include the Word2Vec algorithm for implementing clustering, which maps instance names into a name coordinate system;the Pix2pix algorithm for transforming 17 channels of raw data into two channels of predicted data;and an in-house developed shallow neural network, YogurtPyramid, which is used to further approximate and optimize the prediction results. Our model is trained on the names, coordinates, power consumption, and resistance of the back-end layout component instances of three mainstream open-source SoCs (RISCY, Zero-riscy, and RISCY-FPU), and successfully predicts the IR Drop data of each on-chip instance. In terms of model execution results, the average execution time is 69.94 seconds and the average MAE value is 5.2784, demonstrating the high accuracy prediction capability of the model. © Published under licence by IOP Publishing Ltd.

关键词： Prediction models

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An Iterative Variable Step Error Compensation Approach for Enhancing Robot Trajectory Accuracy 3

An Iterative Variable Step Error Compensation Approach for E...

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3rd international conference on automation, robotics and Computer Engineering, ICARCE 2024

作者： Wang, Chengzhi Zhao, Sikai Zheng, Tianjiao Cai, Hegao Zhao, Jie Zhu, Yanhe Harbin Institute of Technology State Key Laboratory of Robotics and Systems Harbin China

ISBN: (纸本)9798331529505

In this paper, an iterative error compensation approach is analyzed and designed to improve trajectory accuracy of industrial robots. Advanced offline programming and robotic machining tasks require high trajectory accuracy of industrial robots, which is significantly affected by parameter inaccuracy, stiffness deformation, backlash, and so on. Such complicated coupling makes it impossible to model each factor separately, thus data-driven direct error compensation is the major solution to improve trajectory accuracy. Previous methods utilize external measurement equipment to obtain tracking error and rely on the error consistency assumption to compensate for the error by modifying the trajectory commands directly. However, such consistency assumption does not hold. Even a minor incremental modification of the initial commands can lead to unexpected influence on the robot's tracking performance, owning to unaccounted factors such as the robot's dynamic stiffness and natural frequency. This paper decomposes trajectory tracking errors based on servo encoder feedback and external laser tracker, and proposes a variable step error compensation scheme to improve tracking performance. Physical experiments are conducted on a standard 6 degree-of-freedom industrial robot to validate the effectiveness of the proposed method. The results of various error compensation approaches are analyzed to draw a possible error correlation model, in order to account for the error inconsistency in the compensation process. © 2024 IEEE.

关键词： Industrial robots

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Gaze Betray You: Abnormal Attention Detection from Human-Robot Interaction Dynamics

Gaze Betray You: Abnormal Attention Detection from Human-Rob...

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IEEE international conference on robotics and Biomimetics

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

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

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.

关键词： Solid modeling Human-robot interaction Games Virtual reality Feature extraction Safety Pattern matching Monitoring Autonomous vehicles Robots

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Research on Collision Detection Algorithm for Human-SRL Collaborative Motion Planning 4

Research on Collision Detection Algorithm for Human-SRL Coll...

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2022 4th international Symposium on robotics and Intelligent Manufacturing Technology, ISRIMT 2022

作者： Zhang, Xiaobo Liu, Jinguo State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China China University of Chinese Academy of Sciences Beijing100049 China

The existing collision detection algorithms are commonly based on triangle patch models, which leads to complex detection logic. Aiming at this problem, this paper proposes a new collision detection algorithm. It has simple detection logic and fast detection speed, and it is mainly used for human-SRL cooperative motion planning. The algorithm is based on a human-SRL skeleton model and a multi-sphere envelope method and includes four sub-algorithms, namely, a multi-sphere envelope sub-algorithm, an SRL collision detection sub-algorithm, a collision detection sub-algorithm between SRL and human, and a collision detection sub-algorithm between SRL and environment objects. This paper first introduces the pseudo-code of the collision detection algorithm. Secondly, the implementation principles of its four sub-algorithms are expounded. Finally, the correctness of the collision detection algorithm is verified by simulation. © Published under licence by IOP Publishing Ltd.

关键词： Motion planning

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Motion Planning of a Rehabilitation Robotic Arm using Brain data & Deep Learning

Motion Planning of a Rehabilitation Robotic Arm using Brain ...

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RSI/ISM international conference on robotics and Mechatronics (ICROM)

作者： Ali Soltani S. Ali A. Moosavian Advanced Robotics and Automated Systems (ARAS) Lab K. N Toosi University of Technology Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

The Brain-Computer Interface (BCI) technology has emerged as an innovative tool in the field of rehabilitation, aimed at restoring and improving patient motor and sensory functions. The motivation for using BCI in rehabilitation is primarily based on the ability of these systems to establish a direct correlation between brain activity and external devices, such as motor prostheses or assistive robots. This technology aids patients with spinal cord injuries, strokes, or neurological diseases in regaining lost functionality by directly controlling assistive devices. The primary objective of research in this area is to enhance the quality of life for individuals with motor impairments and to improve the efficiency of rehabilitation methods, potentially leading to the development of models for automation in clinics and rehabilitation centers. In this study, to utilize the BCI as an automated system for controlling rehabilitation devices or functional prostheses, a series of experiments were designed and conducted to record electroencephalography (EEG) data from healthy individuals during a variety of hand motions, while simultaneously the motion is captured with two webcams. The initial data was collected and analyzed, and the results indicate that increasing the number of test subjects and employing more advanced equipment for signal recording could lead to more accurate results and the development of a more comprehensive model. Obtained model can then be used to command a rehabilitation arm according to the patient brain signals.

关键词： Webcams Stroke (medical condition) Motors Brain modeling Assistive robots Robot sensing systems Electroencephalography Brain-computer interfaces Recording Spinal cord injury

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Pest Detection and Identification Guided by Feature Maps 12

Pest Detection and Identification Guided by Feature Maps

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12th international conference on Image Processing Theory, Tools and Applications, IPTA 2023

作者： Chen, Miao Chen, Yanan Guo, Minghui Wang, Jianji Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics China Xi'an Jiaotong University School of Software Engineering Xi'an China

ISBN: (纸本)9798350325416

Effective pest detection and identification are of great significance for agricultural activities, and morden machine learning methods, especially the deep neural network, undoubtedly provide convenient and effective ways for this issue. Most of the mainstream methods for pest detection require annotation of manual bounding box. However, the public pest datasets usually lack the labeling information. In this work, we propose a method for detecting pests with weak supervision. We obtain the key regions of the pest targets by deeply mining the information in the feature maps of the convolutional neural networks (CNNs). First, using the class activation mapping technique to generate activation maps of the target class during forward propagation of the model, and then obtaining the key regions of the target based on the hotspots extracted from the activation maps. In addition, our proposed method can be deployed as a general strategy to other CNNs, and experimental results demonstrate that our method can steadily improve the classification accuracy of the model. By applying our method to the single Resnet50, we can achieve 74.27% classification accuracy on the publicly accessible IP102 dataset. Another attribution of this paper is that we propose a lightweight approach to find solutions for pest detection in agricultural automation applications. © 2023 IEEE.

关键词： Deep neural networks

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Asynchronous Federated Learning for Elephant Flow Detection in Software Defined Networking Systems 3

Asynchronous Federated Learning for Elephant Flow Detection ...

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2021 3rd international conference on robotics, Intelligent Control and Artificial Intelligence, ICRICA 2021

作者： Ma, Xiaohang Liao, Ling Xia Li, Zhi Chao, Han-Chieh School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin China School of Electronic Information and Automation Guilin University of Aerospace Technology Guangxi China National Dong Hwa University Taiwan School of Electronic Information and Automation Guilin University of Aerospace Technology Guilin China Department of Electrical Engineering National Dong Hwa University Hualien Taiwan

This paper introduces an Asynchronous Federated Learning (AFL) approach to train an elephant flow model over Software Defined Networking (SDN) systems with distributed controllers. The AFL addresses the issues of data privacy and communication overhead in collecting network statistics over large-scaled SDN systems. It allows each local controller to train a local model based on its local statistics using Decision Tree and upload the local model to the root in an asynchronous manner, so that the root controller can aggregate each local model into a global model once a local model is received to improve its time efficiency. The AFL proposes to weight the performance of each local model to form the global model. The evaluation based on 5 real packet traces demonstrates the accuracy of the AFL is better than any local models and two classical federated learning approaches. © Published under licence by IOP Publishing Ltd.

关键词： Software defined networking

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Design and Visual Servoing Control of a Hybrid Dual-Segment Flexible Neurosurgical Robot for Intraventricular Biopsy

Design and Visual Servoing Control of a Hybrid Dual-Segment ...

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IEEE international conference on robotics and automation (ICRA)

作者： Jian Chen Mingcong Chen Qingxiang Zhao Shuai Wang Yihe Wang Ying Xiao Jian Hu Danny Tat Ming Chan Kam Tong Leo Yeung David Yuen Chung Chan Hongbin Liu School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences (CASIA) Beijing China Centre of AI and Robotics Hong Kong Institute of Science and Innovation Chinese Academy of Sciences (CAIR-HKISI-CAS) HKSAR Department of Biomedical Engineering City University of Hong Kong HKSAR CAIR-HKISI-CAS HKSAR State Key Laboratory of Multimodal Artificial Intelligence Systems CASIA Beijing China Department of Surgery The Chinese University of Hong Kong HKSAR State Key Laboratory of Management and Control for Complex Systems CASIA Beijing China School of Biomedical Engineering and Imaging Sciences King’s College London London UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Traditional rigid endoscopes have challenges in flexibly treating tumors located deep in the brain, and low operability and fixed viewing angles limit its development. This study introduces a novel dual-segment flexible robotic endoscope MicroNeuro, designed to perform biopsies with dexterous surgical manipulation deep in the brain. Taking into account the uncertainty of the control model, an image-based visual servoing with online robot Jacobian estimation has been implemented to enhance motion accuracy. Furthermore, the application of model predictive control with constraints significantly bolsters the flexible robot’s ability to adaptively track mobile objects and resist external interference. Experimental results underscore that the proposed control system enhances motion stability and precision. Phantom testing substantiates its considerable potential for deployment in neurosurgery.

关键词： Jacobian matrices Visualization Endoscopes Estimation Interference Predictive models Control systems

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Advances in Circuit-Driven Milli Robot Systems

Advances in Circuit-Driven Milli Robot Systems

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international conference on Manipulation, automation and robotics at Small Scales (MARSS)

作者： Ron Pelrine Allen Hsu CEO of Pelrine Innovations LLC Longmont CO USA SRI International Menlo Park CA

ISBN: (数字)9798350376807

ISBN: (纸本)9798350376814

This paper reports improved understanding of local robot behavior in circuit-driven mini and micro robots, and its implications for both new and old applications. The directional behavior of circuit-driven robots using parallel trace designs is analyzed to show the highest force direction is diagonal (45 degrees) to the parallel traces, not parallel or perpendicular to the traces as is more commonly used. The planar force is shown to be approximately 40% greater in the diagonal direction. More importantly, we report analysis and modeling which shows that the diagonal direction has a force perpendicular to the driving circuit that reduces or eliminates friction compared to non-diagonal directions. The analysis and modeling have been tested using a system oriented to exploit the advantages of the diagonal direction, along with a pulsed power software and hardware subsystem for elucidating the tradeoffs in moving in the diagonal versus non-diagonal directions. Among the unexpected results, we show operation on relatively thick (> 1 mm) surfaces, high friction surfaces (sandpaper), and speeds of 100 cm/s using milli robots in the diagonal direction. These new capabilities open new applications, such as cleaning solar cells and using small but commercial bearings to enable cooperative robots for 4 or more degrees-of-freedom (DOF) devices.

关键词： Performance evaluation Analytical models Friction Photovoltaic cells Force Magnetic forces Software

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