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IEEE International Conference of Intelligent Robotic and control Engineering (IRCE)

作者： Xiulian Wang Jinggao Lin Xi Jin Changqing Xia Chi Xu College of Automation and Electrical Engineering Shenyang Ligong University China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

With the rapid development of edge computing, industrial automation has evolved a two-layer distributed computing architecture, including local controllers communicating through wired networks and edge controllers communicating wirelessly. The edge controllers in existing studies use WIFI, which has a limited working distance and requires independent network deployment, so they are unsuitable for future large-scale complex industrial production. At present, 5G technology is undergoing rapid development in the field of industrial automation, with wide coverage and excellent mobility, gradually becoming suitable for future large-scale and complex industrial production scenarios. Therefore, this paper proposed an SW5G method based on 5G, which combined the local controller with the edge controller. The controllers were switched according to the constantly changing 5G network transmission delay, thereby optimizing the overall system control performance. Finally, simulation experiments demonstrated good control performance of SW5G in various network scenarios.

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A Novel Dynamically Differentiated Access Scheme for Massive Grant-Free NOMA

A Novel Dynamically Differentiated Access Scheme for Massive...

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IEEE Conference on Vehicular Technology (VTC)

作者： Yitian Wang Meng Zheng Haibin Yu Wei Liang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665454698

Facing the dual challenges of massive access and time-sensitive traffics, grant-free non-orthogonal multiple access (GF-NOMA) emerges as a promising technology for implementing massive ultra-reliable and low-latency communications (mURLLC). In this paper, we propose a differentiated power level access (DPLA) policy that exploits the correlations among power levels of GF-NOMA, and implement DPLA by a dynamically distributed GF-NOMA framework. Further, a closed-form expression to the reliability of DPLA is analytically derived and the optimal framework parameters to maximize reliability are obtained. Finally, considering the traffic variation over time, we propose a dynamically-distributed differentiated-layered transmission $(\mathrm{D}^{3}$ LT) algorithm to improve the reliability online. Simulation results show that the proposed scheme in this work outweighs existing schemes in transmission reliability.

关键词： NOMA Vehicular and wireless technologies Correlation Closed-form solutions Simulation Heuristic algorithms Reliability

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Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning

arXiv

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arXiv 2024年

作者： He, Fan He, Mingzhen Shi, Lei Huang, Xiaolin Suykens, Johan A.K. STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium MOE Key Laboratory of System Control and Information Processing Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory for Contemporary Applied Mathematics School of Mathematical Sciences Fudan University Shanghai200433 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China MOE Key Laboratory of System Control and Information Processing Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China

Ridgeless regression has garnered attention among researchers, particularly in light of the "Benign Overfitting" phenomenon, where models interpolating noisy samples demonstrate robust generalization. However, kernel ridgeless regression does not always perform well due to the lack of flexibility. This paper enhances kernel ridgeless regression with Locally-Adaptive-Bandwidths (LAB) RBF kernels, incorporating kernel learning techniques to improve performance in both experiments and theory. For the first time, we demonstrate that functions learned from LAB RBF kernels belong to an integral space of Reproducible Kernel Hilbert Spaces (RKHSs). Despite the absence of explicit regularization in the proposed model, its optimization is equivalent to solving an 0-regularized problem in the integral space of RKHSs, elucidating the origin of its generalization ability. Taking an approximation analysis viewpoint, we introduce an lq-norm analysis technique (with 0 © 2024, CC BY.

关键词： Vector spaces

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Distributed Optimization by Network Flows with Spatio-Temporal Compression

arXiv

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arXiv 2024年

作者： Ren, Zihao Wang, Lei Yi, Xinlei Wang, Xi Yuan, Deming Yang, Tao Wu, Zhengguang Shi, Guodong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China College of Electronics and Information Engineering Tongji University China School of Electrical Engineering Telecommunications University of New South Wales Sydney Australia School of Automation Nanjing University of Science and Technology China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University China Australian Centre for Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney SydneyNSW2006 Australia

Several data compressors have been proposed in distributed optimization frameworks of network systems to reduce communication overhead in large-scale applications. In this paper, we demonstrate that effective information compression may occur over time or space during sequences of node communications in distributed algorithms, leading to the concept of spatio-temporal compressors. This abstraction classifies existing compressors as spatio-temporal compressors, with their effectiveness described by constructive stability criteria from nonlinear system theory. Subsequently, we apply these spatio-temporal compressors to standard continuous-time consensus flows and distributed prime-dual flows, establishing conditions ensuring convergence. Additionally, we introduce a novel observer-based distributed primal-dual continuous flow integrated with spatio-temporal compressors, which provides broader convergence conditions. These continuous flows achieve exponential convergence to the global optimum when the objective function is strongly convex and can be discretized using Euler approximations. Finally, numerical simulations illustrate the versatility of the proposed spatio-temporal compressors and verify the convergence of algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Stability criteria

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3D Vision Based Lamination Gap Detection of Carbon Fiber Composite

3D Vision Based Lamination Gap Detection of Carbon Fiber Com...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Yuanye Xu Libiao Yuan Wei Liang Yutuo Yang Shuai Liu Ziming Zeng Yinlong Zhang Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China College of Information Engineering Shenyang University of Chemical Technology Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China School of Automotive and Transportation Engineering Shenzhen Polytechnic Shenzhen China

The paper presents a machine learning approach to detect laminate gaps and to classify them as open or closed. Compared with the traditional detection methods, which suffer from low precision of point cloud segmentation and poor effect of gap extraction in traditional gap detection, this paper proposes a gap detection algorithm based on 3D Vision. First, an improved undirected graph point cloud segmentation algorithm is used to overcome the under-segmentation of the target region. Second, an adaptive vector for angle discriminant model is proposed to solve the problem of poor extraction effect of the gap edge points. Finally, the B-spline curve-fitting model is used to describe the 3D points on the lamination edges. The proposed algorithm has been evaluated in a visual quality detection platform for carbon fiber composite laminates. The results show that the proposed approach is able to accurately detect and classify laminate gaps in carbon fiber composite structures, providing a reliable tool for quality control in industrial manufacturing processes.

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Vision based collision detection for a safe collaborative industrial manipulator

Vision based collision detection for a safe collaborative in...

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IEEE International Conference on Intelligence and Safety for robotics (ISR)

作者： Bruno Maric Fran Jurican Matko Orsag Zdenko Kovacic LARICS Laboratory for Robotics and Intelligent Control Systems University of Zagreb Faculty of Electrical Engineering and Computing Unska 3 Zagreb Croatia

ISBN: (纸本)9781665438629;9781665411486

In this work we present an improved method for collision detection that extends the ability of collaborative systems to share the workspace between the operator and the robot. Our work is based on the state-of-the-art GPU-Voxels algorithm, with a dynamic swept volume method for collision detection that minimizes intervention in robot trajectories. We propose a sensor calibration method that allows multiple cameras to be placed around the robot to ensure coverage without shadows. In addition, we show how our previous work on advanced force control algorithms, which in combination with the collision detection algorithm presented, meets all safety requirements of the technical specification ISO/TS 15066. Our ultimate goal is to derive a framework for collaborative industrial manipulators, which are able to work side by side with people.

关键词： Heuristic algorithms Dynamics Collaboration Robot sensing systems Safety Trajectory Calibration

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CPnP: Consistent Pose Estimator for Perspective-n-Point Problem with Bias Elimination

CPnP: Consistent Pose Estimator for Perspective-n-Point Prob...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Guangyang Zeng Shiyu Chen Biqiang Mu Guodong Shi Junfeng Wu School of Data Science Chinese University of Hong Kong Shenzhen P. R. China Key Laboratory of Systems and Control Institute of Systems Science Beijing P. R. China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing P. R. China The Australian Center for Field Robotics School of Aerospace Sydney Mechanical and Mechatronic Engineering The University of Sydney Sydney Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS) Shenzhen P. R. China

The Perspective-n-Point (PnP) problem has been widely studied in both computer vision and photogrammetry societies. With the development of feature extraction techniques, a large number of feature points might be available in a single shot. It is promising to devise a consistent estimator, i.e., the estimate can converge to the true camera pose as the number of points increases. To this end, we propose a consistent PnP solver, named CPnP, with bias elimination. Specifically, linear equations are constructed from the original projection model via measurement model modification and variable elimination, based on which a closed-form least-squares solution is obtained. We then analyze and subtract the asymptotic bias of this solution, resulting in a consistent estimate. Additionally, Gauss-Newton (GN) iterations are executed to refine the consistent solution. Our proposed estimator is efficient in terms of computations—it has $O(n)$ time complexity. Simulations and real dataset tests show that our proposed estimator is superior to some well-known ones for images with dense visual features, in terms of estimation precision and computing time.

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Hybrid Disassembly Line Balancing for Human-robot Collaborative Remanufacturing

Hybrid Disassembly Line Balancing for Human-robot Collaborat...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Feng Guo XiWang Guo MengChu Zhou Weitian Wang ShuJin Qin Qi Kang College of Information and Control Engineering Liaoning Petrochemical University Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Collaborative Robotics and Smart Systems Laboratory (CRoSS Lab) Montclair State University Montclair NJ USA College of Economics and Management Shangqiu Normal University Shangqiu P R. China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

As technologies advance rapidly, the urgency of remanufacturing is escalating. Efficient disassembly processes are crucial at the outset of remanufacturing. This work proposes an innovative disassembly scheme aimed at amplifying efficiency through the utilization of parallelization and human-robot collaboration in the context of a hybrid disassembly line. A single-objective mixed-integer programming model is developed to optimize disassembly profit. A discrete salp swarm algorithm is proposed to solve it since it is NP-hard. We have proposed and integrated uniform variation and two-point crossover operators in this algorithm. After comparing its results with those of the exact solver and other intelligent optimization methods, we conclude its competitive performance in both solution quality and efficiency.

关键词： Computer aided software engineering Automation Optimization methods Human-robot interaction Collaboration Programming

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Nonlinear parameter-varying state-feedback design for a gyroscope using virtual control contraction metrics

arXiv

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arXiv 2021年

作者： Wang, Ruigang Koelwijn, Patrick J.W. Manchester, Ian R. Tóth, Roland Australian Centre for Field Robotics Sydney Institute for Robotics and Intelligent Systems University of Sydney SydneyNSW2006 Australia Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

In this paper, we present a virtual control contraction metric (VCCM) based nonlinear parameter-varying (NPV) approach to design a state-feedback controller for a control moment gyroscope (CMG) to track a user-defined trajectory set. This VCCM based nonlinear stabilization and performance synthesis approach, which is similar to linear parameter-varying (LPV) control approaches, allows to achieve exact guarantees of exponential stability and L2-gain performance on nonlinear systems with respect to all trajectories from the predetermined set, which is not the case with the conventional LPV methods. Simulation and experimental studies conducted in both fully- and under-actuated operating modes of the CMG show effectiveness of this approach compared to standard LPV control methods. © 2021, CC BY.

关键词： State feedback

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Graph-Based Visual-Kinematic Fusion and Monte Carlo Initialization for Fast-Deployable Cable-Driven Robots

Graph-Based Visual-Kinematic Fusion and Monte Carlo Initiali...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： R. Khorrambakht H. Damirchi M.R. Dindarloo A. Saki S.A. Khalilpour Hamid D. Taghirad Stephan Weiss Electrical & Computer Engineering Department Control/Robotics Research Laboratory Tandon School of Engineering New York University Brooklyn NY School of Computer Science University of Adelaide Australia Faculty of Electrical Engineering University of Technology Tehran Iran Department of Smart Systems Technologies University of Klagenfurt Austria

Ease of calibration and high-accuracy task-space state-estimation purely based on onboard sensors is a key requirement for enabling easily deployable cable robots in real-world applications. In this work, we incorporate the onboard camera and kinematic sensors to drive a statistical fusion framework that presents a unified localization and calibration system which requires no initial values for the kinematic parameters. This is achieved by formulating a Monte-Carlo algorithm that initializes a factor-graph representation of the calibration and localization problem. With this, we are able to jointly identify both the kinematic parameters and the visual odometry scale alongside their corresponding uncertainties. We demonstrate the practical applicability of the framework using our state-estimation dataset recorded with the ARAS-CAM suspended cable driven parallel robot, and published as part of this manuscript.

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