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4th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Luo, Jing Yang, Chenguang Liu, Chao Yuan, Yuxia Li, Zhijun South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Peoples R China Univ Montpellier Dept Robot LIRMM UMR5506CNRS F-34095 Montpellier France Univ West England Bristol Robot Lab Bristol BS16 1QY Avon England South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China

ISBN: (纸本)9781728100647

Wrist motion classification is a very common research topic in scientific study. However, wrist motion recognition of the surgeon is often neglected in the robot-assisted surgery or surgical training. Therefore, the objective is to develop a classification method to recognize wrist motion of the surgeon. In order to do that, we present a linear discriminant analysis (LDA) algorithm involving surface electromyography (sEMG) signals to evaluate the motions in this paper. Firstly, sEMG signals are collected by using a MYO armband which can be worn on the forearm of a subject. Root-mean-square (RMS) and waveform length (WL) feature are extracted from the sEMG signals and then those features are regarded as input of the LDA to train the classifier. As a result, we can obtain a classifier to recognize four kinds of wrist motions. Classification experiment is performed by two subjects. The experimental results have been demonstrated by using the proposed approach and it is shown that the accuracy of wrist motion by using RMS feature is higher than that of by using WL feature.

关键词： Wrist Feature extraction Robots Conferences Mechatronics Surgery Training

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Applications of Game Theory in Vehicular Networks: A Survey

arXiv

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arXiv 2021年

作者： Sun, Zemin Liu, Yanheng Wang, Jian Li, Guofa Anil, Carie Li, Keqiang Guo, Xinyu Sun, Geng Tian, Daxin Cao, Dongpu College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Institute of Human Factors and Ergonomics College of Mechatronics and Control Engineering Shenzhen University Guangdong Shenzhen518060 China School of Computer Science and Engineering VIT-AP Andhra Pradesh Amaravati522237 India College of Engineering Nanjing Agricultural University Nanjing210031 China State Key Laboratory of Automotive Safety and Energy Department of Automotive Engineering Tsinghua University Beijing100084 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China Waterloo Cognitive Autonomous Driving Lab University of Waterloo N2L 3G1 Canada

In the Internet of things (IoT) era, vehicles and other intelligent components in an intelligent transportation system (ITS) are connected, forming vehicular networks (VNs) that provide efficient and safe traffic and ubiquitous access to various applications. However, as the number of nodes in an ITS increases, it is challenging to satisfy a varied and large number of service requests with different quality of service (QoS) and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve the objective for either an individual or a group. Game theory (GT), a theoretical framework designed for strategic interactions among rational decision makers sharing scarce resources, can be used to model and analyze individual or group behaviors of communicating entities in VNs. This paper primarily surveys the recent developments of GT in solving various challenges of VNs. This survey starts with an introduction to the background of VNs. A review of GT models studied in the VNs is then introduced, including the basic concepts, classifications, and applicable vehicular issues. After discussing the requirements of VNs and the motivation of using GT, a comprehensive literature review on GT applications in dealing with the challenges of current VNs is provided. Furthermore, recent contributions of GT to VNs that are integrated with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions of applying GT in VNs are outlined. Copyright © 2021, The Authors. All rights reserved.

关键词： Quality of service

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Proposing and Validation of a New Four-Point Finite-Difference Formula With Manipulator Application

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2018年第4期14卷 1323-1333页

作者： Shi, Yang Qiu, Binbin Chen, Dechao Li, Jian Zhang, Yunong Sun Yat Sen Univ Sch Informat Sci & Technol Guangzhou 510006 Guangdong Peoples R China Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China SYSU CMU Shunde Int Joint Res Inst Foshan 528300 Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou 510006 Guangdong Peoples R China

In this paper, a four-point one-step-ahead finite-difference formula is presented, which obtains higher computational precision in approximating the first-order derivative. Then, the formula is used for the discretization of the continuous-time Zhang neural network (CTZNN), and it can greatly overcome the limitation of the conventional formulas in CTZNN discretization. Based on this formula, a new-type discrete-time Zhang neural network (DTZNN) model is proposed and investigated for time-variant matrix pseudoinversion. Numerical experiments further validate the feasibility, effectiveness, and superiority of the proposed new-type DTZNN model for solving the time-variant matrix pseudoinversion. Moreover, the proposed new-type DTZNN model is applied to the control of a robot manipulator. Physical experiment performed on a four-link planar robot manipulator is presented to demonstrate physical realizability and effectiveness of the proposed new-type DTZNN model.

关键词： Discrete-time Zhang neural network (DTZNN) model finite difference robot manipulator control time-variant matrix pseudoinversion truncation error

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Design of a control Platform for Mobile Robot with SSVEP-BCI System

Design of a Control Platform for Mobile Robot with SSVEP-BCI...

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IEEE Conference on Cybernetics and Intelligent systems

作者： Yang Liu Xinyang Li Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China Hammersmith Campus Imperial College London National Heart and Lung Institute London Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9781728134581

ISBN: (纸本)9781728134598

This paper introduces a control platform based on brain-computer interface(BCI) for the mobile robot. The proposed platform consists of three parts: Path Planning, SSVEP-BCI and Mobile Robot control. The path planning part aims to seek an optimal path out from the starting point to the target point, which is denoted as A* algorithm in this work. The found path is shown to the user who determines the actual movement direction using SSVEP-BCI. The signal is sent to the mobile robot to control the movement of the robot. In the BCI part, the collected EEG signals are analyzed with the CCA method to obtain the homologous control signals. The mobile robot is required to complete the movement from the initial point to the destination without collision. The platform is evaluated with an off-line experiment with 4 subjects and the off-line analysis shows the feasibility of the proposed platform.

关键词： Mobile robots Visualization Electroencephalography Path planning Heuristic algorithms Frequency modulation

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Globally optimal consensus maximization for robust visual inertial localization in point and line map

Globally optimal consensus maximization for robust visual in...

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2020 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Yanmei Jiao Yue Wang Bo Fu Qimeng Tan Lei Chen Minhang Wang Shoudong Huang Rong Xiong State Key Laboratory of Industrial Control and Technology Zhejiang University Hangzhou P.R. China Beijing Key Laboratory of Intelligent Space Robotic System Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing P.R. China Minhang Wang is with the Application Lab Huawei Incorporated Company P.R. China Shoudong Huang is with the Center for Autonomous Systems (CAS) the University of Technology Sydney Australia

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

Map based visual inertial localization is a crucial step to reduce the drift in state estimation of mobile robots. The underlying problem for localization is to estimate the pose from a set of 3D-2D feature correspondences, of which the main challenge is the presence of outliers, especially in changing environment. In this paper, we propose a robust solution based on efficient global optimization of the consensus maximization problem, which is insensitive to high percentage of outliers. We first introduce translation invariant measurements (TIMs) for both points and lines to decouple the consensus maximization problem into rotation and translation subproblems, allowing for a two-stage solver with reduced search space. Then we show that (i) the rotation can be estimated by minimizing TIMs using only 1-dimensional branch-and-bound (BnB), (ii) the translation can be estimated by running 1-dimensional search for each of the three axes with prioritized progressive voting. Compared with the popular randomized solver, our solver achieves deterministic global convergence without requiring an initial value. Furthermore, ours is exponentially faster compared with existing BnB based methods. Finally, our experiments on both simulation and real-world datasets demonstrate that the proposed method gives accurate pose estimation even in the presence of 90% outliers (only 2 inliers).

关键词： Location awareness Visualization Pose estimation Search problems State estimation Optimization Rotation measurement

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ADRC with Feedfoward control For Time-Delay systems 7

ADRC with Feedfoward Control For Time-Delay Systems

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IEEE 7th Data Driven control and Learning systems Conference (DDCLS)

作者： Li, Xiangyang Ai, Wei Tian, Senping South China Univ Technol Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510641 Guangdong Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Guangdong Peoples R China

ISBN: (纸本)9781538626184

A novel Active disturbance rejection control (ADRC) with feedforward control is proposed for time-delay systems. The feedforward control, which is inspired by the classic Smith predictor, is added to the original ADRC scheme and tuned to improve the reference tracking performance. The ADRC is mainly tuned to reject total disturbance including model uncertainties and external disturbance. The feedforward control does not change the poles of the main closed-loop system and the stability of the main closed-loop system depends on the conventional ADRC whose stability has been solved theoretically. A first-order plus time-delay (FOPTD) system is used in simulation study where the increasing system order method for time-delay system is used in ADRC. Simulations for reference tracking, external disturbance and variations of time-delay, time-constant and process gain of the plant show that the proposed ADRC with feedforward control provides more effective tracking performance and disturbance rejection abilities than the conventional ADRC for time-delay systems.

关键词： Smith predictor active disturbance rejection control (ADRC) feedforward control tracking differentiator (TD) extended state observer (ESO) time-delay system

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Robust Zeroing Neural-Dynamics and Its Time-Varying Disturbances Suppression Model Applied to Mobile Robot Manipulators

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2018年第9期29卷 4385-4397页

作者： Chen, Dechao Zhang, Yunong Sun Yat Sen Univ Sch Informat Sci & Technol Guangzhou 510006 Guangdong Peoples R China Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China SYSU CMU Shunde Int Joint Res Inst Foshan 528300 Peoples R China Sun Yat Sen Univ Key Lab Machine Intelligence & Adv Comp Minist Educ Guangzhou 510006 Guangdong Peoples R China

This paper proposes a novel robust zeroing neuraldynamics (RZND) approach as well as its associated model for solving the inverse kinematics problem of mobile robot manipulators. Unlike existing works based on the assumption that neural network models are free of external disturbances, four common forms of time-varying disturbances suppressed by the proposed RZND model are investigated in this paper. In addition, theoretical analyses on the antidisturbance performance are presented in detail to prove the effectiveness and robustness of the proposed RZND model with time-varying disturbances suppressed for solving the inverse kinematics problem of mobile robot manipulators. That is, the RZND model converges toward the exact solution of the inverse kinematics problem of mobile robot manipulators with bounded or zero-oriented steady-state position error. Moreover, simulation studies and comprehensive comparisons with existing neural network models, e.g., the conventional Zhang neural network model and the gradient-based recurrent neural network model, together with extensive tests with four common forms of time-varying disturbances substantiate the efficacy, robustness, and superiority of the proposed RZND approach as well as its time-varying disturbances suppression model for solving the inverse kinematics problem of mobile robot manipulators.

关键词： Mobile robot manipulators neural network models robustness time-varying disturbances zeroing neural dynamics (ZND)

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Robot teaching by teleoperation based on visual interaction and extreme learning machine

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NEUROCOMPUTING 2018年 275卷 2093-2103页

作者： Xu, Yang Yang, Chenguang Zhong, Junpei Wang, Ning Zhao, Lijun South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China Natl Inst Adv Ind Sci & Technol Aomi 2-3-26 Tokyo Japan Chinese Univ Hong Kong Shenzhen Res Inst Bldg Shenzhen Peoples R China Harbin Inst Technol State Key Lab Robot & Syst Harbin 150001 Heilongjiang Peoples R China

Compared with traditional robot teaching methods, robots can learn various human-like skills in a more efficient and natural manner through teleoperation. In this paper, we propose a teleoperation method based on human-robot interaction (HRI), which mainly uses visual information. With only one teleoperation, the robot can reproduce a trajectory. There is a certain error between this trajectory and the optimal trajectory due to the cause of the human demonstrator or the robot. So we use an extreme learning machine (ELM) based algorithm to transfer the demonstrator's motions to the robot. To verify the method, we use a Microsoft KinectV2 to capture the human body motion and the hand state, according to which a Baxter robot in Virtual Robot Experimentation Platform (V-REP) will be controlled by the command. Through learning and training by the ELM, the robot in V-REP can complete a certain task autonomously and the robot in reality can reproduce this trajectory well. The experimental results show that the developed method has achieved satisfactory performance. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Robot teaching Teleoperation Extreme learning machine (ELM) Human-robot interaction (HRI)

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Hand Tracking Accuracy Enhancement by Data Fusion Using Leap Motion and Myo Armband

Hand Tracking Accuracy Enhancement by Data Fusion Using Leap...

引用

International Conference on Unmanned systems and Artificial Intelligence

作者： Jingxiang Chen Chao Liu Rongxin Cui Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China LIRMM CNRS-University of Montpellier France School of Marine Engineering Northwestern Polytechnical University Xi'an China Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9781728158594

ISBN: (纸本)9781728158600

In this paper, by using the combination of Leap Motion and Myo armband, two methods for hand tracking and online hand gesture identification are proposed. With the proposed methods, We have improved the measurement accuracy of the palm direction and solved the problem of insufficient accuracy when the palm is at the limit of the measurement range. We use the Kalman filter algorithm and the neural network classification method to process the data measured by Leap Motion and Myo, so that the tracking of the operator's hand gesture is more accurate and robust even when the hand is at positions close to the measurement limit of one single sensor. The methods, which improve the hand tracking accuracy, can be used for robotic control, demonstration or teleoperation. The effectiveness of the proposed methods has been demonstrated through comparative experiments.

关键词： Leap Motion Myo sensor fusion hand tracking hand guesture identification Sensor fusion Hand arm band Data fusion tracking accuracy distance measurement improving precision accuracy of measurement Classification Methods Gestures data integration

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Parameter Identification of JONSWAP Spectrum Acquired by Airborne LIDAR

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Journal of Ocean University of China 2017年第6期16卷 998-1002页

作者： YU Yang PEI Hailong XU Chengzhong Key Laboratory of Autonomous Systems and Networked Control Ministry of EducationSouth China University of Technology LAGEP Batiment CPEUniversité Claude Bernard Lyon 1

In this study, we developed the first linear Joint North Sea Wave Project(JONSWAP) spectrum(JS), which involves a transformation from the JS solution to the natural logarithmic scale. This transformation is convenient for defining the least squares function in terms of the scale and shape parameters. We identified these two wind-dependent parameters to better understand the wind effect on surface waves. Due to its efficiency and high-resolution, we employed the airborne Light Detection and Ranging(LIDAR) system for our measurements. Due to the lack of actual data, we simulated ocean waves in the MATlab environment, which can be easily translated into industrial programming language. We utilized the Longuet-Higgin(LH) random-phase method to generate the time series of wave records and used the fast Fourier transform(FFT) technique to compute the power spectra density. After validating these procedures, we identified the JS parameters by minimizing the mean-square error of the target spectrum to that of the estimated spectrum obtained by FFT. We determined that the estimation error is relative to the amount of available wave record data. Finally, we found the inverse computation of wind factors(wind speed and wind fetch length) to be robust and sufficiently precise for wave forecasting.

关键词： JONSWAP spectrum parameter identification least square method airborne LIDAR

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