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

作者： Huanshu Wei Zhijian Qiao Zhe Liu Chuanzhe Suo Peng Yin Yueling Shen Haoang Li Hesheng Wang The Chinese University of Hong Kong T Stone Robotics Institute Hong Kong Shanghai Jiao Tong University China University of Cambridge United Kingdom Carnegie Mellon University Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

3D Point cloud registration is still a very challenging topic due to the difficulty in finding the rigid transformation between two point clouds with partial correspondences, and it's even harder in the absence of any initial estimation information. In this paper, we present an end-to-end deep-learning based approach to resolve the point cloud registration problem. Firstly, the revised LPD-Net is introduced to extract features and aggregate them with the graph network. Secondly, the self-attention mechanism is utilized to enhance the structure information in the point cloud and the cross-attention mechanism is designed to enhance the corresponding information between the two input point clouds. Based on which, the virtual corresponding points can be generated by a soft pointer based method, and finally, the point cloud registration problem can be solved by implementing the SVD method. Comparison results in ModelNet40 dataset validate that the proposed approach reaches the state-of-the-art in point cloud registration tasks and experiment resutls in KITTI dataset validate the effectiveness of the proposed approach in real applications.

关键词： Three-dimensional displays Aggregates Pose estimation Feature extraction Task analysis intelligent robots

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Spherical interpolated convolutional network with distance-feature density for 3D semantic segmentation of point clouds

arXiv

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arXiv 2020年

作者： Wang, Guangming Yang, Yehui Zhang, Huixin Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Computer Science and Technology University of Cambridge United Kingdom

The semantic segmentation of point clouds is an important part of the environment perception for robots. However, it is difficult to directly adopt the traditional 3D convolution kernel to extract features from raw 3D point clouds because of the unstructured property of point clouds. In this paper, a spherical interpolated convolution operator is proposed to replace the traditional grid-shaped 3D convolution operator. This newly proposed feature extraction operator improves the accuracy of the network and reduces the parameters of the network. In addition, this paper analyzes the defect of point cloud interpolation methods based on the distance as the interpolation weight and proposes the self-learned distance-feature density by combining the distance and the feature correlation. The proposed method makes the feature extraction of spherical interpolated convolution network more rational and effective. The effectiveness of the proposed network is demonstrated on the 3D semantic segmentation task of point clouds. Experiments show that the proposed method achieves good performance on the ScanNet dataset and Paris-Lille-3D dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantic Segmentation

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A Piecewise-Markovian Lyapunov Approach to Reliable Output Feedback control for Fuzzy-Affine systems With Time-Delays and Actuator Faults

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IEEE TRANSACTIONS ON CYBERNETICS 2018年第9期48卷 2723-2735页

作者： Wei, Yanling Qiu, Jianbin Shi, Peng Wu, Ligang State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China School of Electrical and Electronic Engineering University of Adelaide Adelaide SA Australia Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China

This paper addresses the problem of delay-dependent robust and reliable H-infinity static output feedback (SOF) control for a class of uncertain discrete-time Takagi-Sugeno fuzzy-affine (FA) systems with time-varying delay and actuator faults in a singular system framework. The Markov chain is employed to describe the actuator faults behaviors. In particular, by utilizing a system augmentation approach, the conventional closed-loop system is converted into a singular FA system. By constructing a piecewise-Markovian Lyapunov-Krasovskii functional, a new H-infinity performance analysis criterion is then presented, where a novel summation inequality and S-procedure are succeedingly employed. Subsequently, thanks to the special structure of the singular system formulation, the piecewise-affine SOF controller design is proposed via a convex program. Lastly, illustrative examples are given to show the efficacy and less conservatism of the presented approach.

关键词： Piecewise Markovian Lyapunov functional reliable output feedback control time-varying delay T-S fuzzy-affine (FA) systems

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Adaptive tracking control of flapping wing micro-air vehicles with averaging theory

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CAAI Transactions on Intelligence Technology 2018年第1期3卷 18-27页

作者： Chen Qian Yongchun Fang Institute of Robotics and Automatic Information Systems College of Computer and Control Engineering Tianjin 300353People's Republic of China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin 300353 People's Republic of China

An input constrained adaptive tracking controller is designed for flapping micro aerial vehicles, wherein the moving averaging filter is adopted to estimate the averaged states of the system. Specifically, in the outer loop controller, an observer is constructed to estimate the disturbances within the system. Moreover, the constrained thrust is designed to keep the frequency in a proper region so as to meet the requirement of average estimation. Then, a tracking differentiator is used to provide trackable trajectories for the inner loop. Subsequently, a new quaternion- based hybrid attitude tracking controller is designed which successfully deals with high-frequency noises and avoids possible chattering. As supported by mathematical analysis, the proposed control strategy guarantees the uniform ultimate boundedness of the closed-loop system, and it keeps the control torques within the permitted range to meet the application requirement. At last, numerical simulations are carried out to support the validity of the proposed controller, whose results are satisfactory even when the thrust and torques are saturated.

关键词：飞行器跟踪器人工智能发展现状

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Seeking the Analytical Approximation of the Stance Dynamics of the 3D Spring-Loaded Inverted Pendulum Model By Using Perturbation Approach

Seeking the Analytical Approximation of the Stance Dynamics ...

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2019 IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Haitao Yu Shengjun Wang Kaizheng Shan Jun Li Lixian Zhang Haibo Gao State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China

The Spring-Loaded Inverted Pendulum (SLIP) has been widely exploited in both biomechanical and robotics research due to its simple form in mathematics and high accuracy in fitting experimental biology data. However the intrinsic nonlinearity of the SLIP dynamics makes accurate analytical representation unavailable. Traditional methods take advantage of numerical integration to handle this issue while several existing analytical approximations focusing on 2D-SLIP model. The 3D-SLIP suitable to physical reality is rarely investigated. This paper presents a novel perturbation-based approach to obtain the closed-form analytical approximations of the 3D-SLIP model in stance phase. In contrast to existing work ignoring the gravitational forces, the proposed approach just relies on assumptions of small leg compression and small leg swept angle. The performance of the derived approximations has been evaluated via comprehensive numerical analysis. The quality of accurate apex prediction promises the approximation as an advantageous and reliable tool for locomotion control of legged robots.

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Research on PMSM Vector control System Based on Fuzzy PI Parameter Self-Tuning

Research on PMSM Vector Control System Based on Fuzzy PI Par...

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IEEE International Conference on Power and Energy systems (ICPES)

作者： Jianhua Ding Tao Zhang Ran Tao Anna Wang Northeastern University Shenyang 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 Dalian University of Technology Dalian China

ISBN: (数字)9781665404945

ISBN: (纸本)9781665404952

Based on the analysis of the permanent magnet synchronous motor (PMSM) mathematical model, a PMSM rotor field-oriented double closed-loop vector control system is constructed. Aiming at the defects of slow speed response, large overshoot, severe torque ripple, and poor anti-disturbance ability in the traditional PI controller used in the system speed loop, a fuzzy PI parameter self-tuning controller is proposed. The controller can set the two parameters of proportional and integral online according to fuzzy control rules. The system simulation model is built based on Matlab/Simulink. The simulation results show that the fuzzy PI parameter self-tuning controller has the advantages of fast dynamic response, high steady-state precision and strong anti-disturbance capability.

关键词： PI control systems simulation Steady-state Mathematical model Torque measurement Machine vector control Matlab

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A Grasping CNN with Image Segmentation for Mobile Manipulating Robot

A Grasping CNN with Image Segmentation for Mobile Manipulati...

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IEEE International Conference on robotics and Biomimetics

作者： Yingying Yu Zhiqiang Cao Shuang Liang Zhicheng Liu Junzhi Yu Xuechao Chen State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Intelligent Robotics Institute BIT Beijing China

This paper presents a grasping convolutional neural network with image segmentation for mobile manipulating robot. The proposed method is cascaded by a feature pyramid network FPN and a grasping network DrGNet. The FPN network combined with point cloud clustering is used to obtain the mask of the target object. Then, the grayscale map and the depth map corresponding to the target object are combined and sent to the DrGNet network for providing multi-scale images. On this basis, depthwise separable convolution is used for encoding. The results of encoders are refined according to the light-weight RefineNet as well as sSE, which can achieve a better grasp detection. The proposed method is verified by the experiments on mobile manipulating robot.

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Design of Overall Framework of Self-Service Big Data Governance for Power Grid 4th

Design of Overall Framework of Self-Service Big Data Governa...

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4th International Conference on Machine Learning and intelligent Communications, MLICOM 2019

作者： Qiao, Lin Zhou, Qiaoni Song, Chunhe Wu, Hao Liu, Biqi Yu, Shimao State Grid Liaoning Electric Power Co. Ltd. Shenyang110000 China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9783030323875

At present, power grid companies have not formed a complete data quality control system and a comprehensive and effective data quality assurance mechanism, which restricts the deep mining of data value. In this paper, based on the full-service unified data center of power Grid Company, a general framework of self-service power grid big data governance is presented. Firstly, the related work of big data governance is reviewed;secondly, the architecture and characteristics of the grid company’s full-service unified data center are analyzed;thirdly, the related requirements of self-service grid big data governance are analyzed;fourthly, the overall framework of self-service grid big data governance is proposed. Compared with other general big data governance framework, the proposed framework considers the specification of power grid as well as the features of self-service, making the framework more feasible. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Big data

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A Data Quality Improvement Method Based on the Greedy Algorithm 4th

A Data Quality Improvement Method Based on the Greedy Algori...

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4th International Conference on Machine Learning and intelligent Communications, MLICOM 2019

作者： Wang, Zhongfeng Fu, Yatong Song, Chunhe Ge, Weichun Qiao, Lin Zhang, Hongyu Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China State Grid Liaoning Electric Power Co. Ltd. 110000 Shenyang China

ISBN: (纸本)9783030323875

High-quality data is very important for data analysis and mining. Data quality can be indicated by many indicators, and some methods have been proposed for data quality improvement by improving one or more data quality indicators. However, there is few work to discuss the impact of the processing order of data quality indicators on the overall data quality. In this paper, first, some data quality indicators and their improvement methods are given;second, the impact of the processing order of data quality indicators on the overall data quality is discussed, and then a novel data quality improvement method based on the greedy algorithm is proposed. Experiments have been shown that the proposed method can improves the data quality while reducing the time and computational costs. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Data reduction

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Research on optimization method of routing buffer linkage based on Q-learning

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Journal of Physics: Conference Series 2022年第1期2187卷

作者： Zhonghua Han Hongfeng Qi Daliang Chang Wei Gong Shilong Dai School of Information and Control Engineering Shenyang Jianzhu University Shenyang 110168 China. Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang 110016 China. Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China. Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang 110169 China.

According to the characteristics of the painting process of passenger car manufacturing enterprises, by formulating the routing buffer linkage rules based on the total renewal cost, the linkage process of the bus in the routing buffer is controlled, and an improved Q-learning (Q- The routing buffer of learning) algorithm quickly finds the optimal path method. According to the actual production situation, this method improves the dynamic parameters of the algorithm on the basis of the traditional Q-learning algorithm, and improves the optimization speed and accuracy of the algorithm by establishing the correlation between the work-in-process and its neighboring work-in-process in the current state. Through multiple sets of example simulation tests, the effectiveness of the Q-learning algorithm in solving the optimization problem of routing buffer linkage is verified.

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