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arXiv 2020年

作者： Qiao, Zhijian Hu, Hanjiang Shi, Weiang Chen, Siyuan 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 Mechanical Engineering Carnegie Mellon University United States Department of Computer Science and Technology University of Cambridge United Kingdom

In the field of large-scale SLAM for autonomous driving and mobile robotics, 3D point cloud based place recognition has aroused significant research interest due to its robustness to changing environments with drastic daytime and weather variance. However, it is time-consuming and effort-costly to obtain high-quality point cloud data for place recognition model training and ground truth for registration in the real world. To this end, a novel registration-aided 3D domain adaptation network for point cloud based place recognition is proposed. A structure-aware registration network is introduced to help to learn features with geometric information and a 6-DoFs pose between two point clouds with partial overlap can be estimated. The model is trained through a synthetic virtual LiDAR dataset through GTA-V with diverse weather and daytime conditions and domain adaptation is implemented to the real-world domain by aligning the global features. Our results outperform state-of-the-art 3D place recognition baselines or achieve comparable on the real-world Oxford RobotCar dataset with the visualization of registration on the virtual dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

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Research on High Precision control of Joint Position Servo system for Hydraulic Quadruped Robot

Research on High Precision Control of Joint Position Servo S...

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第三十八届中国控制会议

作者： CHEN Zhihua WANG Shoukun XU Kang WANG Junzheng ZHAO Jiangbo NIU Shanshuai Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information Technology School of Automation Beijing Institute of Technology

Electro-hydraulic servo valve-controlled cylinder(EHSVCC) is a highly integrated and high-performance electrohydraulic servo driving unit(EHSDU), which is the basic driving unit of the leg joint for hydraulic quadruped robot. The robot impedance control mostly adopts control methods based on position inner loop and impedance outer loop, the high precision control of position inner loop is the key to improve impedance control. Therefore, it is of great significance to study a high precision position control method. Firstly, the electro-hydraulic servo drive unit of the foot robot is taken as the research object. The model of the nonlinear system is established, and the feedback linearization of the system is carried out. Secondly, in order to improve the tracking performance and anti-jamming ability of the position inner loop input/output a sliding mode variable structure robust controller(SMVSRC) with switching function, variable structure control rate and improved boundary layer function is designed. Finally, the electro-hydraulic servo drive unit(EHSDU) and the hydraulic foot robot test platform are tested, the influence of the two control algorithms on the position response and tracking error for EHSDU is analyzed and compared under the control of sinusoidal signals with different amplitudes and frequencies and diagonal gait signals. The experimental results show that the improved SMVSRC algorithm is feasible and effective, which provides a theoretical basis for the later study of impedance control based on position inner loop.

关键词： Hydraulic quadruped robot Position control Feedback linearization Sliding mode controller

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Sensorless control of a Tubular Oscillatory LPMSM based on MARS and Deadbeat Current Predictive control

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IOP Conference Series: Materials Science and Engineering 2020年第6期768卷

作者： Kang Lu Tao Wu Haonan Chen He Wang Jia Gao School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China

In the vector control system of the tubular oscillatory linear permanent magnet (PM) synchronous motor (LPMSM), it is difficult for traditional mechanical sensors to accurately obtain the feedback information under harsh conditions. In this work, a speed sensorless vector control system is designed and applied to the tubular oscillatory LPMSM which adopts the model reference adaptive system (MRAS), and the deadbeat current predictive control (DCPC) is also adopted to replace the traditional PI current loop to establish the coordination controller, which overcomes the difficulties of global optimization and PI control parameter setting. And the method is proved by the simulation results and it gets better dynamic performance.

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Anchor-based spatio-temporal attention 3D convolutional networks for dynamic 3D point cloud sequences

arXiv

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arXiv 2020年

作者： Wang, Guangming Chen, Muyao Liu, Hanwen Yang, Yehui Liu, Zhe Wang, Hesheng The Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment 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 The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science and Technology University of Cambridge

With the rapid development of measurwement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perception most focus on the image or video, but deep learning methods for dynamic 3D point cloud sequences are underexplored. Therefore, developing efficient and accurate perception method compatible with these advanced instruments is pivotal to autonomous driving and service robots. An Anchor-based Spatio-Temporal Attention 3D Convolution operation (ASTA3DConv) is proposed in this paper to process dynamic 3D point cloud sequences. The proposed convolution operation builds a regular receptive field around each point by setting several virtual anchors around each point. The features of neighborhood points are firstly aggregated to each anchor based on the spatio-temporal attention mechanism. Then, anchor-based 3D convolution is adopted to aggregate these anchors' features to the core points. The proposed method makes better use of the structured information within the local region and learns spatio-temporal embedding features from dynamic 3D point cloud sequences. Anchor-based Spatio-Temporal Attention 3D Convolutional Neural Networks (ASTA3DCNNs) are built for classification and segmentation tasks based on the proposed ASTA3DConv and evaluated on action recognition and semantic segmentation tasks. The experiments and ablation studies on MSRAction3D and Synthia datasets demonstrate the superior performance and effectiveness of our method for dynamic 3D point cloud sequences. Our method achieves the state-of-the-art performance among the methods with dynamic 3D point cloud sequences as input on MSRAction3D and Synthia datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

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key technologies of GNSS/INS/VO deep integration for UGV navigation in urban canyon 11

Key technologies of GNSS/INS/VO deep integration for UGV nav...

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2017 11th Asian control Conference, ASCC 2017

作者： Meiling, Wang Yafeng, Li Guoqiang, Feng Yi, Yang Tong, Liu Xiao, Xuan School of Automation State Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781509015733

As a new emerging integrated navigation technology, the GNSS/INS/VO deep integration system can fully leverage the synergistic and complementary characteristics of the three involved subsystems to achieve cost reduction while improving navigation accuracy, availability, and robustness, by mutual aiding between subsystems. In this paper, firstly, based on a brief review of the technical background and research status of the GNSS/INS/VO deep integration technology, the processing flows of this deep integration mode in both signal and information domains are analyzed, and its performance advantages are summarized. Then, the top-level technology roadmap of the GNSS/INS/VO deep integration for UGV navigation in the urban canyon is presented, and several key technical issues from scheme demonstration to engineering implementation are explored. At last, the short and medium term development prospects of the GNSS/INS/VO deep integration are envisaged. The proposed research ideas and system design concept for GNSS/INS/VO deep integration could be used as a reference for relevant technicians in navigation field. © 2017 IEEE.

关键词： Navigation

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Establishment and Optimization of Gas Flow Prediction Model for Annealing Furnace Based on GA-SVM 37

Establishment and Optimization of Gas Flow Prediction Model ...

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37th Chinese control Conference, CCC 2018

作者： Qu, Qilin Yuan, Yan Yali, Jin Zhang, Yongyue China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan430074 China School of Information Science and Engineering Central South University Changsha410083 China

ISBN: (纸本)9789881563941

T0616. The temperature accuracy of the steel coil is directly affected the production quality of the steel plate. In the annealing furnace temperature control system, strip temperature control is mainly decided by various furnace annealing furnace gas flow in the area, so setting up a precise model between gas flow and temperature has become an important problem of the control system of annealing furnace. In this paper, SVM is applied to the prediction model of the gas flow in the annealing furnace since it's suitable for nonlinear and high dimensional problems as a machine learning method. In view of that the choice of C, G and the model parameters of the SVM directly affect the prediction precision of the model, this paper uses GA to optimize these two parameters. Finally, we use the data of field production as training and testing samples to establish and verify the model. The experimental results showed that the established model well achieved the requirements of the accuracy of the target. © 2018 Technical Committee on control Theory, Chinese Association of automation.

关键词： Flow of gases

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Hybrid protocol for distributed non-differentiable extended monotropic optimization

Hybrid protocol for distributed non-differentiable extended ...

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International Conference on control and automation (ICCA)

作者： Xia Jiang Xianlin Zeng Jian Sun Jie Chen Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Beijing Advanced Innovation Center for Intelligent Robots and Systems (Beijing Institute of Technology) Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology) Ministry of Education Beijing China School of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

This paper presents a hybrid protocol design for distributed non-differentiable extended monotropic optimization problems, which have various applications in large-scale optimization and evolutionary computation. The considered objective function is the sum of local non-differentiable objective functions, which are assigned to different agents in multi-agent networks, with local set constraints and affine equality constraints. Each agent can only access to its local non-differentiable objective function, local set constraint and equality constraint, and exchange information with its neighbors to obtain the global optimal solution. For this type of optimization problems, we propose a distributed state-dependent hybrid method over multi-agent networks. In addition, we properly design the jump map and jump set to improve the transient performance and accelerate the consensus process of existing distributed continuous-time methods. With invariance principle of hybrid dynamical systems, we prove that the proposed hybrid protocol converges to the global optimal solution. We verify that the proposed hybrid method has a good convergence property and transient performance through numerical experiments.

关键词： Optimization Protocols Linear programming Dynamical systems Heuristic algorithms Convergence Transient analysis

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Research on a Thermostatic Heating control system for Optically Pumped Cesium Magnetometer

Research on a Thermostatic Heating Control System for Optica...

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第37届中国控制会议

作者： Shuting Hu Jian Ge Haobin Dong Liyue Xue Zhiwen Yuan Jun Zhu HaiYang Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System

Since the operating temperature of absorption chamber affects the sensitivity of the optically pumped cesium magnetometer（OPCM） directly, it is necessary to control the temperature precisely. In this paper, by using the positive temperature coefficient（PTC） and the integral separation proportional-integral-derivative（PID） algorithm, a high-precision thermostatic heating system is designed for OPCM. Firstly, the PTC heating device and the TSic506 temperature sensor are used to form a closed loop control system. Then, the three parameters of P, I and D are adjusted for different temperature, and the temperature control system is realized by STM32 microcontroller. Finally, the integral separation PID algorithm is used to eliminate overshoot. Experiments show that the effective temperature control ranges are 45?C5?C, the accuracy is less than ±0.2?C, and the system stability time is 300 s. It is obviously that the designed system has reference value and guidance significance for OPCM.

关键词： Thermostatic heating control system Integral separation PID algorithm Optically pumped cesium magnetometer

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Information Reconstruction Error Evaluation Method Based on Singular Entropy Method

Information Reconstruction Error Evaluation Method Based on ...

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China International Electrical and Energy Conference (CIEEC)

作者： Shiming Liu Guochao Zhao Xisong Zhu Yuhao Zhang Xiangning Lin Key Laboratory of System Intelligent Dispatch and Control Shandong University Shandong China State Key Laboratory of Advanced Electromagnetic Engineering and Technology Huazhong University of Science and Technology Hubei China

ISBN: (数字)9781728116754

ISBN: (纸本)9781728116761

Communication failures of digital substations can result in loss of sampled information data. Information reconstruction algorithms are often used to reconstruct missing sample data, which causes errors in the amplitude and phase of the Fourier filter calculation. Studies have shown that the amount and location of sampled data loss has a direct impact on the size of the reconstruction error. Combining the singular value decomposition and information entropy theory, this paper comprehensively considers the influencing factors of reconstruction error, studies the correlation between reconstruction error and sampling data singular entropy, and proposes a reconstruction error estimation method based on singular entropy. This method is used to evaluate the reconstruction errors of three interpolation methods.

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control-oriented fault detection of solid oxide fuel cell system unknown input on fuel supply

Control-oriented fault detection of solid oxide fuel cell sy...

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作者： Wu, Xiao-long Xu, Yuan-wu Xue, Tao Shuai, Junchao Jiang, Jianhua Deng, Zhonghua Fu, Xiaowei Li, Xi School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science & Technology Wuhan China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Wuhan Industrial Economic Development Research Center Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China

As the solid oxide fuel cell (SOFC) system work environment is a high-temperature environment for a long time, it is difficult to obtain the SOFC stack internal state change directly. When the fault occurs, it is difficult to determine where the fault occurs. Moreover, the existing literature ignores the impact of faults, which creates many problems for SOFC system control. Therefore, a state observer-based fault detection method, which is used to detect the input flow sensor fault and the fuel input fault, is proposed. Their advantage is that they do not need data processing. To realize the fault detection, the observer is used to track the changes of SOFC stack chamber temperature. To obtain the observer estimation parameter, an approach from the actual stack structure parameters is employed to approximate the observer parameters. The results show the proposed fault detect method can judge fuel input fault type quickly and shield the disturbances signals from the sensor effectively. The proposed method also can be used to other operating points or air input fault. © 2018 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd

关键词： Fault detection

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