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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yanhao Li Hao Li École d’Ingénieurs SJTU-ParisTech (SPEIT) Shanghai China Shanghai Jiao Tong University (SJTU) Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management

Initial global localization is important to mobile robotics in terms of navigation initialization (or re-initialization) and loop closure in SLAM. 3D LiDARs are commonly used for mobile robotics, yet LiDAR-based initial global localization (especially at large scale such as in outdoor environments) is still challenging due to lack of salient features in LiDAR range data. Inspired by visual SLAM oriented initial global localization methods, we propose a method of LiDAR-based initial global localization using 2D submap projection image (SPI). For this, global descriptors from SPIs are extracted for place recognition; pose estimation is realized by feature point matching between the queried SPI and SPIs from a global map database. The proposed initial global localization module runs at 2.4 Hz with precision of 1.2 m and for translation and 1.2° for rotation, which can serve as a suitable initial estimate for subsequent pose estimation refinement via existing mature point cloud registration methods.

关键词： Location awareness Visualization Laser radar Simultaneous localization and mapping Three-dimensional displays Pose estimation Two dimensional displays

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Giant-cavity-based quantum sensors with enhanced performance

arXiv

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arXiv 2022年

作者： Zhu, Y.T. Wu, R.B. Peng, Z.H. Xue, Shibei Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Center for intelligent and networked systems Department of Automation Tsinghua University Beijing100084 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan Province Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China

Recent progresses have revealed that quantum systems with multiple position-dependent couplings, e.g., giant atoms, can exhibit some unconventional phenomena, such as non-exponential decay etc. However, their potential applications are still open questions. In this paper, we propose a giant-cavity-based quantum sensor for the first time, whose performance can be greatly enhanced compared to traditional cavity-based sensors. In our proposal, two cavities couple to a dissipative reservoir at multiple points while they couple to a gain reservoir in a single-point way. To detecting a unknown parameter using this sensor, a waveguide is coupled to one of the cavities where detecting fields can pass through for homodyne detection. We find that multiple position-dependent couplings can induce an inherent non-reciprocal coupling between the cavities, which can enhance the performance of sensors. Output noise in our scheme can be reduced to the shot noise level, which is about one order magnitude lower than the results in Ref. [Lau and Clerk (2018)]. Besides, the signal-to-noise ratio per photon is also enhanced by about one order of magnitude. These results show that the multiple-point-coupling structure is beneficial to nowadays quantum devices. Copyright © 2022, The Authors. All rights reserved.

关键词： Couplings

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A Many-Objective Evolutionary Algorithm Assisted by Ideal Hyperplane

SSRN

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SSRN 2022年

作者： Zhang, Zhixia Wen, Jie Wang, Hui Cai, Xingjuan Cui, Zhihua Zhang, Wensheng School of Computer Science and Technology Taiyuan University of Science and Technology China School of Information Engineering Nanchang Institute of Technology Nanchang330099 China State Key Laboratory of Intelligent Control and Management of Complex System Institute of Automation Chinese Academy of Sciences China State Key Lab. for Novel Software Technology Nanjing University China

In many-objective optimization problems (MaOPs), it is a difficult task for evolutionary algorithms to balance convergence and diversity while rapidly converging to the Pareto front. As the number of objectives increases, the difficulty of evolutionary algorithms increases with individual selection pressure. To address above problems, we propose a many-objective evolution algorithm assisted by an ideal hyperplane (MaOEA-IH). Firstly, the ideal hyperplane as a reference surface to guide the individual to search the Pareto front, which is established from the extremum of each dimension of the objective space. Secondly, a parallel p -norm mating selection strategy is proposed to improve convergence. In addition, two factors are considered in this strategy: 1) different p -norm are used to measure different spatial scales;2) individual selection uncertainty is defined by incorporating a probabilistic perturbation mechanism. Thirdly, the improved shift-based density estimation strategy is used as the evaluation criterion for environmental selection to improve the convergence and diversity of solution sets and enhance selection pressure. To verify the effectiveness of the proposed algorithm, MAOEA-IH is compared with seven excellent algorithms in three benchmark sets (DTLZ, WFG and MaF). The results show that the proposed MAOEA-IH is highly competitive in solving MaOPs. © 2022, The Authors. All rights reserved.

关键词： Geometry

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Research on Infrared Image processing Algorithm of Generator Carbon Brush Based on Adaptive Stochastic Resonance Array

Research on Infrared Image Processing Algorithm of Generator...

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Chinese Automation Congress (CAC)

作者： Shangbin Jiao Weichao Huang Bo Geng Youmin Zhang Xiaohui Wu Pengyue Wu Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi ’an University of Technology Xi’an China Concordia University Montreal Canada Huaneng Weihai Power Generation Co. Ltd Wei’ hai China Xi'an Thermal Power Research Institute Co. Ltd Xi’an China

ISBN: (纸本)9781665426480

The safe and stable operation of the generator set is related to the national economy and people's livelihood. As an important part of generator excitation system, carbon brush and slip ring temperature monitoring can effectively evaluate the state of the generator, which plays a vital role. Most of the current researches use infrared images to monitor carbon brush temperature, but there are often problems such as high noise and unclear focus. According to the requirements of infrared image display, this paper combines the method of bistable stochastic resonance to denoise the image, and on this basis, introduces the adaptive stochastic resonance array method to denoise the gray image. Experimental results show that compared with common image denoising methods and classic bistable stochastic resonance, the adaptive stochastic resonance array method has improved both visual effects and peak signal- to-noise ratio (PSNR), which further proves the good application of stochastic resonance in weak signal detection and extraction.

关键词： Temperature sensors Temperature measurement Brushes Stochastic resonance Adaptive arrays Generators Carbon

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PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators

arXiv

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arXiv 2023年

作者： Cong, Runmin Yang, Wenyu Zhang, Wei Li, Chongyi Guo, Chun-Le Huang, Qingming Kwong, Sam Institute of Information Science Beijing Jiaotong University Beijing100044 China School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China College of Computer Science Nankai University Tianjin300350 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China Department of Computer Science City University of Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

Due to the light absorption and scattering induced by the water medium, underwater images usually suffer from some degradation problems, such as low contrast, color distortion, and blurring details, which aggravate the difficulty of downstream underwater understanding tasks. Therefore, how to obtain clear and visually pleasant images has become a common concern of people, and the task of underwater image enhancement (UIE) has also emerged as the times require. Among existing UIE methods, Generative Adversarial Networks (GANs) based methods perform well in visual aesthetics, while the physical model-based methods have better scene adaptability. Inheriting the advantages of the above two types of models, we propose a physical model-guided GAN model for UIE in this paper, referred to as PUGAN. The entire network is under the GAN architecture. On the one hand, we design a Parameters Estimation subnetwork (Par-subnet) to learn the parameters for physical model inversion, and use the generated color enhancement image as auxiliary information for the Two-Stream Interaction Enhancement subnetwork (TSIE-subnet). Meanwhile, we design a Degradation Quantization (DQ) module in TSIE-subnet to quantize scene degradation, thereby achieving reinforcing enhancement of key regions. On the other hand, we design the Dual-Discriminators for the style-content adversarial constraint, promoting the authenticity and visual aesthetics of the results. Extensive experiments on three benchmark datasets demonstrate that our PUGAN outperforms state-of-the-art methods in both qualitative and quantitative metrics. The code and results can be found from the link of https://***/proj_***. © 2023, CC BY-NC-SA.

关键词： Discriminators

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Model-Free Sliding Mode Iterative Learning control for Cz Silicon Single Crystal Diameter

Model-Free Sliding Mode Iterative Learning Control for Cz Si...

引用

第三十九届中国控制会议

作者： Yin Wan Ding Liu Junchao Ren Congcong Liu Jie Sun National & Local Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

Aiming at the complex nonlinear dynamic time-varying characteristics for Czochralski（Cz） silicon single crystal growth process and the difficulty in modeling and controlling the crystal diameter by conventional mechanisms, based on the idea of data-driven modeling and control, this paper proposes an improved model-free sliding mode iterative learning control（MFA-SMILC） method. First, a data-driven model of crystal diameter is established using an extreme learning machine（ELM）with actual process data;Then, based on the compact-format dynamic linear（CFDL） data model, a discrete sliding mode control algorithm is used to design a data-driven controller structure for crystal diameters, and the stability of the iterative tracking error is verified by the stability analysis;Finally, the proposed MFA-SMILC controller is applied to silicon single crystal diameter control, and compared with the conventional model-free adaptive iterative learning control（MFA-ILC）, it is found that MFASMILC has faster response speed and convergence speed, which verifies the effectiveness of the proposed control method.

关键词： Cz silicon single crystal growth Crystal diameter Model free iterative learning control Sliding mode control

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Sensorless Speed control of PMSMs based on an Improved Particle Filter

Sensorless Speed Control of PMSMs based on an Improved Parti...

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2019 IEEE International Conference on Electrical, control and Instrumentation Engineering, ICECIE 2019

作者： Ren, Hai-Peng Nan, Zi-Yuan Li, Jie Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'An University of Technology Xi'an China Xi'An University of Technology Department of Electrical Engineering Xi'an China

ISBN: (纸本)9781728139395

Position sensors in permanent magnet synchronous motor (PMSM) are restrained in some applications because of the space restriction and the reliability of the system. A marginalized particle filter (MPF) can used to staminate the speed of the motor by combining a Kalman filter (KF) with a particle filter (PF). In the MPF algorithm, the rotor position of the permanent magnet synchronous motor (PMSM) is represented by a set of particles, and the rotor speed associated with each particle is estimated by using the KF. The PF here is used to handle the non-Gaussianity and nonlinearity of the system. In this paper, the uniform distribution of particles is proposed to replace the traditional Gaussian distribution of particles in the PF to get better performance at the low-speed range. The motor drive system prototype is built using a TMS320F28335 digital signal processor as a controller core. The proposed improved PF is used in the sensorless speed vector control PMSM system. The experimental results show that the proposed PF with a uniform distribution of initial particles enables more accurate speed estimation in the low-speed range compared to the conventional Gaussian distribution, while increasing number of particles also helps to improve the accuracy. © 2019 IEEE.

关键词： Kalman filters

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Private and Robust Distributed Nonconvex Optimization via Polynomial Approximation

arXiv

引用

arXiv 2021年

作者： He, Zhiyu He, Jianping Chen, Cailian Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Automatic Control Laboratory ETH Zürich Zürich8092 Switzerland

There has been work that exploits polynomial approximation to solve distributed nonconvex optimization problems involving univariate objectives. This idea facilitates arbitrarily precise global optimization without requiring local evaluations of gradients at every iteration. Nonetheless, there remains a gap between existing guarantees and practical requirements, e.g., privacy preservation and robustness to network imperfections. To fill this gap and keep the above strengths, we propose a Private and Robust Chebyshev-Proxy-based distributed Optimization Algorithm (PR-CPOA). Specifically, to ensure both the accuracy of solutions and the privacy of local objectives, we design a new privacy-preserving mechanism. This mechanism leverages the randomness in blockwise insertions of perturbed vector states and hence provides a stronger privacy guarantee in the scope of (α, β)-data-privacy. Furthermore, to gain robustness against network imperfections, we use the push-sum consensus protocol as a backbone and discuss its specific enhancements. Thanks to the purely consensus-type iterations, we avoid the privacy-accuracy trade-off and the bother of selecting proper step sizes in different settings. We rigorously analyze the accuracy, privacy, and complexity of the proposed algorithm. We show that the advantages brought by introducing polynomial approximation are maintained when all the above requirements exist. © 2021, CC BY-NC-SA.

关键词： Polynomial approximation

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Global Finite-Time Stabilization for a Chain of Integrators in the Presence of Actuator Saturation by Homogeneous Feedback

Global Finite-Time Stabilization for a Chain of Integrators ...

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IEEE Conference on Decision and control

作者： Tan Hou Yuanlong Li Zongli Lin Department of Automation Shanghai Jiao Tong University Shanghai P. R. China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA USA Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai P. R. China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA USA

This paper considers the problem of globally stabilizing a chain of integrators with actuator saturation in finite time. The proposed method is adapted from an existing scheduled low gain approach by relaxing the range of the value of the low gain parameter. Due to the special structure of the considered system, the proposed feedback law not only results in finite-time stability but also is homogeneous in the whole state space. The homogeneity of the proposed feedback law allows its implementation through the discretization of a compact set of the state space and thus obviation from numerical difficulties in the existing scheduled low gain design. A numerical example is presented to validate our theoretical results and demonstrate that our approach achieves faster convergence than the existing methods.

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Correction to: Visual Servoing of Unknown Objects for Family Service Robots

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Journal of intelligent & Robotic systems 2022年第4期106卷 1-1页

作者： Xin, Jing Dong, Caixia Zhang, Youmin Yao, Yumeng Gong, Ailing Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China Department of Mechanical Industrial and Aerospace Engineering Concordia University Montreal Canada

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