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arXiv 2022年

作者： Jiang, Haodong Wang, Wentao Shen, Yuan Li, Xinghan Ren, Xiaoqiang Mu, Biqiang Wu, Junfeng China School of Data Science The Chinese University of HongKong Shenzhen China College of Control Science and Engineering The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China School of Mechatronic Engineering and Automation Shanghai University Shanghai China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

state estimation is an essential part of autonomous systems. Integrating the Ultra-Wideband (UWB) technique has been shown to correct the long-term estimation drift and bypass the complexity of loop closure detection. However, few works on robotics treat UWB as a stand-alone state estimation solution. The primary purpose of this work is to investigate planar pose estimation using only UWB range measurements. We prove the excellent property of a two-step scheme, which says we can refine a consistent estimator to be asymptotically efficient by one step of Gauss-Newton iteration. Grounded on this result, we design the GN-ULS estimator, which reduces the computation time significantly compared to previous methods and presents the possibility of using only UWB for real-time state estimation. Copyright © 2022, The Authors. All rights reserved.

关键词： state estimation

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Video Flame Recognition Based on Feature Fusion and Extreme Learning Machine 5

Video Flame Recognition Based on Feature Fusion and Extreme ...

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5th International Conference on Automation, control and robotics Engineering, CACRE 2020

作者： Zhong, Chen Tian, Zhijia Ding, Hongjun Wang, Ke Research Institute of Mem Shenyang Fire Science and Technology Shenyang China Harbin Institute of Technology State Key Laboratory of Robotics and Systems Harbin China

ISBN: (纸本)9781728198880

This paper proposes a video flame detection method based on Extreme Learning Machine (ELM). Visual Background Extractor++(ViBE++) algorithm is used to extract the dynamic foreground features of flame video images, and combined with color histogram threshold analysis, the flame region in the image is segmented. By extracting and processing the flame geometrical features such as roundness, sharpness and gravity center, the dynamic and static features of flame image are fused, thus the suspected flame area is screened out. Using the geometric features of suspected flame area and based on ELM model, the training and classification of sample sets are performed. Experimental results show that this method has higher operating speed and accuracy under the condition of less environmental interference. © 2020 IEEE.

关键词： Knowledge acquisition

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Bi-Directional Human-Robot Handover Using a Novel Supernumerary Robotic System

Bi-Directional Human-Robot Handover Using a Novel Supernumer...

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IEEE Workshop on Advanced robotics and its Social Impacts, ARSO

作者： Yuwei Du Xin Zhang Mattia Leonori Pietro Balatti Jing Jin Qiang Wang Arash Ajoudani Department of Control Science and Engineering School of Astronautics Harbin Institute of Technology Heilongjiang China Human-Robot Interfaces and Interaction (HRI2) Lab Istituto Italiano di Tecnologia Genova Italy State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

Cooperative human-robot systems are the key technology to improve efficiency in agile production. In this work, a novel on-shoulder supernumerary robotic limb (SRL) is developed for human-robot collaboration. The SRL system is mainly composed of a 6-degree-of-freedom (DoF) lightweight robotic arm and a wearable and adjustable base, which allows different users to regulate the arm installation position to fit their physical dimensions and expand their workspace. As the SRL is close to the human head, nullspace-based collision avoidance is designed to achieve safe motion planning. Meanwhile, a Kalman filter is designed to actively predict human hand position and yield anticipatory motion control of our SRL in handover tasks. Furthermore, we conduct a bi-directional handover case study to verify the capability of the SRL system and the effectiveness of the proposed collaborative handover. The result shows that our control method can guarantee intuitive and natural handover behavior.

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A method of laser focusing control in microlaser-induced breakdown spectroscopy

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Plasma Science and Technology 2019年第3期21卷 31-39页

作者： Wei WANG Lanxiang SUN Peng ZHANG Liming ZHENG Lifeng QI Wei DONG Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences Key Laboratory of Networked Control System Chinese Academy of Sciences

This paper presents a method for the automatic adjustment of the laser defocusing amount in micro-laser-induced breakdown spectroscopy. A microscopic optical imaging system consisting of a CCD camera and a 20× objective lens was adopted to realize the method. The real-time auto-focusing of the system was achieved by detecting the effective pixels of the light spot generated by the laser pointer. The focusing accuracy of the method could achieve 3 μm. The element concentrations of Mn and Ni in low-alloy steels were analyzed at a crater diameter of about 35 μm using the presented method. After using the presented method, the determination coefficients of Mn and Ni both exceeded 0.997, with the root-mean-square errors being 0.0133 and 0.0395, respectively. Scanning analysis was performed on the inclined plane and the curved surface by means of focusing control and non-focusing control. Ten characteristic spectral lines of Fe were selected as the analysis lines. With the focusing control, the average relative standard deviations obtained on the inclined plane and curved surface were both less than 5%, and much less than the values without focusing control, 14.6% and 40.39%.

关键词： LIBS microanalysis ablation crater focusing control

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Flow Sensing-Based Underwater Target Detection Using Distributed Mobile Sensors

Flow Sensing-Based Underwater Target Detection Using Distrib...

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IEEE Conference on Decision and control

作者： Jun Wang Tongsheng Shen Dexin Zhao Feitian Zhang Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing China National Innovation Institute of Defense Technology Beijing China Department of Advanced Manufacturing and Robotics and the State Key Laboratory of Turbulence and Complex Systems College of Engineering Peking University Beijing China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper presents a novel bioinspired underwater target detection method with distributed mobile sensors. Inspired by the fish's lateral line system, distributed mobile sensors are proposed to measure their respective local flow pressure and collectively estimate the parameters (e.g., position and dimension) of underwater targets with dynamic sensor trajectory planning. This paper formulates an optimal trajectory planning problem for a group of mobile sensors using information entropy as the cost function, with an aim to maximize the confidence level in the estimated target parameters. This paper in particular investigates the detection of a circular target in a uniform flow. A Bayesian filter is adopted to assimilate the distributed pressure measurements dynamically to estimate the position and radius of given circular targets. To verify the effectiveness of the proposed method, simulation is conducted with both fixed and moving circular targets in uniform flow. Simulation results are presented and analyzed.

关键词： Trajectory planning Simulation Object detection Robot sensing systems Sensor systems Entropy Sensors

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Spatial-nonlocality-induced non-Markovian electromagnetically induced transparency in a single giant atom

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Physical Review A 2022年第4期106卷 043710-043710页

作者： Y. T. Zhu S. Xue R. B. Wu W. L. Li Z. H. Peng M. Jiang Department of Automation Shanghai Jiao Tong University Shanghai 200240 People's Republic of China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 People's Republic of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 People's Republic of China Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing 100084 People's Republic of 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 and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha 410081 People's Republic of China School of Electronics and Information Engineering Soochow University Suzhou 215006 People's Republic of China

The nonlocal waveguide coupling of giant atoms can bring up novel physics and applications. In this paper, we find unconventional electromagnetically induced transparency in giant atoms that has not been reported so far. We present a consistent theory for describing the unconventional EIT which includes a real-space scattering method and a time-delayed master equation. Our analysis shows this phenomenon is a full quantum effect led by a quantized interaction between the atom and the waveguide. Our theory shows that it can be observed when the time delay between two neighboring coupling points is comparable to the relaxation time of the atom. This phenomenon results from the inherent nonlocality of the giant atom, which physically forces propagating fields to be standing waves in space and the atom exhibiting retardations in time.

关键词： Quantum optics with artificial atoms Scattering theory Quantum master equation

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Comprehensive Evaluation Index of Wind Turbine Operation Fatigue Based on Equivalent Economic Loss

Comprehensive Evaluation Index of Wind Turbine Operation Fat...

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International Conference on control, Automation, robotics and Vision (ICARCV)

作者： Jing He Shaolin Li Qi Yao State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing China Energy and Electricity Research Center Jinan University Zhuhai China

ISBN: (纸本)9781665476881

The fatigue analysis is important in the operation of the wind turbine. However, integrating the fatigue index of each substructure to construct the overall fatigue index of the wind turbine is difficult. Different from the existing research that only uses the weighted summation method to design the overall fatigue index, an index construction method that relates the operating fatigue to the economic loss is proposed. The cumulative damage of the structure is derived by using the damage equivalent load in the fatigue evaluation of each substructure of the wind turbine, and then the maintenance cost of each structure is analyzed in depth, and the maintenance cost function of each substructure is constructed to determine the economic loss equivalent to the cumulative damage of the structure. Since the proposed method converts fatigue damage into economic loss, the economic loss of each structure can be directly added to construct the overall equivalent economic loss index, which avoids the problem of custom weighting of the comprehensive index structure. Finally, simulation experiments verify the scientificity and superiority of the proposed indicator.

关键词： Costs Simulation Design methodology Optimal control Maintenance engineering Fatigue Wind turbines

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Online Non-Contact Weighing Duck Carcasses Using 2d Images and Deep Learning

SSRN

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SSRN 2022年

作者： Zhao, Yuliang Tian, Kaixuan Chen, Ruoyu Yang, Yongliang School of Control Engineering Northeastern University Hebei Qinhuangdao066001 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China

In the poultry meat processing industry, non-contact weighing methods based on computer vision have become increasingly popular due to their low costs and high efficiency. However, the accuracy of the traditional 2D image weighing techniques is reduced by the overlap and sway of carcasses. In this paper, we propose a more accurate regression model, Vggx, to weigh duck carcasses through image processing and deep learning. We adopt an image segmentation technique to obtain 2D images of a single duck section under a complex background. The images of the same duck are fused together to estimate its 3D information. Then, we use a convolutional neural network regression model to estimate the weight of duck carcass with the acquired 3D information. The Mean Absolute Percentage Error of our model is 1.46% and the average weight error of our model is 23.1 gram. Experimental results demonstrate that our method is high performance. © 2022, The Authors. All rights reserved.

关键词： Image segmentation

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Multi-hop reasoning over sparse knowledge graphs with deep reinforcement learning

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Expert systems with Applications 2025年 290卷

作者： Yiyang Liu Lejun Fu Yukai Fu Tianxing Wu Hongfei Bai State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 PR China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang 110016 PR China College of Information Engineering Shenyang University of Chemical Technology Shenyang 110142 PR China School of Computer Science and Engineering Southeast University Nanjing 211189 PR China

In recent years, knowledge reasoning has demonstrated substantial progress in inferring missing information, effectively alleviating the limitations caused by the incompleteness of knowledge graphs (KGs). Dealing with entities that have sufficient paths between them, multi-hop reasoning methods have achieved notable success and demonstrated interpretability, but they still exhibit deficiencies when dealing with sparse KGs. To overcome the insufficiency of edges in sparse KGs and the absence of effective guidance in multi-hop reasoning methods, a hybrid multi-hop collaborative Multi-Agent Sparse Knowledge Graph Reasoning model (MASKGR) is proposed. Attention-based dynamic completion (ADC) and variable rule guidance(VRG) are used to enhance the reasoning capability of deep reinforcement learning (RL) in sparse KGs. ADC focuses on high-value relations based on attention weights and combines embedding-based methods to predict the tail entities of these relations, expanding the original action space and improving the exploration capability of agents. Moreover, VRG greatly improves the ability of MASKGR to use rule-guided agent learning by utilizing the variable rule ratio and the variable loss reshaping. ADC can be applied to both single-agent and multi-agent in other models, while VRG is specifically designed for multi-agent. Comprehensive experiments have been conducted on four datasets: FB15K-237-10 %, FB15K-237-50 %, NELL23K, and WD-singer. Specifically, MASKGR achieves relative improvements of 1.3 % in Mean Reciprocal Rank (MRR) and 1.1 % in the proportion of correct tail entity rankings in the top 3 (Hit@3) on the FB15K-237-10 %, 2.4 % in MRR and 1.3 % in Hit@3 on the NELL23K, and 2.7 % in MRR and 3.6 % in Hit@3 on the WD-singer. The results indicate that MASKGR outperforms state-of-the-art multi-hop reasoning models based on reinforcement learning.

关键词： Deep reinforcement learning

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Enhanced Autonomous Exploration and Mapping of an Unknown Environment with the Fusion of Dual RGB-D Sensors

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Engineering 2019年第1期5卷 164-172页

作者： Ningbo Yu Shirong Wang Institute of Robotics and Automatic Information Systems Nankai University Tianjin 300353 China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin 300353 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China

The autonomous exploration and mapping of an unknown environment is useful in a wide range of applications and thus holds great significance. Existing methods mostly use range sensors to generate twodimensional (2D) grid maps. Red/green/blue-depth (RGB-D) sensors provide both color and depth information on the environment, thereby enabling the generation of a three-dimensional (3D) point cloud map that is intuitive for human perception. In this paper, we present a systematic approach with dual RGB-D sensors to achieve the autonomous exploration and mapping of an unknown indoor environment. With the synchronized and processed RGB-D data, location points were generated and a 3D point cloud map and 2D grid map were incrementally built. Next, the exploration was modeled as a partially observable Markov decision process. Partial map simulation and global frontier search methods were combined for autonomous exploration, and dynamic action constraints were utilized in motion control. In this way, the local optimum can be avoided and the exploration efficacy can be ensured. Experiments with single connected and multi-branched regions demonstrated the high robustness, efficiency, and superiority of the developed system and methods.

关键词： Autonomous exploration Red/green/blue-depth Sensor fusion Point cloud Partial map simulation Global frontier search

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