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A speed measurement method for underwater robots using an artificial lateral line sensor

SMART MATERIALS AND STRUCTURES 2022年第1期31卷 015011-015011页

作者： Zhang, Zhuoliang Zhou, Chao Cao, Zhiqiang Tan, Min Cheng, Long Deng, Sai Fan, Junfeng Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

Underwater robot technology has made considerable progress in recent years. However, due to the harsh environment and noise in the flow field near the underwater robots, it is difficult to measure some basic parameters, including swimming speed. The traditional speed measurement methods for underwater robots have the disadvantages of being limited by the environment and bulky. In order to overcome these shortcomings, an artificial lateral line (ALL) sensor based on cantilever structure was developed in this paper. According to the deformation of cantilever beam under water impact, the swimming speed of underwater robots can be measured. In addition, an 'end-to-end' calibration algorithm was proposed to calibrate the ALL sensor in the noisy environment, avoiding the complicated noise modeling and filter design process. To reduce the risk of overfitting, a hybrid loss function based on physical model was adopted. Compared with the classical calibration method, our method can reduce the error by 47.8%. Our sensor achieved an average absolute error of 0.07897 m s(-1), and can measure water speed up to 3 m s(-1).

关键词： artificial lateral line speed of underwater robot hybrid loss function calibration in noisy environment flow sensor

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Snoring detection based on a stretchable strain sensor

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Science China(Information Sciences) 2021年第7期64卷 224-226页

作者： Zhengwei LI Qingkun SONG Long CHENG Min TAN School of Automation Harbin University of Science and Technology State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences

Snoring is caused by the stenosis of the upper respiratory tract, which can lead to the vibration of the palatine *** brings not only harm to the snorer [1] but also sleep deprivation to those around them [2]. Furthermore, studies show that loud snorers have a 67%greater chance of suffering from a stroke and a 34%higher risk of having a heart attack. To identify people at risk from these diseases,snoring recording and analysis become highly important [3].

关键词： Information systems and Communication Service

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Parallel control for Continuous-Time Linear systems:A Case Study

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 919-928页

作者： Qinglai Wei Hongyang Li Fei-Yue Wang the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the University of Chinese Academy of Sciences Beijing 100049 Qingdao Academy of Intelligent Industries Qingdao 266109China the Institute of Systems Engineering Macao University of Science and Technology

In this paper,a new parallel controller is developed for continuous-time linear *** main contribution of the method is to establish a new parallel control law,where both state and control are considered as the *** structure of the parallel control is provided,and the relationship between the parallel control and traditional feedback controls is *** the situations that the systems are controllable and incompletely controllable,the properties of the parallel control law are *** parallel controller design algorithms are given under the conditions that the systems are controllable and incompletely ***,numerical simulations are carried out to demonstrate the effectiveness and applicability of the present *** Terms-Continuous-time linear systems,digital twin,parallel controller,parallel intelligence,parallel systems.

关键词： Index Terms—Continuous-time linear systems digital twin parallel controller parallel intelligence parallel systems

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Collaborative Scheduling of Computing Tasks for Edge Computing

Collaborative Scheduling of Computing Tasks for Edge Computi...

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2021 China Automation Congress, CAC 2021

作者： Liu, Yue Xiong, Gang Zhu, Fenghua Chen, Shichao Zhang, Liguo Liu, Xudong School of Artificial Intelligence and Automation Beijing University of Technology The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation The Cloud Computing Center Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Qingdao Academy of Intelligent Industries Chinese Academy of Sciences Beijing China Faculty of Information and Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781665426473

In the face of the rapid development of the IoT, the traditional cloud computing, while providing services for mobile intelligent devices, also brings problems such as network bandwidth occupation and communication delay. Edge computing can solve these problems to some extent. But too many offloaded tasks may result in an edge server becoming overloaded and execution delay of tasks increasing, the computing resources of idle edge devices are also not fully utilized. In order to make full use of the computing resources of idle edge devices in the local network and reduce the execution delay for computing tasks, this paper takes the average delay time for all end devices as the optimization objective and proposes an end-edge cooperative scheduling (EECS) model to schedule tasks for edge computing. The scheduling algorithm used in EECS model is improved based on MCT algorithm. We use the EdgeCloudSim to verify the EECS model and scheduling algorithm. The simulation results show that the average completion time of computing tasks in EECS model, compared with only single edge server (SEE) task computing model, can be reduced by up to three times. In addition, the system load can be balanced to a certain extent in local network. © 2021 IEEE

关键词： Edge computing

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Real-Time Scheduling of Autonomous Mining Trucks via Flow Allocation-Accelerated Tabu Search

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2022年第3期7卷 466-479页

作者： Zhang, Xiaotong Guo, Ao Ai, Yunfeng Tian, Bin Chen, Long Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia Univ Chinese Acad Sci Artificial Intelligence Dept Beijing 100049 Peoples R China Waytous Inc Qingdao 266109 Shandong Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Minist Emergency Management Key Lab Intelligent Min & Robot Beijing 100012 Peoples R China

Material transportation is an essential process in mining operations and accounts for a large portion of total energy consumption. With the development of automation technology, autonomous mining trucks have been deployed in open-pit mines to reduce transportation costs. Compared to manned mining trucks, autonomous trucks are more appropriate for automatic scheduling techniques due to their high informatization, high-precision perception, and sophisticated control. However, the existing truck scheduling research does not exploit autonomous trucks' strengths to perform more delicate control, but still takes a traditional approach toward autonomous trucks. In this paper, we propose a mixed-integer programming model for joint optimization of autonomous trucks trips and speeds to minimize energy consumption. To solve the proposed scheduling model, a novel tabu search algorithm is proposed, which consists of two parts: an improved flow allocation model with matching factor is formulated in the upper part to determine the optimal truck flow, and in the lower part a tabu search procedure guided with the optimal flow is developed. Based on the mathematical model and solution technique, we also propose a real-time scheduling system of autonomous trucks for the stochastic and dynamic mining environment. A coal mine in Inner Mongolia, China, for example, we verified the effectiveness of the proposed truck scheduling model and real-time scheduling approach. We demonstrate that the proposed allocation model effectively accelerates the tabu search procedure, and for short-term decisions, the proposed solution technique satisfies the computational requirements of the real-time scheduling system.

关键词： Mathematical models Fuels Autonomous vehicles Real-time systems Dynamic scheduling Load modeling Transportation Autonomous truck heuristic algorithm open-pit mine real-time scheduling

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Constructing public health evidence knowledge graph for decision-making support from COVID-19 literature of modelling study

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Journal of Safety Science and Resilience 2021年第3期2卷 146-156页

作者： Yunrong Yang Zhidong Cao Pengfei Zhao Dajun Daniel Zeng Qingpeng Zhang Yin Luo The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190China Shenzhen Artificial Intelligence and Data Science Institute(Longhua) ShenzhenChina School of Data Science City University of Hong Kong-Hong Kong SARChina

The needs of mitigating COVID-19 epidemic prompt policymakers to make public health-related decision under the guidelines of *** unstructured COVID-19 publications make it challenging for policymakers to obtain relevant *** graphs(KGs)can formalize unstructured knowledge into structured form and have been used in supporting decision-making ***,we introduce a novel framework that can ex-tract the COVID-19 public health evidence knowledge graph(CPHE-KG)from papers relating to a modelling *** screen out a corpus of 3096 COVID-19 modelling study papers by performing a literature assessment *** define a novel annotation schema to construct the COVID-19 modelling study-related IE dataset(CPHIE).We also propose a novel multi-tasks document-level information extraction model SS-DYGIE++based on the *** the model on the new corpus,we construct CPHE-KG containing 60,967 entities and 51,140 ***,we seek to apply our KG to support evidence querying and evidence mapping *** SS-DYGIE++(SpanBERT)model has achieved a F1 score of 0.77 and 0.55 respectively in document-level entity recognition and coreference resolution *** has also shown high performance in the relation identification *** evidence querying,our KG can present the dynamic transmissions of COVID-19 pandemic in different countries and *** evidence mapping of our KG can show the impacts of variable non-pharmacological interventions to COVID-19 *** demonstrates the quality of our KG and shows that it has the potential to support COVID-19 policy making in public health.

关键词： Evidence-based public health Knowledge graph COVID-19 Decision-making support

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Discrete-time Linear Tracking control Based on Parallel System Theory

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Gao, Junjie Chen, Mingliang Wei, Qinglai The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment China Nuclear Power Engineering Co. Ltd Guangdong Shenzhen518172 China

Inspired by parallel system theory, a novel parallel tracking controller is presented for discrete-time linear systems in this paper. The core point is to model the time derivative of system control, which makes the control system and the control law completely equivalent in mathematical form. First, the architecture of discrete-time linear parallel tracking control is introduced. Then, in the sight of the reachability of control systems, the pole assignment theorems are given. The merits of this presented method comparing with traditional feedback control are analyzed. Eventually, the efficiency of this method is proved through numerical results. © 2024 Gao, J., Chen, M. & Wei, Q.

关键词： Discrete time control systems

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Reinforcement Learning Based Variable Impedance control for High Precision Human-robot Collaboration Tasks 6

Reinforcement Learning Based Variable Impedance Control for ...

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6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Meng, Yan Su, Jianhua Wu, Jiaxi Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

ISBN: (纸本)9780738133645

Human-robot collaboration is an important area with great potential in intelligent manufacturing. Due to the diversity of collaboration tasks, robot collaboration skills should have the ability to adapt to different skills. However, problems such as skill expression and generalization are challenging. Meanwhile, the differences in the skills of various operators bring difficulties to collaborative robots. This work develops a variable impedance learning method for human-robot collaboration assembly. Unlike most previous work that mainly discussed a special human collaborator with the fixed impedance parameters, this work learns a robot impedance by reinforcement learning. We aim to make the inertia, damping, and stiffness parameters adaptive by Proximal Policy Optimization (PPO) algorithm. Hence, we can let the robot collaborate with various human collaborators to accomplish a high-precision assembly task. Two experiment results illustrate the validity of the proposed method. The detailed experimental videos are available at https://***/AJyjW2NwA74.

关键词： Service robots Force Collaboration Reinforcement learning Robot sensing systems Manipulators Trajectory

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Hovering control of an Underwater Vehicle

Hovering Control of an Underwater Vehicle

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2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022

作者： Cao, Yaozhong Song, Dalei Wang, Zhan Wang, Yu University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China Ocean University of China Department of Automation and Measurement Qingdao266100 China Zhejiang Provincial Energy Group Company Ltd Hangzhou310000 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

This paper aims to achieve the hovering control of an underwater vehicle with uncertain parameters based on cascade PID. First, the inner loops for each channels are designed respectively. Then, the outer loops including depth controller, horizontal position controller and yaw controller are designed. The vector transformation method are used to force the vehicle to approach the target point along approximately straight line in the horizontal plane. Simulation results demonstrate the stability and validity of the proposed method. © 2022 IEEE.

关键词： controllers

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A Convolutional Neural Network With Multi-scale Kernel and Feature Fusion for sEMG-based Gesture Recognition

A Convolutional Neural Network With Multi-scale Kernel and F...

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IEEE International Conference on Robotics and Biomimetics (IEEE ROBIO)

作者： Han, Lijun Zou, Yongxiang Cheng, Long Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China

ISBN: (纸本)9781665405355

The sEMG-based gesture recognition has great potential in human-computer interaction. However, the current approaches are far from optimal. In this paper, a novel convolutional neural network which combines the multi-scale kernal and feature fusion (MKFF-CNN) is proposed. This model could extract multi-scale features and make full use of these feature maps. One dataset called "gForce dataset" is recorded in this work, comprising 13 able-bodied participants. To verify the effectiveness of the proposed model, evaluations on both the gForce dataset and the Ninapro DB6 are conducted. The experimental results demonstrate that, MKFF-CNN achieves an accuracy of 97.65% on the gForce dataset, which is 1.39% higher than that of the subnetwork without early fusion, and at least 6.54% higher than that of the subnetwork with single-scale convolution kernel. On the Ninapro DB6, MKFF-CNN achieves 98.52% accuracy, which is 1.3% higher than the state-of-the-art works, showing the superiority of the proposed model.

关键词： Surface electromyography hand gesture recognition convolutional neural network multi-scale kernel feature fusion

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