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Chinese Automation Congress (CAC)

作者： Xiaoyan Gong Yifan Wen Xiwei Liu Hongxia Zhao Sifeng Jing Qiang Li Kai Gao Jilong Luo The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Institute of Smart Education Systems Qingdao China The 21st Century International School Beijing China Beijing NO. 2 Middle School Beijing China Beijing University of Chemical Technology Beijing China

ISBN: (纸本)9781665426480

Recently, with continuous implementation of education reform, AI (Artificial Intelligence) has been included in K-12 (Kindergarten to 12th grade) curricula and AI education is in full swing in China. However, standard, cost-efficient AI education platforms for K-12 schools are still not available. So in this paper, based on Jetson Nano, we developed a GPU-enhanced AI teaching platform including AI learning computers for students to develop AI-related algorithms and applications, AI computer clusters for whole classes, and modular auto-driving cars to run and test algorithms and applications.

关键词： Computers Automation Education Clustering algorithms Graphics processing units Automobiles Artificial intelligence

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GPO: Global Plane Optimization for Fast and Accurate Monocular SLAM Initialization

GPO: Global Plane Optimization for Fast and Accurate Monocul...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Sicong Du Hengkai Guo Yao Chen Yilun Lin Xiangbing Meng Linfu Wen Fei-Yue Wang PA2R2T Group the State Key Laboratory of Complex System Management and Control Institute of Automation Chinese Academy of Sciences Beijing China ByteDance AI Lab Beijing China

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Initialization is essential to monocular Simultaneous Localization and Mapping (SLAM) problems. This paper focuses on a novel initialization method for monocular SLAM based on planar features. The algorithm starts by homography estimation in a sliding window. It then proceeds to a global plane optimization (GPO) to obtain camera poses and the plane normal. 3D points can be recovered using planar constraints without triangulation. The proposed method fully exploits the plane information from multiple frames and avoids the ambiguities in homography decomposition. We validate our algorithm on the collected chessboard dataset against baseline implementations and present extensive analysis. Experimental results show that our method outperforms the ne-tuned baselines in both accuracy and real-time.

关键词： Cameras Simultaneous localization and mapping Optimization Matrix decomposition Transmission line matrix methods Estimation Three-dimensional displays

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Line-of-Sight Strategy-Based Path-Following system for a Multi-Joint Robotic Fish

Line-of-Sight Strategy-Based Path-Following System for a Mul...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Shijie Dai Chao Zhou Zhengxing Wu Min Tan Junzhi Yu School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Naval Research Academy Beijing China State Key Laboratory for Turbulence and Complex System BIC-ESAT College of Engineering Peking University Beijing China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

This paper proposes a real-time path-following control system for a multi-joint robotic fish. The mechanical structure and dynamic model of the robotic fish for pathfollowing are first described. Then, the framework of the path-following control algorithm is established based on the built dynamic model, including a modified line-of-sight (LOS) guidance law, an active disturbance rejection control (ADRC)based heading controller and a proportional-integral-derivative (PID)-based speed controller. Specially, the modified LOS strategy is designed to select the tracking points and also provide the desired heading angle. Afterwards, to overcome systematic uncertainties and environmental disturbances, the ADRC method is adopted to design the heading controller. Meanwhile, the PID controller is also developed to maintain an appropriate swimming speed. Finally, simulations in both linear-and circular-path following are presented to validate the effectiveness of the proposed method.

关键词： Robots Robot kinematics Task analysis Real-time systems Oscillators Mathematical model Force

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Road acceleration spectrum replication on Stewart platform with Three Variable controller

Road acceleration spectrum replication on Stewart platform w...

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第三十八届中国控制会议

作者： Hu Xiaoyu Wang Shoukun State Key Laboratory of Intelligent Control and Decision of Complex System School of Automation Beijing Institute of Technology

Comparing to the commonly used hydraulic three-axial shaking tables, multi-degree of freedom shaking systems implemented by Stewart platform have the merits of being portable, easy to install and with high load capacity, but are not well studied because of the inherent nonlinearity and strong coupling of Stewart platform. In order to replicate multi-degree of freedom road acceleration spectrum on Stewart platform, analysis of the system was carried out. Firstly, the system structure was introduced and the linearized platform kinematic equations, at the equilibrium position, were inducted. The models of actuators that drive the platform were simplified and the principles of three variable controller(TVC) were deduced briefly. Finally, MATLAB-Adams co-simulation environment was built to perform multi-degree of freedom road acceleration spectrum replication with TVC. The results show that it can reproduce high accuracy multi-degree of freedom road acceleration spectrum with TVC on Stewart platform.

关键词： Shaking table Stewart platform three variable controller

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A Heuristic Algorithm for Solving Resource Constrained Project Scheduling Problem with Transfer Time under Resource Bundle

A Heuristic Algorithm for Solving Resource Constrained Proje...

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第三十八届中国控制会议

作者： Junqi Cai Zhihong Peng School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex System

In this paper we investigate an extension of Resource Constrained Projected Scheduling Problem(RCPSP): the Resource Constrained Project Scheduling Problem with Transfer Time under Resource Bundle. For this complex problem, the use of a parallel scheduling scheme is proposed. Such scheme has been successfully applied to the RCPSP. Introducing transfer time and resource bundle into RCPSP causes two problems, one is the possibility of resources significantly exceed the need of a task, the other is cooperation among unfamiliar members from different team may increase the makespan of the project. In order to deal with the problem, two new concepts are developed: task-related resource weight and collaboration coefficient. Some discussion of such concepts is given of and use them for new heuristic framework we proposed. A computational test is performed and the results show that the new heuristic is very effective in finding better solutions within a very short time.

关键词： RCPSP heuristic algorithm resource bundle transfer time

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Parallel Hospital: ACP-Based Hospital Smart Operating system

Parallel Hospital: ACP-Based Hospital Smart Operating System

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Yongjun Wang Fei-Yue Wang Xiao Wang Zhongjun Guan Liwei Ouyang Jiangong Wang Lan Yan Wenbo Zheng Wenwen Zhang Beijing Tiantan Hospital Capital Medical University Beijing Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Software Engineering Xi'an Jiaotong University Xi'an China

This article aims to propose a parallel hospital based on parallel medicine. The parallel hospital takes the parallel idea of virtual and real interaction as its core concept and uses parallel idea's artificial scenarios, computational experiments, and parallel execution (ACP) theory as the guiding theory to construct a new generation of intelligent hospital management system with virtual and real interaction. We conducted a case study of the parallel hospital practice case of Tiantan Hospital-Tiantan Smart Brain, so as to confirm the feasibility and scientificity of the proposed parallel hospital.

关键词： Training Hospitals Operating systems Digital twin Conferences

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Real-world Robot Reaching Skill Learning Based on Deep Reinforcement Learning

Real-world Robot Reaching Skill Learning Based on Deep Reinf...

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第32届中国控制与决策会议

作者： Naijun Liu Tao Lu Yinghao Cai Rui Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences

Traditional programming method can achieve certain manipulation tasks with the assumption that robot environment is known and ***,with robots gradually applied in more domains,robots often encounter working scenes which are complicated,unpredictable,and *** overcome the limitation of traditional programming method,in this paper,we apply deep reinforcement learning(DRL) method to train robot agent to obtain skill *** policy trained with DRL on real-world robot is time-consuming and costly,we propose a novel and simple learning paradigm with the aim of training physical robot ***,our method train a virtual agent in an simulated environment to reach random target position from random initial ***,virtual agent trajectory sequence obtained with the trained policy,is transformed to real-world robot command with coordinate transformation to control robot performing reaching *** show that the proposed method can obtain self-adaptive reaching policy with low training cost,which is of great benefits for developing intelligent and robust robot manipulation skill system.

关键词： Robot reaching skill learning deep reinforcement learning

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An Interpretability Analysis of Travel Decision Learning in Cyber-Physical-Social-systems

An Interpretability Analysis of Travel Decision Learning in ...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Miao Guo Peijun Ye Xudong Liu Sheng Liu Gang Xiong Liguo Zhang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Information and Technology Beijing University of Technology Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Beijing China

In the few decades, the rise of Internet and social media has fundamentally changed the management, control, and operation modes of modern engineering and social systems like defense, energy, critical health care and ... 详细信息

关键词： Deep learning Social networking (online) Scalability Perturbation methods Neurons Transportation Transforms

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Joint Face Alignment and 3D Face Reconstruction with Efficient Convolution Neural Networks

Joint Face Alignment and 3D Face Reconstruction with Efficie...

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International Conference on Pattern Recognition

作者： Keqiang Li Huaiyu Wu Xiuqin Shang Zhen Shen Gang Xiong Xisong Dong Bin Hu Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

3D face reconstruction from a single 2D facial image is a challenging and concerned problem. Recent methods based on CNN typically aim to learn parameters of 3D Morphable Model (3DMM) from 2D images to render face alignment and 3D face reconstruction. Most algorithms are designed for faces with small, medium yaw angles, which is extremely challenging to align faces in large poses. At the same time, they are not efficient usually. The main challenge is that it takes time to determine the parameters accurately. In order to address this challenge with the goal of improving performance, this paper proposes a novel and efficient end-to-end framework. We design an efficient and lightweight network model combined with Depthwise Separable Convolution and Muti-scale Representation, Lightweight Attention Mechanism, named Mobile-FRNet. Simultaneously, different loss functions are used to constrain and optimize 3DMM parameters and 3D vertices during training to improve the performance of the network. Meanwhile, extensive experiments on the challenging datasets show that our method significantly improves the accuracy of face alignment and 3D face reconstruction. Model parameters and complexity of our method are also improved greatly.

关键词： Training Solid modeling Three-dimensional displays Convolution Face recognition Neural networks complexity theory

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The Prediction of the Epidemic Trend of COVID-19 Using Neural Networks

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IFAC-PapersOnLine 2020年第5期53卷 857-862页

作者： Jing Yang Zhen Shen Xisong Dong Xiuqin Shang Wei Li Gang Xiong J. Yang is with the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China and also with College of Information Sciences & Technology Beijing University of Chemical Technology Beijing 100029 China. Z. Shen is with the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China and also with the Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao Shandong 266113 China. X. Dong X. Shang and W. Li are with the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China. G. Xiong is with Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China and also with the Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan Guangdong 523808 China.

In this paper, a BP neural network and an LSTM network are applied respectively to the prediction of Coronavirus Disease 2019 (COVID-19) in Wuhan, China and South Korea. The methods do not require specific theories of modelling and the predicted values can be obtained as long as the conventional parameters are set. The mean absolute percentage error (MAPE) of all the experiments are below 5% and the values of the determinable coefficient R are all larger than 0.9. The experiments show that the models can fit the actual values well and make relatively accurate predictions. As of March 29, 2020, the cumulative number of confirmed cases in Wuhan is expected to reach 50,068 using BP neural networks and 49,972 using LSTM network, respectively. As of April 13, 2020, the cumulative number of confirmed cases in South Korea is expected to reach 8,862 using BP neural networks and 8,716 using LSTM network, respectively. The models of neural networks are effective in predicting the trend of the COVID-19 epidemic, which is meaningful to prevent and control the epidemic.

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