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A Robust Visual Person-Following Approach for Mobile Robots in Disturbing Environments

IEEE systems JOURNAL 2020年第2期14卷 2965-2968页

作者： Pang, Lei Cao, Zhiqiang Yu, Junzhi Guan, Peiyu Chen, Xuechao Zhang, Weimin 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 Peking Univ Coll Engn State Key Lab Turbulence & Complex Syst BIC ESArDept Mech & Engn Sci Beijing 100871 Peoples R China Beijing Adv Innovat Ctr Intelligent Robots & Syst Beijing 100081 Peoples R China Beijing Inst Technol Intelligent Robot Inst Beijing 100081 Peoples R China

This article proposes a robust visual following approach with a deep learning-based person detector, a Kalman filter (KF), and a reidentification module. The KF is introduced to predict the position of the target person, and its state is updated by the associated detection result. To deal with severe distractions and even full occlusion, the reidentification module with an identification model, a verification model, and an appearance gallery is employed in multi-person disturbing environments. Without any customized markers, the proposed approach can follow the target person steadily, and it is robust to occlusion and posture changes of the target person. Experiments results validate the effectiveness of the proposed approach.

关键词： Mobile robots Detectors Cameras Visualization Robot vision systems Kalman filter (KF) mobile robot person detector person-following reidentification

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Resilient control under Quantization and Denial-of-Service: Co-designing a Deadbeat controller and Transmission Protocol

arXiv

引用

arXiv 2021年

作者： Liu, Wenjie Sun, Jian Wang, Gang Bullo, Francesco Chen, Jie State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Department of Control Science and Engineering Tongji University Shanghai201804 China Mechanical Engineering Department The Center of Control Dynamical Systems and Computation UC Santa BarbaraCA93106-5070 United States

This paper is concerned with the problem of stabilizing continuous-time linear time-invariant systems subject to quantization and Denial-of-Service (DoS) attacks. In this context, two DoS-induced challenges emerge with the design of resilient encoding schemes, namely, the coupling between encoding strategies of different signals, and the synchronization between the encoder and decoder. To address these challenges, a novel structure that is equipped with a deadbeat controller as well as a delicate transmission protocol for the input and output channels, co-designed leveraging the controllab.lity index, is put forward. When both input and output channels are subject to DoS attacks and quantization, the proposed structure is shown able to decouple the encoding schemes for input, output, and estimated output signals. This property is further corroborated by designing encoding schemes as well as conditions that ensure exponential stability of the closed-loop system. On the other hand, when only the output channel is subject to network phenomenon, the proposed structure can achieve exponential stabilization without acknowledgment (ACK) signals, in contrast to existing ACK-based results. Finally, a numerical example is given to demonstrate the practical merits of the proposed approach as well as the theory. © 2021, CC BY.

关键词： Denial-of-service attack

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A Collab.rative Communication-Qmix Approach for Large-scale Networked Traffic Signal control

A Collaborative Communication-Qmix Approach for Large-scale ...

引用

IEEE intelligent Transportation systems Conference (ITSC)

作者： Chen, Xiaoyu Xiong, Gang Lv, Yisheng Chen, Yuanyuan Song, Bing Wang, Fei-Yue 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 Chinese Acad Sci Beijing Engn Res Ctr Intelligent Syst & Technol Inst Automat Beijing Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr 3D Printing & Intelligent Beijing Peoples R China

ISBN: (纸本)9781728191423

Networked Traffic Signal control (NTSC) has become an essential component in intelligent Transportation systems (ITS). To satisfy both scale and coordination challenging requirements for large-scale networked traffic signal control, this paper proposes a Communication-Qmix (CQmix) approach based on Qmix and Long Short-Term Memory (LSTM) communication module correspondingly. Firstly, we apply Qmix as the foundation for balancing large-scale and effective optimization, benefiting from its centralized-training and decentralized-execution mechanism. Then a communication module based on LSTM is implemented for effective global coordination. Further, random origin-destination demands (ODs) about different maximum traffic flows and occurrence times are performed to adapt the actual traffic flow pattern in practice. We conduct experiments on both synthetic and complicated real Zhongguancun road networks, and the proposed CQmix demonstrates its superiority over the baseline methods.

关键词： Roads Conferences Memory management Transportation Collab.ration Optimization Convergence

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Urban trip generation forecasting based on gradient boosting algorithm 1

Urban trip generation forecasting based on gradient boosting...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Li, Zhishuai Xiong, Gang Zhang, Yu Zheng, Meng Dong, Xisong Lv, Yisheng Institute of Automation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing Municipal Institute of City Planning and Design Traffic Planning Department Beijing China

ISBN: (纸本)9781665433372

The four-step transportation model plays an important role in urban planning. The quality of the first phase, i.e. trip generation, determines the performance of the global course. The majority of trip generation forecasting models highly rely on mathematical derivation and have many predictor variables during the prediction, which leads to low accuracy of results and requires lab.riously hand-crafted design of input vectors. This paper is the first to introduce the gradient boosting decision tree (GBDT) algorithm for trip generation prediction, and harmonizes such a powerful machine learning method with traditional urban planning requirements to achieve better prediction performance. Unlike the commonly used linear regression method, GBDT can automatically perform feature selection and model the non-linear relationships between input and output variables. Experimental results on real-world residential travel census in Beijing prove that the GBDT model significantly outperforms the baseline and can forecast the trip generation more accurately. © 2021 IEEE.

关键词： Decision trees

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Society 5.0: Metaverse Facilitated Human-centric “5H” Services Across Cyber-Physical-Social Spaces

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IFAC-PapersOnLine 2024年第3期58卷 159-164页

作者： Xiao Wang Jun Huang QingHua Ni Zhe Wan HuaDan Xue Larry Stapleton Fei-Yue Wang Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei 230601 China Macau University of Science and Technology Macau 999078 China Beijing Normal University Beijing 100875 China Department of Radiology Peking Union Medical College Hospital Shuaifuyuan Beijing 100730 Dongcheng District China Brownsbarn Thomastown Co. Kilkenny Ireland R95D8P2 Qingdao Academy of Intelligent Industries Qingdao 266109 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

Cyber-Physical-Social systems bridge the gap between social resource organization and distribution, and give birth to Society 5.0, which represents a transformative vision aimed at realizing the human-centric paradigm for more efficient and effective sustainable development. In this paper, we discuss the decentralized paradigm in fostering human needs-driven smart services, in line with Maslow’s hierarchy of needs theory. Societies 5.0 is committed to optimizing the use of natural, artificial, and social resources by enhancing democratic participation and ensuring that decision-making processes are more transparent, reliable, and aligned with the needs of the populace in sustainable ways. Moreover, our discussion extends to how Society 5.0’s principles correlate with the United Nations Sustainable Development Goals (SDGs), providing a framework that supports health, education, and poverty reduction, among other targets. This approach also aligns with the Human Development Index (HDI), reinforcing the notion that technological progress in Society 5.0 does not overshadow but rather complements and enhances human values, paving a synergistic path towards sustainable development that is both advanced and fundamentally human-centric.

关键词： Cyber-Physical-Social systems Society 5.0 Human-Centric Services Socio-technical synergistic sustainable development

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Robotic Autonomous Grasping Technique: A Survey

Robotic Autonomous Grasping Technique: A Survey

引用

Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Lili Wang Zhen Zhang Jianhua Su Qipeng Gu the School of Artificial Intelligence University of Chinese Academy of Sciences Beijing P.R. China the State Key Lab. of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing P.R. China

ISBN: (纸本)9781665426312

This paper provides a comprehensive survey of robotic autonomous grasping techniques. We summarize three key tasks: grasp detection, affordance detection, and model migration. Grasp detection determines the graspable area and grasping posture of the manipulator, so that the robot can successfully perform the grasps. The grasp detection methods based on deep learning are divided into 3DoF grasp and 6DoF grasp. The object affordances based grasping methods can further improve the robot's understanding of objects and environment, thereby improving the robot's intelligence and autonomy. Methods for object affordances detection are classified as learning-based, knowledge-based, and simulation-based. Model migration means that when the grasping model is migrated to other scenes where lightness and background changes, only little or no lab.l data is required, so that the grasping model can be used in the target scene quickly and efficiently. This paper focuses on domain adaptation (DA) methods in model migration.

关键词： Deep learning Adaptation models Affordances Knowledge based systems Focusing Grasping Manipulators

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The Emerging intelligent Vehicles and intelligent Vehicle Carriers Collab.rative systems 35

The Emerging Intelligent Vehicles and Intelligent Vehicle Ca...

引用

IEEE intelligent Vehicles Symposium (IV)

作者： Shen, Zhipeng Huang, Chao Huang, Hailong Wang, Yutong Wang, Fei-Yue Jamalipour, Abbas Pham, Duc Truong Vlacic, Ljubo Savkin, Andrey V. Hong Kong Polytechn Univ Dept Aeronaut & Aviat Engn Hong Kong 999077 Peoples R China Hong Kong Polytechn Univ Dept Ind & Syst Engn Hong Kong 999077 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Engn Macau 999078 Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2050 Australia Univ Birmingham Dept Mech Engn Birmingham B15 2TT England Griffith Univ Inst Intelligent & Integrated Syst Brisbane Qld 4222 Australia Griffith Univ Sch Engn & Built Environm Brisbane Qld 4222 Australia Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

ISBN: (纸本)9798350348811;9798350348828

In this paper, we propose the innovative use of intelligent Vehicle Carriers (IVCs) as a key solution to address the energy constraints of small-scale unmanned intelligent Vehicles (IVs). IVCs function as both transporters and charging stations, significantly boosting the operational range and efficiency of IVs. Our research delves into the IV-IVC collab.rative framework, highlighting the existing challenges, exploring potential solutions, and examining a range of applications. This study offers a visionary approach to revolutionizing intelligent transportation systems by leveraging the synergistic relationship between IVs and IVCs.

关键词： intelligent vehicle highway systems

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Data-driven control of dynamic event-triggered systems with delays

arXiv

引用

arXiv 2021年

作者： Wang, Xin Sun, Jian Berberich, Julian Wang, Gang Allgöwer, Frank Chen, Jie State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart70550 Germany Department of Control Science and Engineering Tongji University Shanghai201804 China

This paper studies data-driven control of unknown sampled-data systems with communication delays under an event-triggering transmission mechanism. Data-based representations for stochastic linear systems with a known or an unknown system input matrix are first developed, along with a novel class of dynamic triggering schemes for sampled-data systems with time delays. A model-based stability condition for the resulting event-triggered time-delay system is established using a looped-functional approach. Combining this model-based condition with the data-driven representations, data-based stability conditions are derived. Building on the data-based conditions, methods for co-designing the controller gain and the event-triggering matrix are subsequently provided for both cases with or without using the input matrix. Finally, a numerical example is presented to corroborate the role of additional prior knowledge of the input matrix in reducing the conservatism of stability conditions, as well as the merits of our approaches relative to existing results. © 2021, CC BY.

关键词： Sampled data control systems

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An IoT Edge Computing System Architecture and its Application

An IoT Edge Computing System Architecture and its Applicatio...

引用

2020 IEEE International Conference on Networking, Sensing and control, ICNSC 2020

作者： Chen, Shichao Li, Qijie Zhang, Hua Zhu, Fenghua Xiong, Gang Tang, Ying Macau University of Science and Technology State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences China Beijing University of Technology State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Aerospace Smart Manufacturing Technology Development Co. Ltd Platform Research and Development Department Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Engineering Research Center of Intelligent Systems and Technology Institute of Automation Cloud Computing Center Chinese Academy of Sciences Beijing China Rowan University Department of Electrical and Computer Engineering GlassboroNJ United States Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (纸本)9781728168531

As the number of devices connected to the Internet of things (IoT) surges, the amount of data explodes. Therefore it not only increases the bandwidth load of data transmission but also aggravates the computing and storage load of a cloud platform. At the same time, the traditional computing paradigm centered on cloud computing cannot meet the real-time requirements in many application scenarios. The emergence of edge computing can solve the problems of realtime data processing and network bandwidth occupation in the current IoT scene. In this paper, according to the characteristics of IoT, such as fragmented data, heterogeneous network, and limited energy consumption, the architecture of an IoT edge computing system is constructed to suit better an IoT scene. In addition, the application of edge computing key technologies such as virtualization, edge intelligence, computing offload, collab.rative scheduling and micro-services in resource-constrained IoT scenarios is analyzed in detail. Finally, the functions and application of energy consumption monitoring and optimization to a central air-conditioning system are analyzed and summarized, which is a typical application of edge computing in the context of the IoT. © 2020 IEEE.

关键词： Edge computing

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YOLOv4 Based Deep Learning Algorithm for Defects Detection and Classification of Rail Surfaces

YOLOv4 Based Deep Learning Algorithm for Defects Detection a...

引用

IEEE intelligent Transportation systems Conference (ITSC)

作者： Anwar, Noreen Shen, Zhen Wei, Qinglai Xiong, Gang Ye, Peijun Li, Zhishuai Lv, Yisheng Zhao, Hongxia Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol Beijing Peoples R China Chinese Acad Sci Cloud Comp Ctr Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9781728191423

To investigate the safety of railway operations, it is significant to measure the technical defects of the rail transit. Traditional machine vision technology does not show better detection results due to the complicated and distinct features of the rail surface. This paper presents a deep learning algorithm based on YOLOv4 which better realise the rail surface's defect detection. Our proposed approach uses modified version of CSPDarkent-53 as the backbone, consequently achieve faster and more accurate detection outcomes. We use six types of datasets of different images of the rail surface for investigation of the defects and compare the outcomes with a different network. This technique is used to evaluate our proposed method's efficiency to detect and classify the defects by comparing them with other techniques. Moreover, the experimental outcomes depict the promising capability of our proposed method. The results represent that accuracy of this algorithm can reach up to 96.5% on the mAP metric. This approach has significant importance for the surface detection of defects in rail transit.

关键词： Rails Deep learning Training Measurement Production Object detection Rail transportation

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