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UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 430-445页

作者： Jiawen Kang Junlong Chen Minrui Xu Zehui Xiong Yutao Jiao Luchao Han Dusit Niyato Yongju Tong Shengli Xie IEEE School of Automation Guangdong University of TechnologyGuangzhou 510006China 111 Center for Intelligent Batch Manufacturing based on IoT Technology Guangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China School of Computer Science and Engineering Nanyang Technological UniversitySingaporeSingapore Pillar of Information Systems Technology and Design Singapore University of Technology and DesignSingaporeSingapore College of Communication Engineering Army Engineering University of PLANanjing 210007China National Natural Science Foundation of China Beijing 100085China Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of EducationGuangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangzhou 510006China

Avatars, as promising digital representations and service assistants of users in metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and

关键词： Avatar blockchain metaverses multi-agent deep reinforcement learning transformer UAVs

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Integrated Inspection of QoM, QoP, and QoS for AOI Industries in metaverses

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IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2071-2078页

作者： Yutong Wang Yonglin Tian Jiangong Wang Yansong Cao Shixing Li Bin Tian IEEE The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China North Automatic Control Technology Institute in Taiyuan ShanxiChina Qingdao Academy of Intelligent Industries Shandong 266109China

With the rapid development of information technologies such as digital twin, extended reality, and blockchain,the hype around "metaverse" is increasing at astronomical speed. However, much attention has been paid to its entertainment and social functions. Considering the openness and interoperability of metaverses, the market of quality inspection promises explosive growth. In this paper, taking advantage of metaverses, we first propose the concept of Automated Quality Inspection(Auto QI), which performs integrated inspection covering the entire manufacturing process, including Quality of Materials, Quality of Manufacturing(Qo M), Quality of Products, Quality of Processes(Qo P), Quality of Systems, and Quality of Services(Qo S). Based on the scenarios engineering theory, we discuss how to perform interactions between metaverses and the physical world for virtual design instruction and physical validation feedback. Then we introduce a bottomup inspection device development workflow with productivity tools offered by metaverses, making development more effective and efficient than ever. As the core of quality inspection,we propose Quality Transformers to complete detection task,while federated learning is integrated to regulate data sharing.In summary, we point out the development directions of quality inspection under metaverse tide.

关键词： Automated Optical Inspection metaverses Quality Inspection Transformers

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Parallel Sensing in metaverses: Virtual-Real Interactive Smart Systems for “6S” Sensing

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IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2047-2054页

作者： Yu Shen Yuhang Liu Yonglin Tian Xiaoxiang Na School of Artificial Intelligence University of Chinese Academy of ScienceBeijing 100190China IEEE The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Department of Engineering University of CambridgeTrumpington StreetCB21PZUnited Kingdom

In the construction of metaverses,sensors that are referred to as the“bridge of information transmission”,play a key role.The functionality and efficiency of today’s sensors,which operate in a manner similar to physical sensing,are frequently constrained by their hardware and software.In this research,we proposed the Parallel Sensing framework,which includes background,concept,basic methods and typical application of parallel sensing.In our formulation,sensors are redefined as the integration of real physical sensors and virtual software-defined sensors based on parallel intelligence,in order to boost the performance of the sensors.Each sensor will have a parallel counterpart in the virtual world within the framework of parallel sensing.Digital sensors serve as the brain of sensors and maintain the same properties as physical sensors.Parallel sensing allows physical sensors to operate in discrete time periods to conserve energy,while cloud-based descriptive,predictive,and prescriptive sensors operate continuously to offer compensation data and serve as guardians.To better illustrate parallel sensing concept,we show some example applications of parallel sensing such as parallel vision,parallel point cloud and parallel light fields,both of which are designed by construct virtual sensors to extend small real data to virtual big data and then boost the performance of perception models.Experimental results demonstrate the effective of parallel sensing framework.The interaction between the real and virtual worlds enables sensors to operate actively,allowing them to intelligently adapt to various scenarios and ultimately attain the goal of“Cognitive,Parallel,Crypto,Federated,Social and Ecologic”6S sensing.

关键词： Parallel Sensing Parallel Intelligence CPSS metaverses Virtual and Real Interaction “6S”Sensing.

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The DAO to MetaControl for MetaSystems in metaverses:The System of Parallel Control Systems for Knowledge Automation and Control Intelligence in CPSS

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1899-1908页

作者： Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing100190Chi the Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao999078China

An investigation and outline of MetaControl and DeControl in metaverses for control intelligence and knowledge automation are presented.Prescriptive control with prescriptive knowledge and parallel philosophy is proposed as the starting point for the new control philosophy and technology,especially for computational control of metasystems in cyberphysical-social systems.We argue that circular causality,the generalized feedback mechanism for complex and purposive systems,should be adapted as the fundamental principle for control and management of metasystems with metacomplexity in metaverses.Particularly,an interdisciplinary approach is suggested for MetaControl and DeControl as a new form of intelligent control based on five control metaverses:metaverses,MultiVerses,InterVerses,TransVerse,and DeepVerses.

关键词： MetaControl MetaSystems metaverses CPSS DAO DeControl Prescriptive Control Parallel Control Computational Control Knowledge Automation Control Intelligence Parallel Intelligence

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Parallel Factories for Smart Industrial Operations:From Big AI Models to Field Foundational Models and Scenarios Engineering

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IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2079-2086页

作者： Jingwei Lu Xingxia Wang Xiang Cheng Jing Yang Oliver Kwan Xiao Wang Qingdao Academy of Intelligent Industries Qingdao 266114 The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190 Motion G Inc.Singapore 068898Singapore IEEE School of Artificial Intelligence Anhui UniversityHefei 230031

The rapid advancement of fundamental theories and computing capacity has brought artificial intelligence,internet of things, extended reality, and many other new intelligent technologies into our daily lives. Due to the lack of interpretability and reliability guarantees, it is extremely challenging to apply these technologies directly to real-world industrial systems. Here we present a new paradigm for establishing parallel factories in metaverses to accelerate the deployment of intelligent technologies in real-world industrial systems: QAII-1.0. Based on cyber-physical-social systems,QAII-1.0 incorporates complex social and human factors into the design and analysis of industrial operations and is capable of handling industrial operations involving complex social and human behaviors. In QAII-1.0, a field foundational model called Eu Artisan combined with scenarios engineering is developed to improve the intelligence of industrial systems while ensuring industrial interpretability and reliability. Finally, parallel oil fields in metaverses are established to demonstrate the operating procedure of QAII-1.0.

关键词： Cyber-physical-social system(CPSS) Industry5.0 metaverses Parallel factories Parallel intelligence

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