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Teleoperation of an Industrial Robot using Public Networks and 5G SA Campus Networks

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Teleoperation of an Industrial Robot using Public Networks a...

IEEE International Conference on Robotic Computing (IRC)

作者： Jannik Rohde Olga Meyer Quy Luu Duc Christoph Jürgenhake Talib Sankal Roman Dumitrescu Robert H. Schmitt Competence Center DigITools for Production Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany Systems Engineering Fraunhofer Institute for Mechatronic Systems Design IEM Paderborn Germany Digital Infrastructures Fraunhofer Institute for Production Technology IPT Aachen Germany Heinz Nixdorf Institute University of Paderborn Paderborn Germany Laboratory for Machine Tools and Production Engineering WZL of RWTH Aachen University Aachen Germany

ISBN: (纸本)9781665472616

The next generation of cellular mobile communication standard 5G is considered to have more stable and secure communication links, low latency and higher flexibility which can be enablers for various new scenarios and applications. Teleoperation is the field that can benefit greatly from 5G technology. Demanding and mission-critical use cases such as telesurgery have challenging requirements like low latency and high reliability of the communication channel, which can be possibly met by 5G in the future. This work presents a 5G architecture for the teleoperation of an industrial robot over long distance. For this purpose three Fraunhofer Institutes were connected over public internet and 5G campus networks. Furthermore, this work describes the latency performance of the investigated teleoperation use case. The measurements in the experiment are performed with two 5G standalone (SA) networks and an edge cloud.

关键词： Cloud computing 5G mobile communication Service robots Mission critical systems Computer architecture Communication channels Reliability

Adversarial Attacks and Defenses for Semantic Communication in Vehicular Metaverses

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arXiv 2023年

作者： Kang, Jiawen He, Jiayi Du, Hongyang Xiong, Zehui Yang, Zhaohui Huang, Xumin Xie, Shengli The School of Automation Guangdong University of Technology Guangzhou510006 China The Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China The Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China The School of Computer Science and Engineering Nanyang Technological University Singapore The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310007 China Hangzhou310007 China Zhejiang Laboratory Hangzhou31121 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore

For vehicular metaverses, one of the ultimate user-centric goals is to optimize the immersive experience and Quality of Service (QoS) for users on board. Semantic Communication (SemCom) has been introduced as a revolutionary paradigm that significantly eases communication resource pressure for vehicular metaverse applications to achieve this goal. SemCom enables high-quality and ultra-efficient vehicular communication, even with explosively increasing data traffic among vehicles. In this article, we propose a hierarchical SemCom-enabled vehicular metaverses framework consisting of the global metaverse, local metaverses, SemCom module, and resource pool. The global and local metaverse are brand-new concepts from the metaverse’s distribution standpoint. Considering the QoS of users, this article explores the potential security vulnerabilities of the proposed framework. To that purpose, this study highlights a specific security risk to the framework’s SemCom module and offers a viable defense solution, so encouraging community researchers to focus more on vehicular metaverse security. Finally, we provide an overview of the open issues of secure SemCom in the vehicular metaverses, notably pointing out potential future research directions. © 2023, CC BY.

关键词： Quality of service

Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey

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arXiv 2024年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Fang, Yuguang Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia Department of Computer Science City University of Hong Kong Kowloon Hong Kong The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of intelligent modules in the Internet of Vehicles (IoV) by synthesizing augmented datasets, completing sensor data, and making sequential decisions. In addition, the mixture of experts (MoE) can enable the distributed and collaborative execution of AI models without performance degradation between connected vehicles. In this survey, we explore the integration of MoE and GAI to enable Artificial General Intelligence in IoV, which can enable the realization of full autonomy for IoV with minimal human supervision and applicability in a wide range of mobility scenarios, including environment monitoring, traffic management, and autonomous driving. In particular, we present the fundamentals of GAI, MoE, and their interplay applications in IoV. Furthermore, we discuss the potential integration of MoE and GAI in IoV, including distributed perception and monitoring, collaborative decision-making and planning, and generative modeling and simulation. Finally, we present several potential research directions for facilitating the integration. © 2024, CC BY.

关键词： Integration

Simulation-Based System Improvement with Work Domain Functional Analysis: A Large-Size Product Manufacturing Case Study

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IFAC-PapersOnLine 2022年第29期55卷 120-125页

作者： Enrique Ruiz Zúñiga Takayuki Hirose Hideki Nomoto Tetsuo Sawaragi Systems Design Laboratory Kyoto University (JSPS Research Fellow) Department of Intelligent Automation University of Skövde. Högskolevägen Box 408 Skövde 541 28 Sweden Japan Manned Space Systems Corporation Otemachi Bldg. 1-6-1 Otemachi Chiyoda-ku Tokyo 100-0004 Japan Department of Mechanical Engineering and Science Kyoto University Kyoto 615-8540 Japan

Manufacturing companies worldwide have recently experienced challenging times due to a lack of staff, materials, and components. This has mainly been caused by abrupted logistics chains and collateral effects of the last pandemic situation. Ideally, resilience engineering systems, systems that have recovery capacity from difficulties, are prepared to overcome changes in demand and disruption in production. However, lack of flexibility, adaptability, and available digital data limit the implementation of resilience systems. To overcome this problem with a high number of interrelations considering human-machine interactions, a methodology including Discrete-Event Simulation, Work Domain Analysis, and Functional Resonance Analysis Method is proposed to design, analyze, and improve complex manufacturing systems. These tools allow deeper analysis of the interrelations of the system at different abstraction levels and both with quantitative and qualitative perspectives. Going through an industrial case study, the aim is to increase the capacity and resilience of a leisure-boat manufacturing company producing highly-customized large-size products, which adds additional constraints to the problem. The objectives are to increase flexibility and productivity at the same time as maintaining high-quality product standards. The results highlight the identification of some constraints of the system such as the main production bottleneck, lack of space, and a limited number of transports, molds, and skilled personnel. The implementation and results of the methodology have proved to serve as a decision-support tool, providing insight about limitations of the system to managers and stakeholders, as well as a guideline for increasing capacity and resilience of the manufacturing process.

关键词： Discrete-Event Simulation Functional Resonance Analysis Method Capacity Analysis Resilience systems Manufacturing

Generative AI for Space-Air-Ground Integrated Networks

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arXiv 2023年

作者： Zhang, Ruichen Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Zhang, Ping Kim, Dong In The College of Computing and Data Science Nanyang Technological University Singapore The Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The University of Sydney SydneyNSW2006 Australia The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Recently, generative AI technologies have emerged as a significant advancement in artificial intelligence field, renowned for their language and image generation capabilities. Meantime, space-air-ground integrated network (SAGIN) is an integral part of future B5G/6G for achieving ubiquitous connectivity. Inspired by this, this article explores an integration of generative AI in SAGIN, focusing on potential applications and case study. We first provide a comprehensive review of SAGIN and generative AI models, highlighting their capabilities and opportunities of their integration. Benefiting from generative AI’s ability to generate useful data and facilitate advanced decision-making processes, it can be applied to various scenarios of SAGIN. Accordingly, we present a brief survey on their integration, including channel modeling and channel state information (CSI) estimation, joint air-space-ground resource allocation, intelligent network deployment, semantic communications, image extraction and processing, security and privacy enhancement. Next, we propose a framework that utilizes a generative diffusion model (GDM) to construct channel information map to enhance quality of service for SAGIN. Simulation results demonstrate the effectiveness of the proposed framework. Finally, we discuss potential research directions for generative AI-enabled SAGIN. © 2023, CC BY.

关键词： Generative adversarial networks

Stability analysis of linear systems with two additive time-varying delays via the extended reciprocally convex matrix inequality

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Stability analysis of linear systems with two additive time-...

第40届中国控制会议

作者： Chen Yan Chuan-Ke Zhang Xi-Zi Zhou School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Art and Design Wuhan University of Technology

This paper investigates the stability of linear systems with two additive time-varying delays. Firstly, an augmented Lyapunov-Krasovskii functional(LKF) is constructed by introducing a novel augmented term which contains the derivative of state vector into integral terms. Then the extended reciprocally convex matrix inequality together with the Wirtinger integral inequality is used to estimate the derivative of the proposed LKF. As a result, a stability criterion with less conservatism is established. Finally, the advantage of the stability criterion is demonstrated through a numerical example and the application of the stability criterion to stability analysis of load frequency control is illustrated.

关键词： linear system additive time-varying delays stability analysis reciprocally convex matrix inequality

An integrated discrete-event simulation with functional resonance analysis and work domain analysis methods for industry 4.0 implementation

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Decision Analytics Journal 2023年 9卷

作者： Ruiz Zúñiga, Enrique Yasue, Naruki Hirose, Takayuki Nomoto, Hideki Sawaragi, Tetsuo JSPS Research Fellow Department of Intelligent Automation University of Skövde Högskolevägen Box 408 Skövde 541 28 Sweden Systems Design Laboratory Department of Mechanical Engineering and Science Kyoto University Kyoto 615-8540 Japan Japan Manned Space Systems Corporation Otemachi Bldg. 1-6-1 Otemachi Chiyoda-ku Tokyo 100-0004 Japan

In the Industry 4.0 era, advanced analytical tools are essential for progressing with digital transformation, especially within complex socio-technical systems. However, the growing complexity of these systems in manufacturing impedes system improvement, and traditional analytical methods focusing solely on the technological aspect often fall short. To overcome this problem, this paper introduces an integrated methodology combining Discrete-Event Simulation, Functional Resonance Analysis Method, and Work Domain Analysis for analysing and enhancing manufacturing systems by considering factors like operator skill levels, demand changes, and production constraints. Implemented in two industrial case studies, this methodology effectively identifies system limitations and aids in structured data analysis, positioning it as a vital decision support system in the digital transformation of Industry 4.0. © 2023 The Authors

关键词： Discrete-event simulation Functional resonance analysis method Industry 4.0 Manufacturing Socio-technical systems Work domain analysis

Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

Hierarchical Optimization of Landing Performance for Lander with Adaptive Landing Gear

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Chinese Journal of Mechanical engineering 2019年第2期32卷 24-35页

作者： Zongmao Ding Hongyu Wu Chunjie Wang Jianzhong Ding School of Mechanical Engineering and Automation Beijing University of Aeronautics and Astronautics Beijing System Design Institute of Electro-Mechanic Engineering China Aerospace Science and Industry Corporation Limited Department of Mechanical Engineering Tsinghua University State Key Laboratory of Virtual Reality and Systems Beijing University of Aeronautics and Astronautics

A parameterized dynamics analysis model of legged lander with adaptive landing gear was established. Based on the analysis model, the landing performances under various landing conditions were analyzed by the optimized Latin hypercube experimental design method. In order to improve the landing performances, a hierarchical optimization method was proposed considering the uncertainty of landing conditions. The optimization problem was divided into a higher level(hereafter the "leader") and several lower levels(hereafter the "follower"). The followers took condition?ing factors as design variables to find out the worst landing conditions, while the leader took bu er parameters as design variables to better the landing performance under worst conditions. First of all, sensitivity analysis of landing conditioning factors was carried out according to the results of experimental design. After the sensitive factors were screened out, the response surface models were established to reflect the complicated relationships between sensi?tive conditioning factors, bu er parameters and landing performance indexes. Finally, the response surface model was used for hierarchical optimization iteration to improve the computational e ciency. After selecting the optimum bu er parameters from the solution set, the dynamic model with the optimum parameters was simulated again under the same landing conditions as the simulation before. After optimization, nozzle performance against damage is improved by 5.24%, the acceleration overload is reduced by 5.74%, and the primary strut improves its performance by 21.10%.

关键词： Landing gear Soft landing Sensitivity analysis Response surfaces Hierarchical optimization