Квантовая программная инженерия и индустрия 4.0 как платформа для интеллектуального управления роботизированными социотехническими системами в Индустрии 5.0 / 6.0
Боковая панель статьи
Основное содержимое статьи
Аннотация
Рассматриваются основы построения и развития пятой и шестой промышленных революций (I5.0 / I6.0) как развитие результатов проекта Индустрия 4.0 (I4.0) с применением моделей интеллектуальной когнитивной робототехники, квантовой программной инженерии, квантового интеллектуального управления и дружественных интерфейсов типа «мозг - компьютер», «человек - робот». Обсуждаются вопросы построения физических законов интеллектуального управления роботизированными социотехническими системами на основе законов информационно-термодинамического распределения критериев устойчивости, управляемости и робастности. Извлеченная квантовая информация позволяет сформировать дополнительную «социальную» термодинамическую силу управления, скрытую в информационном обмене между агентами многокомпонентной социотехнической системе.
Скачивания
Информация о статье
Это произведение доступно по лицензии Creative Commons «Attribution» («Атрибуция») 4.0 Всемирная.
Библиографические ссылки
Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges / B. Chen [et al.] // IEEE Access. – 2018. – No 6. – Pp. 6505 – 6519. – DOI: 10.1109/ACCESS.2017.2783682.
IEEE Intellectual Property Rights // IEEE - The world's largest technical professional organization. –IEEE, 2024. – URL: http://www.ieee.org/publications_standards/publications/rights/index.html.
Digital twin and virtual reality: a co-simulation environment for design and assessment of indus-trial workstations/ V. Havard, B. Jeanne, M. Lacomblez, D. Baudry // Production & Manufactur-ing Research – 2019. – Vol. 7. – No 1. – Pp. 472–489. – DOI: 10.1080/21693277.2019.1660283
A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robot-ics / Z. Huang [et al.] // Sensors. – 2021. – Vol. 21. – No 19. – Article 6340. – DOI: https://doi.org/10.3390/s21196340.
Tyatyushkina O., Ulyanov S. Intelligent cognitive control of robotic sociotechnical systems. Pt.1: Robotic systems and «Human being - robot» interactive models in project «Industry 4.0» // Sys-tem Analysis in Science and Education. – 2021. – No 3. – Pp. 44–101(In Russ). – URL: http://sanse.ru/download/447.
Ulyanov S. V. Intelligent cognitive control of sociotechnical robotic systems. Pt. 2: Nonlinear model generation of intelligent cognitive robotics for project “Industry 4.0” // System Analysis in Science and Education. – 2021. – No 3. – Pp. 1–43(In Russ). – URL: http://sanse.ru/download/449.
Alcácer V., Cruz-Machado V. Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems // Intern. J. Eng. Sci. and Technol. – 2019. – Vol. 22. – Pp.899 – 919. – DOI: https://doi.org/10.1016/j.jestch.2019.01.006.
Pegman G. Leading European Robotics: Robotic Visions to 2020 and beyond – The Strategic Research Agenda for robotics in Europe. Industrial Technologies Conference Brussels, Belgium, 2020.
Intelligent Cognitive Robotics / A. G. Reshetnikov, S. V. Ulyanov, D. P. Zrelova, P. V. Zrelov. – M.: Kurs, 2023. – 464 p.
A Methodology for Flexible Implementation of Collaborative Robots in Smart Manufacturing Systems / H. Giberti [et al.] // Robotics. – 2022. –Vol. 11. – No 9. – DOI: https://doi.org/10.3390/ robotics11010009.
Intelligent cognitive robotics. Vol. 1: Soft computational intelligence and information-thermodynamic law of intelligent cognitive control / O. Tyatyushkina, A. Reshetnikov, V. Ulya-nov [et al.]. – M.: Kurs, 2022. – 528 p.
Marinho M. M., Quiroz-Omaña J. J., Harada K. Design and Validation of a Multi-Arm Robot Platform for Scientific Exploration // arXive.org e-Print archive. – arXiv:2210.11877v1 [cs.RO] 21 Oct 2022.
Kaigom E.G. Potentials of the Metaverse for Robotized Applications in Industry 4.0 and Industry 5.0 // arXive.org e-Print archive. – arXiv:2404.00783v1 [cs.RO] 31 Mar 2024.
Интеллектуальная когнитивная робототехника. Ч. 1: Технологии квантовых когнитивных вычислений / В. В. Кореньков, С. В. Ульянов, А. А. Шевченко, А. В. Шевченко. – Москва : КУРС, 2022. – 557 с.
Ulyanov S.V. Intelligent cognitive robotics. Vol. 2. – M.: Kurs, 2022.
Quantum soft engineering toolkit. Pt I. / Ivancova O.V., Korenkov V.V., Ulyanov S.V., Zrelov P.V. – M.: Kurs, 2022.
Tyatyushkina O.Yu., Ulyanov S.V. Unmanned Aerial Robotic Vehicles. Pt. 2: Unconventional models of unmanned aerial systems and aerial embedding manipulators // System analysis in sci-ence and education. – 2022. – No 3. – Pp. 53-109/ – URL: http: //sanse.ru/download/474.
All the Feels: A dexterous hand with large area sensing / R. Bhirangi [et al.] // arXive.org e-Print archive. – arXiv:2210.15658v1 [cs.RO] 27 Oct 2022.
Berscheid L., Friedrich C., Kröger T. Robot Learning of 6DoF Grasping using Model-based Adap-tive Primitives // arXive.org e-Print archive. – arXiv:2103.12810v1 [cs.RO] 23 Mar 2021.
Robot Cooking with Stir-fry: Bimanual Non-prehensile Manipulation of Semi-fluid Objects / J. Liu [et al.] // arXive.org e-Print archive. – arXiv:2205.05960v1 [cs.RO] 12 May 2022.
SAM-RL: Sensing-Aware Model-Based Reinforcement Learning via Differentiable Physics-Based Simulation and Rendering / Jun Lv [et al.] // arXive.org e-Print archive. – arXiv:2210.15185v1 [cs.RO] 27 Oct 2022.
Robust Control of a New Asymmetric Teleoperation Robot Based on a State Observer / B. Shi, H. Wu, Y. Zhu, M. Shang, // Sensors. – 2021. – Vol. 21. – Pp. 6197. –DOI: https://doi.org/10.3390/s21186197.
Caveats on the first-generation da Vinci Research Kit: latent technical constraints and essential calibrations / Z. Cui, J. Cartucho, S. Giannarou, F. R. y Baena // arXive.org e-Print archive. – arXiv:2210.13598v1 [cs.RO] 24 Oct 2022.
Kasaei H., Kasaei M. Throwing Objects into A Moving Basket While Avoiding Obstacles // arX-ive.org e-Print archive. – arXiv:2210.00609v1 [cs.RO] 2 Oct 2022.
A Mixed-Reality Tele-Operation Method for High-Level Control of a Legged-Manipulator Robot / C. Cruz Ulloa, D. Domínguez, J. Del Cerro, A. Barrientos // Sensors. – 2022. – Vol. 22. – No 21. – Pp. 8146. – DOI: https://doi.org/10.3390/s22218146.
Towards Semantic Integration of Machine Vision Systems to Aid Manufacturing Event Under-standing / K. Xia [et al.] // Sensors. – 2021. – Vol. 21. – No 13. – Pp. 4276. – DOI: https://doi.org/10.3390/s21134276.
Multimodal Human-Robot Interface for Accessible Remote Robotic Interventions in Hazardous Environments / G. Lunghi [et al.] // IEEE Access. – 2019. – Vol. 7. – Pp. 127290 – 127319. – DOI: https://doi.org/10.1109/ACCESS.2019.293949.
Sheridan T. B. Human–Robot Interaction: Status and Challenges // Human factors. – 2016. – Vol. 58. – No. 4. – Pp. 525 –532. – DOI: 10.1177/0018720816644364
MiniCERNBot Educational Platform: Antimatter Factory Mock-up Missions for Problem-Solving STEM Learning / M. Garcés [et al.] // Sensors. – 2021. – Vol. 21. – Pp. 1398. – DOI: https://doi.org/10.3390/s21041398
Tyatyushkina O.Yu., Ulyanov S.V. Unmanned Aerial Vehicles. Pt. 1: Bio-inspired and aerial–aquatic locomotion // System analysis in science and education. – 2022. – No 3. – Pp. 8-52. – URL: http://sanse.ru/download/473.
Tyatyushkina O.Yu., Ulyanov S.V. Unmanned Aerial Robotic Vehicles. Pt. 2: Unconventional models of unmanned aerial systems and aerial embedding manipulators // System analysis in sci-ence and education. – 2022. – No 3. – Pp. 53-109. – URL: http: //sanse.ru/download/474.
Mohiuddin A. et al. A survey of single and multi-UAV aerial manipulation // Unmanned Systems. – 2020. – Vol. 8. – No 2. – Pp. 119-147. – DOI: https://doi.org/10.1142/S2301385020500089.
Past, Present and Future of Aerial Robotic Manipulators / A. Ollero [et al.] // IEEE TRANSAC-TIONS ON ROBOTICS. – 2022. – Vol. 38. – No. 1. – Pp. 626-645. – [Preprint version final ver-sion at http://ieeexplore.ieee.org/, 2021.]
Sanalitro D. Aerial Cooperative Manipulation: full pose manipulation in air and in interaction with the environment. – DOCTORAT DE L’UNIVERSITÉ FÉDÉRALE TOULOUSE MIDI-PYRÉNÉES. l’Institut National des Sciences Appliquées de Toulouse (INSA de Toulouse), 2022.
Huan N., Kostas A. Forceful Aerial Manipulation based on an Aerial Robotic Chain: Hybrid Modeling and Control // IEEE Robotics and Automation Letters. – 2021. – Vol. 6. – No 2. – Pp. 3711-3719. – DOI: 10.1109/LRA.2021.3064254.
Zhao M., Anzai T., Nishio T. Design, Modeling and Control of a Quadruped Robot SPIDAR: Spherically Vectorable and Distributed Rotors Assisted Air-Ground Amphibious Quadruped Ro-bot // arXive.org e-Print archive. – arXiv:2301.04050v1 [cs.RO] 10 Jan 2023
The current state and future outlook of rescue robotics / Delmerico J. et al. // Journal of Field Robotics. – 2019. –Pp. 1–21. – DOI: 10.1002/rob.21887.
A survey of quantum computing hybrid applications with brain-computer interface / D. Huang, M. Wang, J. Wang, J. Yan // Cognitive Robotics. – 2022. – Vol. 2. – Pp. 164–176. – DOI: https://doi.org/10.1016/j.cogr.2022.07.002.
Hybrid EEG-EMG based brain computer interface (BCI) system for real-time robotic arm control / S. Abdullah, M. A. Khan, M. Serpelloni, E. Sardini // Advanced Materials Letters. – 2019. – Vol. 10. – No 1. – Pp. 35-40. – DOI: 10.5185/amlett.2019.2171.
Duarte R. M. Low cost Brain Computer Interface system for AR Drone Control. – PhD Thesis. UNIVERSIDADE FEDERAL DE SANTA CATARINA CENTRO TECNOLÓGICO PROGRAMA DE PÓS GRADUAÇÃO EM ENGENHARIA DE AUTOMAÇÃO E SISTEMAS, 2017.
van Erp T., Gladysz B. Quantum Technologies in Manufacturing Systems: Perspectives for Applica-tion and Sustainable Development // Procedia CIRP. – 2022. – Vol. 107. – Pp.1120–1125. – DOI: https://doi.org/10.1016/j.procir.2022.05.118.
Artificial Intelligence and Machine Learning for Quantum Technologies / M. Krenn, J. Landgraf, T. Foesel, F. Marquardt // arXive.org e-Print archive. – arXiv:2208.03836v1 [quant-ph] 7 Aug 2022.
Industry quantum computing applications / A. Bayerstadler [et al.] // EPJ Quantum Technology. – 2021. – Vol. 8. – Article number: 25 (2021). – DOI: https://doi.org/10.1140/epjqt/s40507-021-00114-x.
Quantum Communication Systems: Vision, Protocols, Applications, and Challenges /Syed Rakib Hasan, Mostafa Zaman Chowdhury, Md. Saiam, Yeong Min Jang // arXive.org e-Print archive. – arXiv: 2212.13333v1 [quant-ph] 29 Dec 2022.
Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future / S. J. Nawaz [et al.] // IEEE Access. – Vol.7. – Pp. 46317-46350. – DOI: 10.1109/ACCESS.2019.2909490.