Quantum software industrial engineering and intelligent cognitive robotics in industry 4.0 as control objects - prototypes of industry 5.0 / 6.0 models: introduction
Автор: Tyatyushkina Olga Yu., Ulyanov Sergey V.
Журнал: Сетевое научное издание «Системный анализ в науке и образовании» @journal-sanse
Статья в выпуске: 1, 2023 года.
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International project Industry 4.0 (based on intelligent cognitive robotics and Internet of Things (IoT) as forth industrial revolution) together with Quantum Software Engineering and Quantum intelligent control implementations (as the third quantum revolution) open new possibilities for the development “wise Industry 5.0” with practically unbounded information resources (based on a new end-to-end information technology of quantum soft computing and small quantum computers for Quantum Internet of Things). A exible manufacturing system (FMS) involving several robots with different capabilities in a shop oor layout context [1-3]. This use case is composed of an automatic production system and a set of manual workstations where the operator can be assisted by cobotic arms for assembly tasks. In this article we consider autonomous and smarm robots with different intelligent and cognitive levels for Industry 4.0 with the application of embedded quantum intelligent controllers as model background of Industry 5.0.
Cobotic arms, quantum intelligent controllers, sociotechnical cyber-physical robotic systems, industry 5.0
Короткий адрес: https://sciup.org/14128092
IDR: 14128092
Список литературы Quantum software industrial engineering and intelligent cognitive robotics in industry 4.0 as control objects - prototypes of industry 5.0 / 6.0 models: introduction
- Chen B. et al. Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges // IEEE Access. – 2018. – No 6. – Pp. 6505 – 6519.
- Havard V. et al. Digital twin and virtual reality: a co-simulation environment for design and assessment of industrial workstations // Production & Manufacturing Research. – 2019. –Vol. 7. –No 1. –Pp. 472-489. –DOI: 10.1080/21693277.2019.1660283.
- Huang Z. AI-Driven Digital Twins // Sensors. – 2021. – No 21. – Pp. 6340. – DOI: 10.3390/s21196340.
- Tyatyushkina O., Ulyanov S. Intelligent cognitive control of robotic sociotechnical systems. Pt.1: Ro-botic systems and «Human being - robot» interactive models in project «Industry 4.0» // System Analy-sis in Science and Education. – 2021. – No 3. – Pp. 44–101. – 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. – 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: 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.
- Bhirangi R. et al. All the Feels: A dexterous hand with large area sensing // arXiv:2210.15658v1 [cs.RO] 27 Oct 2022.
- Berscheid L. et al. Robot Learning of 6DoF Grasping using Model-based Adaptive Primitives // arXiv:2103.12810v1 [cs.RO] 23 Mar 2021.
- Liu J. et al. Robot Cooking with Stir-fry: Bimanual Non-prehensile Manipulation of Semi-fluid Objects // arXiv:2205.05960v1 [cs.RO] 12 May 2022.
- Lv J. et al. SAM-RL: Sensing-Aware Model-Based Reinforcement Learning via Differentiable Physics-Based Simulation and Rendering // arXiv:2210.15185v1 [cs.RO] 27 Oct 2022.
- Marinho M.M. et al. Design and Validation of a Multi-Arm Robot Platform for Scientific Exploration // arXiv:2210.11877v1 [cs.RO] 21 Oct 2022.
- Shi B. et al. Robust Control of a New Asymmetric Teleoperation Robot Based on a State Observer // Sensors. – 2021. – Vol. 21. – Pp. 6197. – DOI: 10.3390/s21186197.
- Cui Z. et al. Caveats on the first-generation da Vinci Research Kit: latent technical constraints and essential calibrations // arXiv:2210.13598v1 [cs.RO] 24 Oct 2022.
- Giberti H. et al. A Methodology for Flexible Implementation of Collaborative Robots in Smart Manu-facturing Systems // Robotics. – 2022. – Vol. 11. – No 9. – DOI: 10.3390/ robotics11010009.
- Kasaei H., Kasaei M. Throwing Objects into A Moving Basket While Avoiding Obstacles // arXiv:2210.00609v1 [cs.RO] 2 Oct 2022.
- Ulloa C. et al. A Mixed-Reality Tele-Operation Method for High-Level Control of a Legged-Manipulator Robot // Sensors. – 2022. – Vol. 22. – Pp. 8146. – DOI: 10.3390/s22218146.
- Xia K. et al. Towards Semantic Integration of Machine Vision Systems to Aid Manufacturing Event Understanding // Sensors. – 2021. – Vol. 21. – Pp. 4276. – DOI: 10.3390/s21134276.
- 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 science and educa-tion. – 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 : 10.1142/S2301385020500089.
- Ollero A. et al. Past, Present and Future of Aerial Robotic Manipulators // IEEE TRANSACTIONS ON ROBOTICS. – 2022. – Vol. 38. – No. 1. – Pp. 626-645.
- 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.
- Fu J. et al. Myoelectric Control Systems for Upper Limb Wearable Robotic Exoskeletons and Exosuits - A Systematic Review // Sensors. - 2022. - Vol. 22. - Pp 8134. – DOI : 10.3390/ s22218134.
- Gull M.A. et al. A Review on Design of Upper Limb Exoskeletons // Robotics. – 2020. – Vol. 9. – No 16. – DOI : 10.3390/robotics9010016.
- Shi, Y. et al. Soft Wearable Robots: Development Status and Technical Challenges // Sensors. – 2022. – Vol. 22. – Pp. 7584. – DOI : 10.3390/s22197584.
- Gunasekara J.M. et al. Control Methodologies for Upper Limb Exoskeleton Robots // IEEE/SICE Inter-national Symposium on System Integration (SII) Kyushu University, Fukuoka, Japan December 16-18, 2012. – 2012. – Pp. 19-24. – DOI : 10.1109/SII.2012.6427387.
- Villegas I. Recognition and Characteristics EEG Signals for Flight Control of a Drone // IFAC Paper-sOnLine. – 2021. – Vol. 54 – No 4. – Pp. 50–55.
- Jeong J-H. et al. Towards Brain-Computer Interfaces for Drone Swarm Control // arXiv:2002.00519v1 [cs.NE] 3 Feb 2020.
- Tyatyushkina O. Yu., Reshetnikov A.G., Ulyanov S. V. Intelligent cognitive robotics. Vol. 1: Soft computational intelligence and information-thermodynamic law of intelligent cognitive control. – M.: Kurs, 2022.
- Кореньков В.В., Ульянов С.В. Интеллектуальная когнитивная робототехника. Ч. 1: Технологии квантовых когнитивных вычислений. – М.: Курс, 2022.
- Ulyanov S.V. Intelligent cognitive robotics. Vol. 2. – M.: Kurs, 2022.
- Ivancova O.V., Korenkov V.V., Ulyanov S.V., Zrelov P.V. Quantum soft engineering toolkit. Pt I. – M.: Kurs, 2022.
- Perez-Salinas A. et al. Data re-uploading for a universal quantum classifier // Quantum. – 2020. – Vol. 4. - Pp. 226.
- Park C. et al. Visual Simulation Software Demonstration for Quantum Multi-Drone Reinforcement Learning // arXiv:2211.15375v1 [quant-ph] 24 Nov 2022.