Data Fusion Research for Collaborative Robots-Manipulators within Industry 5.0

Vladyslav Yevsieiev; Svitlana Maksymova; Dmytro Gurin; Ahmad Alkhalaileh

Authors

Vladyslav Yevsieiev Department of Computer-Integrated Technologies, Automation and Robotics, Kharkiv National University of Radio Electronics, Ukraine
Svitlana Maksymova Department of Computer-Integrated Technologies, Automation and Robotics, Kharkiv National University of Radio Electronics, Ukraine
Dmytro Gurin Department of Computer-Integrated Technologies, Automation and Robotics, Kharkiv National University of Radio Electronics, Ukraine
Ahmad Alkhalaileh Senior Developer Electronic Health Solution, Amman, Jordan

Keywords:

Industry 5.0, Collaborative Robot, Work Area, Computer Vision, Sensor, Data, Data Fusion.

Abstract

The article examines methods of data fusion (Data Fusion) for collaborative manipulator robots in the context of Industry 5.0. Three approaches are considered: Kalman filter, Bayesian estimation and Dempster-Shafer theory. The Kalman filter has proven to be effective for linear systems, but requires modification for nonlinear problems. Bayesian estimation provides accuracy for complex systems, although it requires more resources. The Dempster-Shafer theory is effective under data uncertainty, but has a high computational complexity. The conclusions indicate the importance of choosing a data fusion method depending on the requirements for accuracy and adaptability of robots in the production conditions of Industry 5.0.

References

1. Yevsieiev, V., & et al. (2024). Human Operator Identification in a Collaborative Robot Workspace within the Industry 5.0 Concept. Multidisciplinary Journal of Science and Technology, 4(9), 95-105.

2. Abu-Jassar, A., & et al. (2023). Obstacle Avoidance Sensors: A Brief Overview. Multidisciplinary Journal of Science and Technology, 3(5), 4-10.

3. Gurin, D., & et al. (2024). Using the Kalman Filter to Represent Probabilistic Models for Determining the Location of a Person in Collaborative Robot Working Area. Multidisciplinary Journal of Science and Technology, 4(8), 66-75.

4. Basiuk, V., & et al. (2024). Command recognition system research. Technical Science Research in Uzbekistan, 2(5), 50-61.

5. Baker, J. H., Laariedh, F., Ahmad, M. A., Lyashenko, V., Sotnik, S., & Mustafa, S. K. (2021). Some interesting features of semantic model in Robotic Science. SSRG International Journal of Engineering Trends and Technology, 69(7), 38-44.

6. Abu-Jassar, A. T., Al-Sharo, Y. M., Lyashenko, V., & Sotnik, S. (2021). Some Features of Classifiers Implementation for Object Recognition in Specialized Computer systems. TEM Journal: Technology, Education, Management, Informatics, 10(4), 1645-1654.

7. Sotnik, S., Mustafa, S. K., Ahmad, M. A., Lyashenko, V., & Zeleniy, O. (2020). Some features of route planning as the basis in a mobile robot. International Journal of Emerging Trends in Engineering Research, 8(5), 2074-2079.

8. Nevliudov, I., & et al.. (2020). Method of Algorithms for CyberPhysical Production Systems Functioning Synthesis. International Journal of Emerging Trends in Engineering Research, 8(10), 7465-7473.

9. Mustafa, S. K., Yevsieiev, V., Nevliudov, I., & Lyashenko, V. (2022). HMI Development Automation with GUI Elements for Object-Oriented Programming Languages Implementation. SSRG International Journal of Engineering Trends and Technology, 70(1), 139-145.

10. Nevliudov, I., Yevsieiev, V., Lyashenko, V., & Ahmad, M. A. (2021). GUI Elements and Windows Form Formalization Parameters and Events Method to Automate the Process of Additive Cyber-Design CPPS Development. Advances in Dynamical Systems and Applications, 16(2), 441-455.

11. Maksymova, S., Matarneh, R., Lyashenko, V. V., & Belova, N. V. (2017). Voice Control for an Industrial Robot as a Combination of Various Robotic Assembly Process Models. Journal of Computer and Communications, 5, 1-15.

12. Ahmad, M. A., Baker, J. H., Tvoroshenko, I., & Lyashenko, V. (2019). Modeling the structure of intellectual means of decision-making using a system-oriented NFO approach. International Journal of Emerging Trends in Engineering Research, 7(11), 460-465.

13. Lyashenko, V., Abu-Jassar, A. T., Yevsieiev, V., & Maksymova, S. (2023). Automated Monitoring and Visualization System in Production. International Research Journal of Multidisciplinary Technovation, 5(6), 9-18.

14. Abu-Jassar, A. T., Attar, H., Lyashenko, V., Amer, A., Sotnik, S., & Solyman, A. (2023). Access control to robotic systems based on biometric: the generalized model and its practical implementation. International Journal of Intelligent Engineering and Systems, 16(5), 313-328.

15. Al-Sharo, Y. M., Abu-Jassar, A. T., Sotnik, S., & Lyashenko, V. (2023). Generalized Procedure for Determining the Collision-Free Trajectory for a Robotic Arm. Tikrit Journal of Engineering Sciences, 30(2), 142-151.

16. Ahmad, M. A., Sinelnikova, T., Lyashenko, V., & Mustafa, S. K. (2020). Features of the construction and control of the navigation system of a mobile robot. International Journal of Emerging Trends in Engineering Research, 8(4), 1445-1449.

17. Yevsieiev, V., & et al. (2024). Research of Existing Methods of Representing a Collaborative Robot-Manipulator Environment within the Framework of Cyber-Physical Production Systems. Multidisciplinary Journal of Science and Technology, 4(9), 112-120.

18. Vizir, Y., & et al. (2024). Lighting Control Module Software Development. ournal of Universal Science Research, 2(2), 29–42.

19. Gurin, D., & et al. (2024). MobileNetv2 Neural Network Model for Human Recognition and Identification in the Working Area of a Collaborative Robot. Multidisciplinary Journal of Science and Technology, 4(8), 5-12.

20. Samoilenko, H., & et al. (2024). Review for Collective Problem-Solving by a Group of Robots. Journal of Universal Science Research, 2(6), 7-16.

21. Gurin, D., & et al. (2024). CAMShift Algorithm for Human Tracking in the Collaborative Robot Working Area. Journal of Universal Science Research, 2(8), 87–101.

22. Basiuk, V., & et al. (2023). Mobile Robot Position Determining Using Odometry Method. Multidisciplinary Journal of Science and Technology, 3(3), 227-234.

23. Vasiurenko, O., Lyashenko, V., Baranova, V., & Deineko, Z. (2020). Spatial-Temporal Analysis the Dynamics of Changes on the Foreign Exchange Market: an Empirical Estimates from Ukraine. Journal of Asian Multicultural Research for Economy and Management Study, 1(2), 1-6.

24. Mustafa, S. K., Ayaz, A. M., Baranova, V., Deineko, Z., Lyashenko, V., & Oyouni, A. A. A. (2020). Using wavelet analysis to assess the impact of COVID-19 on changes in the price of basic energy resources. International Journal of Emerging Trends in Engineering Research, 8(7), 2907-2912.

25. Lyashenko, V. V., Lyubchenko, V. A., Ahmad, M. A., Khan, A., & Kobylin, O. A. (2016). The Methodology of Image Processing in the Study of the Properties of Fiber as a Reinforcing Agent in Polymer Compositions. International Journal of Advanced Research in Computer Science, 7(1), 15-18.

26. Lyubchenko, V., & et al.. (2016). Digital image processing techniques for detection and diagnosis of fish diseases. International Journal of Advanced Research in Computer Science and Software Engineering, 6(7), 79-83.

27. Lyashenko, V. V., Matarneh, R., Kobylin, O., & Putyatin, Y. P. (2016). Contour Detection and Allocation for Cytological Images Using Wavelet Analysis Methodology. International Journal, 4(1), 85-94.

28. Kuzemin, O., & Lyashenko, V. Microsituation Concept in GMES Decision Support Systems/A. Kuzemin, V. Lуashenko. Intelligent Data Processing in Global Monitoring for Environment and Security (pр. 217–238).–2011.–Р, 217-238.

29. Lyashenko, V., Laariedh, F., Ayaz, A. M., & Sotnik, S. (2021). Recognition of Voice Commands Based on Neural Network. TEM Journal: Technology, Education, Management, Informatics, 10(2), 583-591.

30. Orobinskyi, P., Deineko, Z., & Lyashenko, V. (2020). Comparative Characteristics of Filtration Methods in the Processing of Medical Images. American Journal of Engineering Research, 9(4), 20-25.

31. Sotnik S., & et al.. (2022). Key Directions for Development of Modern Expert Systems. International Journal of Engineering and Information Systems (IJEAIS), 6(5), 4-10.

32. Sotnik S., & Lyashenko V. (2022). Prospects for Introduction of Robotics in Service. International Journal of Academic Engineering Research (IJAER), 6(5), 4-9.

33. Lyashenko, V., & Sotnik, S. (2022). Overview of Innovative Walking Robots. International Journal of Academic Engineering Research (IJAER), 6(4), 3-7.

34. Baranova, V., Orlenko, O., Vitiuk, A., Yakimenko-Tereschenko, N., & Lyashenko, V. (2020). Information system for decision support in the field of tourism based on the use of spatio-temporal data analysis. International Journal of Advanced Trends in Computer Science and Engineering, 9(4), 6356-6361.

35. Sotnik, S., & et al.. (2022). Modern Industrial Robotics Industry. International Journal of Academic Engineering Research, 6(1),. 37-46.

36. Lyashenko, V., Kobylin, O., & Baranchykov, Y. (2018, October). Ideology of Image Processing in Infocommunication Systems. In 2018 International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T) (pp. 47-50). IEEE.

37. Lyashenko, V., & Sotnik, S. (2020). Analysis of Basic Principles for Sensor System Design Process Mobile Robots. Journal La Multiapp, 1(4), 1-6.

38. Lyashenko V, Matarneh R, Sotnik S. Modeling of Machine Design with Numerical Control in UG NX 7.5 System. The International Journal of Engineering and Science (IJES), 2018, 7(7), pp. 28-37.

39. Matarneh, R., Sotnik, S., & Lyashenko, V. (2018). Search of the molding form connector plane on the approximation basis by the many-sided surface with use of the convex sets theory. International Journal of Mechanical and Production Engineering Research and Development, 8(1), 977-988.

40. Babker, A. M., Suliman, R. S., Elshaikh, R. H., Boboyorov, S., & Lyashenko, V. (2024). Sequence of Simple Digital Technologies for Detection of Platelets in Medical Images. Biomedical and Pharmacology Journal, 17(1), 141-152.

41. Nascimento, H., & et al. (2020). Collision avoidance interaction between human and a hidden robot based on kinect and robot data fusion. IEEE Robotics and Automation Letters, 6(1), 88-94.

42. He, B., & et al. (2021). Data fusion-based sustainable digital twin system of intelligent detection robotics. Journal of Cleaner Production, 280, 124181.

43. Gao, Q., & et al. (2021). Hand gesture recognition using multimodal data fusion and multiscale parallel convolutional neural network for human–robot interaction. Expert Systems, 38(5), e12490.

44. Gupta, A., & et al. (2022). Simultaneous localization and mapping (slam) and data fusion in unmanned aerial vehicles: Recent advances and challenges. Drones, 6(4), 85.

45. Alatise, M. B., & et al. (2020). A review on challenges of autonomous mobile robot and sensor fusion methods. IEEE Access, 8, 39830-39846.

46. Kolar, P., & et al. (2020). Survey of datafusion techniques for laser and vision based sensor integration for autonomous navigation. Sensors, 20(8), 2180.

47. Qi, W., & et al. (2021). Multi-sensor guided hand gesture recognition for a teleoperated robot using a recurrent neural network. IEEE Robotics and Automation Letters, 6(3), 6039-6045.

48. Khattak, S., & et al. (2020). Complementary multi–modal sensor fusion for resilient robot pose estimation in subterranean environments. In 2020 International Conference on Unmanned Aircraft Systems (ICUAS), IEEE, 1024-1029.