Published November 22, 2024
| Version v1
Journal article
Open
Data Fusion Research for Collaborative Robots-Manipulators within Industry 5.0
Creators
- 1. Department of Computer-Integrated Technologies, Automation and Robotics, Kharkiv National University of Radio Electronics, Ukraine
- 2. Senior Developer Electronic Health Solution, Amman, Jordan
Description
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.
Files
125-137 Vladyslav Yevsieiev.pdf
Files
(639.4 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:49871bf2dbb618517c35f3151ee5e286
|
639.4 kB | Preview Download |
Additional details
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.