Research on Methods for Controlling a Group of Mobile Robots Under Uncertainty

Moisieiev, M.; Yevsieiv, V.

Публікація:
Research on Methods for Controlling a Group of Mobile Robots Under Uncertainty

dc.contributor.author	Moisieiev, M.
dc.contributor.author	Yevsieiv, V.
dc.date.accessioned	2025-10-04T11:17:32Z
dc.date.available	2025-10-04T11:17:32Z
dc.date.issued	2025
dc.description.abstract	The article considers modern methods for controlling a group of mobile robots in environments with a high level of uncertainty, which is a relevant task in the context of the development of the Industry 5.0 concept. The main attention is paid to the analysis of centralized and decentralized approaches, probabilistic models, fuzzy logic, collective intelligence methods, and reinforcement learning algorithms. The conducted research demonstrates their features, advantages, and limitations when applied in production and service scenarios. It is shown that decentralized and bioinspired methods provide high stability and scalability, while probabilistic models and fuzzy logic allow you to work effectively with incomplete information. Machine learning methods provide the ability to adapt and self-learning, but require significant computing resources. The results obtained indicate the feasibility of integrating different approaches into hybrid systems, which allows you to increase the efficiency and reliability of group control of robots in complex environments.
dc.identifier.citation	Moisieiev M. Research on Methods for Controlling a Group of Mobile Robots Under Uncertainty / M. Moisieiev, V. Yevsieiv // Manufacturing & Mechatronic Systems 2025 : Thesises of Reports of IX-st International Conference, October 25-26, 2025. - Kharkiv, 2025. - P. 26-29.
dc.identifier.uri	https://openarchive.nure.ua/handle/document/32848
dc.language.iso	en_US
dc.subject	group management
dc.subject	POMDP
dc.subject	fuzzy logic
dc.subject	collective intelligence
dc.subject	reinforcement learning.
dc.title	Research on Methods for Controlling a Group of Mobile Robots Under Uncertainty
dc.type	Thesis
dspace.entity.type	Publication