Moisieiev, M.Yevsieiv, V.2025-10-042025-10-042025Moisieiev 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.https://openarchive.nure.ua/handle/document/32848The 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.en-USgroup managementPOMDPfuzzy logiccollective intelligencereinforcement learning.Thesis