Methods of managing an automated mobile system

Subject matter: Description of existing management methods for an automated mobile system, particularly a swarm of bots. Goal: To conduct a comparative analysis of centralized, decentralized, and distributed management methods to identify their advantages and disadvantages. Tasks: To study various approaches to managing a swarm of bots, analyze their efficiency, reliability, and applicability in different fields. Methods: Theoretical analysis of existing approaches, modeling, and simulation to assess their performance and reliability. The research includes the use of genetic algorithms, swarm algorithms, and artificial potential fields for managing bot trajectories. Results: The conducted analysis shows that centralized methods provide high accuracy and coordination but have limited fault tolerance and scalability. Decentralized methods offer greater flexibility and robustness but may have coordination issues between nodes. Distributed methods provide high autonomy and adaptability but require a reliable communication infrastructure. Genetic algorithms and swarm algorithms are effective for planning bot trajectories but have high computational costs. Conclusions: The use of different management methods depends on the specific requirements and conditions of the task. Centralized methods are suitable for tasks where precision and coordination are important, decentralized methods for tasks requiring flexibility and robustness, and distributed methods for tasks needing autonomy and adaptability. Future research should focus on integrating different approaches to enhance the efficiency and reliability of bot swarm management systems.