Modeling and optimization of mobile robot behavior considering uncertainty factors

This study addresses the control strategy for a group of mobile robots using a modified ant colony algorithm, taking into account uncertainty factors. An analysis of existing approaches is conducted, and a mathematical model of the optimization problem is proposed. Additionally, an adaptation algorithm for dynamic environmental changes based on the modified ant colony algorithm is developed. The primary focus is on the algorithm’s adaptation to dynamic environmental conditions, including the emergence of new tasks, changes in route availability, and the failure of individual robots. To evaluate the effectiveness of the algorithm, computational experiments were conducted with varying numbers of tasks and mobile robots in the group. The results demonstrate that the proposed approach ensures the identification of optimal group control strategies while maintaining robustness against environmental changes. The algorithm proved effective in task allocation and route planning under conditions of limited computational resources and dynamic changes in the operational field. Prospects for further research are discussed, including the integration of neural network and genetic algorithms.