The proposed system for autonomous mobile robot navigation involves a multi-step approach. Initially, the environment is initialized, and the path planning is performed using the Artificial Potential Field (APF) algorithm,Dynamic obstacles are continuously monitored, and a Bayesian Regressing Neural Network (BRNN) classifies them into safe or dangerous zones based on speed and distance information. If an obstacle is classified as dangerous, the fuzzy logic is employed to adjust the robot's speed, preventing collisions. After obstacle avoidance, the robot returns to the initial path determined by the APF algorithm to continue navigation towards the target.

Алсайед Н. (науч. рук. Краснов А.Ю.) Advanced Control Algorithms for Dynamic Environment Navigation and Obstacle Avoidance // Сборник тезисов докладов конгресса молодых ученых. Электронное издание. – СПб: Университет ИТМО, [2024]. URL: https://kmu.itmo.ru/digests/article/12455