INTELLIGENT AI-BASED NAVIGATION SYSTEM FOR MOBILE ROBOTS - U.A. Ziyamuxammedova; Mahmudov L. A; Erkayev S. R (TADQIQOTLAR, 2025)
Abstract:  The  integration  of  artificial  intelligence  (AI)  into  mobile  robotic systems is redefining the landscape of autonomous navigation, decision-making, and task  execution.  Traditional  rule-based  systems,  while  effective  in  structured environments, often fail in dynamic or unknown terrains. This research investigates the application of AI techniques – namely fuzzy logic and reinforcement learning  – in developing  an  intelligent  navigation  system  for  mobile  robots  within  a  virtual simulation  environment.  Using  the  CoppeliaSim  Edu  platform,  a  three-wheeled differential-drive mobile robot was modeled, equipped with various sensors (infrared, ultrasonic, compass), and placed in a randomized maze-like environment. The robot’s goal  was  to  autonomously  navigate  the  environment,  avoid  obstacles,  and  reach predefined targets efficiently. Initially, a fuzzy rule-based controller was implemented to  provide  baseline  navigation  logic.  Subsequently,  a  Q-learning  reinforcement learning  agent  was  introduced  to  optimize  path  planning  through  experience-based learning.  The  combination  of  these  techniques  led  to  a  marked  improvement  in navigation  performance,  as  evidenced  by  reduced  completion  times  and  increased obstacle  avoidance  efficiency.  Graphical  data  and  tabular  analysis  support  the effectiveness of AI-based control versus traditional logic. The research confirms that virtual environments, coupled with intelligent algorithms, can serve as powerful tools for  developing  and  testing  advanced  robotic  control  systems  before  deployment  in physical hardware. This approach is cost-effective, safe, and scalable for educational, research, and industrial applications.