INTELLIGENT AI-BASED NAVIGATION SYSTEM FOR MOBILE ROBOTS

Аннотация

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.