An Improved Fuzzy Logic Controller for Mobile Robots Navigation in Unknown Environments

Abstract

ABSTRACT This paper presents a dual Fuzzy Logic Controller system for robot navigation in dynamic and unknown environments. The proposed system integrates two core Fuzzy Logic Controllers:one for goal‐seeking and another for obstacle avoidance. To ensure smooth transitions between both behaviors, a third fuzzy fusion block is introduced, inferring a continuous weighting parameter K based on real‐time sensor data. Additionally, the obstacle avoidance behavior is reinforced with a dynamic direction prioritization mechanism that selects the safest and most efficient turn based on obstacle layout. To overcome local minima and isolated regions, a subgoal generation module is embedded, enabling the robot to dynamically reposition itself toward free space without predefined waypoints. The overall architecture is presented through a structured introduction comprising background, literature survey, and a clear statement of contributions. Extensive validation is carried out through both simulation in ROS2‐Gazebo and real‐world experiments using a Pioneer 3‐DX platform. Comparative analyses demonstrate that the proposed improvements significantly enhance navigation efficiency, path smoothness, and adaptability in cluttered or evolving environments, while preserving low computational cost and interpretability.