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.
Benaicha et al. (Wed,) studied this question.