Autonomous mobile robots (AMRs) operating in dense and dynamically changing environments require robust navigation and reliable obstacle-avoidance capabilities. This study aims to propose an intelligent navigation framework to enable AMRs to navigate efficiently in cluttered environments.
The Cascade Neuro-Fuzzy (CN-Fuzzy) framework uses vision, LiDAR and ultrasonic sensors to detect environmental obstacles. A cascade neural network analyzes distance measurements from the sensors to calculate an optimal turning angle for the AMR, enabling it to follow a target path. Subsequently, a fuzzy logic controller generates velocity commands to ensure smooth and adaptive robot motion. The proposed system is evaluated through MATLAB simulations and real-time experimental validation.
The CN-Fuzzy design navigated successfully in cluttered environments. The path length error was 2.85% in unknown environments, 2.98% in indoor environments and 3.37% in complex situations. The motion time error decreased to 1.61% in unknown situations, 2.66% in indoor environments and 3.22% in complex scenarios. The proposed system achieves an average path error of 3.07% and an average motion time error of 2.50%, with corresponding root mean square error values of 2.39 cm for path length and 0.22 s for navigation time and mean square error values of 5.71 cm² and 0.047 s² for path length and motion time, respectively. These findings demonstrate the system’s real-time path tracking and obstacle avoidance capabilities. Its lower error rates, enhanced robustness and smoother linear and angular velocity variations in both experiment Scenario I and Scenario II make it well-suited for precision and time-sensitive AMR navigation tasks.
This research introduces a CN-Fuzzy control framework fusing neural learning and fuzzy control for the adaptive navigation of AMRs. This hybrid approach, unlike conventional control approaches, improves situational awareness and decision-making, thus enhancing operational capabilities in densely cluttered static and dynamic environments by allowing AMRs to better interpret sensor data and respond to unexpected obstacles in real time.
