Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.
Bhargava et al. (Mon,) studied this question.