Advanced Control Algorithms for Dynamic Environment Navigation and Obstacle Avoidance

This study proposes an autonomous navigation approach for the Pioneer P3-DX Autonomous Wheeled Robot (AWR) in environments containing both static and dynamic obstacles. The robot utilizes the Artificial Potential Field (APF) algorithm for path calculation, while a neural network aids in zone classification. Three ultrasonic sensors provide distance measurements for hazard assessment. These measurements, along with relative velocity and angle data, aid in identifying regions of elevated risk (Zone 1) and those of lesser risk (Zone 2). Upon hazard detection, fuzzy logic facilitates effective collision avoidance by adjusting wheel velocities. Simulation results conducted in MATLAB and V-REP demonstrate the approach’s efficacy in navigating diverse obstacles, showcasing its adaptability and resilience compared to alternative algorithms. This research introduces an innovative methodology for autonomous mobile robot navigation, emphasizing its reliability and efficiency in traversing intricate environments with varying risk levels.