Analysis of Bat Echolocation‐Inspired Algorithm for SUV Path Planning and Control

ABSTRACT Smart underwater vehicles (SUVs) consistently encounter challenges navigating cluttered underwater environments, evading irregular obstacles and varying currents while establishing a practically obstacle‐avoiding path. Although other state‐of‐the‐art algorithms may help an SUV reach its goals, they frequently fall short of preventing collisions with complex obstacles. A bat echolocation‐inspired algorithm (BEIA) combined with a proportional‐integral‐derivative (PID) controller is suggested as an approach to the problem. The BEIA technique makes this research's obstacle‐avoiding path planning and navigation easier. The PID controller helps the SUV adjust its trajectory precisely and prevents it from overshooting from its intended course. While comparing simulated and experimental findings, the suggested algorithm performs in this challenge, achieving an average navigational time deviation of 4.85% and a 6.15% deviation in path accuracy. With an improvement of over 3.88%, the technique demonstrated improved performance and successfully addressed the difficulties presented by irregular obstacles and varying currents to provide efficient and seamless navigation. The BEIA‐PID optimizes the SUV's route following, obstacle avoidance, and trajectory planning.