Adaptive Interval Line-of-Sight Based Path Following and Field Testing for Autonomous Underwater Vehicles

This study explores an adaptive guidance rate methodology based on interval error, leveraging the V-stability analysis for AUVs. It through the vector field around a predefined path to generate a stable heading, integrating a variable look-ahead distance for enhanced heading adjustment flexibility and precision. An interval error vector is introduced to refine the heading control, improving guidance accuracy. Moreover, a PID control algorithm is employed for the fundamental control of the AUV, boosting system stability and responsiveness. The AILOS algorithm, developed from these concepts, was implemented on the MOOS-IvP platform and subjected to field tests in real aquatic environments. Experimental results validate the performance of both AILOS and ALOS algorithms in path tracking, with AILOS demonstrating superior flexibility in path variation management, affirming its effectiveness and adaptability for maritime operations. This investigation contributes to advancing navigational control strategies for AUVs.