Applying the self-tuning fuzzy control with the image detection technique on the obstacle-avoidance for autonomous underwater vehicles

This study suggests a simple searching technique for obstacle-avoidance of autonomous underwater vehicles (AUVs) in varying ocean environments by using the self-tuning fuzzy controller. The corresponding hydrodynamic coefficients for the AUV are obtained by the test of Planar Motion Mechanism (PMM), which serves as the important data inputs for the control system. Subsequently, the self-tuning fuzzy controller would be adopted to command the propulsion of AUVs. The function of obstacle-avoidance is based on the underwater image detection method with the BK triangle sub-product of fuzzy relations which can evaluate the safety and remoteness of the candidate routes and the successive optimal heading of strategic routing can then be selected. In the present simulations, four types of motion control factors are selected as the platform to investigate the maneuvering performance of obstacle-avoidance, i.e. self-tuning control, visibility, safety and current effect. Eventually, the present study indicates that the self-tuning fuzzy controller, combined with the image detection technique based on BK triangle sub-product of fuzzy relations, is verified to be a useful searching technique for obstacle-avoidance of AUVs.