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A fundamental successful docking operation requires the autonomous underwater vehicle (AUV) to be able to guide, navigate, and control itself into the docking station in a strategic manner and even possibly execute different maneuvers at different mission phases, depending on docking scenario, requirements, and homing sensor type. A docking station, due to environmental or mission requirements, is possibly oriented at a specific direction instead of allowing omnidirectional homing, and necessitates vehicle docking in only this direction. Depending on the operating environment, either wave or current presence or both can result in a dominating disturbance to the vehicle docking operation. In this work, an inverted ultrashort baseline (USBL) system is used as the main homing sensor to complement the existing navigation suite on the DSO-developed AUV. A docking guidance system was designed and implemented using the Sugeno fuzzy inference system (FIS). A desired heading vector field and the fuzzy rules were developed to perform the fuzzy docking maneuver. An error-state Kalman filter (KF) was designed, formulated, and implemented successfully on the AUV and has proven to perform excellent relative positioning estimation in sea trials. A software architecture was designed for the docking algorithms, and implemented onto a single board computer in the AUV. A sensor fusion approach to the software programming was adopted to ensure that navigation data from all navigation sensors are properly acquired and synchronized. A docking station was designed and eventually deployed at sea for docking trials. Successful AUV docking attempts at sea trials were demonstrated, thus showing the effectiveness of the implemented docking algorithms.

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