Localization of a Ship-Hull Cleaning Robot Near the Ship Using a One-Way LBL Positioning System in Port

Abstract

Maritime transportation accounts for the most of global trade, and improving fuel efficiency while reducing carbon dioxide emissions has become a critical research topic. One of the key technical challenges in enhancing fuel efficiency is the prevention and removal of marine biofouling on ship hulls. In addition, the International Maritime Organization (IMO) has raised concerns regarding the transfer of invasive aquatic species through biofouling on ship hulls and has proposed guidelines to mitigate this risk. Effective and environmentally responsible hull cleaning technologies are increasingly required not only for improving fuel efficiency but also for preventing ecological impacts. Conventionally, hull cleaning is performed either during periodic inspections in dry dock or by divers in harbor environments. While frequent cleaning is desirable to maintain optimal fuel efficiency, dry-dock inspections are typically conducted only once per year, and diver-based cleaning is both costly and associated with significant safety risks. To address these challenges, underwater robotic systems have been proposed as a promising solution for automated hull cleaning. Our research group has been actively developing ship hull cleaning robots and has demonstrated the feasibility and potential of robotic cleaning through a series of studies and experimental validations. However, a major challenge in achieving fully autonomous operation is accurate localization in underwater environments, where GPS signals are unavailable and sensing conditions are severely degraded. In particular, reliable position estimation near large ship hulls is difficult due to multipath effects, limited visibility, and the lack of distinctive features. In this paper, we focus on this challenge and present a localization method for a ship-hull cleaning robot using a oneway Long Baseline (LBL) positioning system. The effectiveness of the proposed approach is demonstrated through experimental validation in realistic environments.