Fouling organisms reduce a ship’s fuel efficiency and disturb the ecosystem. Therefore, the International Maritime Organization (IMO) and many nations have enacted laws that mandate periodic hull cleaning for removing fouling organisms. However, cleaning niche areas of the ship hull is extremely difficult. Due to their complex shape, applying antifouling paint and cleaning with hull cleaning robots is difficult, but about 80% of fouling organisms are concentrated in the niche areas. To resolve this issue, this research proposes the use of an autonomous cleaning robot with a hydraulic polyarticular robot arm to clean niche areas of the ship hull. This robot can approach niche areas of the ship hull with complex shapes using its polyarticular arm. It was designed to be able to scan the cleaning area, establish a cleaning plan, and clean accordingly. This robot autonomously cleaned a propeller blade, which is a typical niche area of the ship hull, to verify the applicability of this system. The experiment results show that approximately 80% of the biofouling was removed from the hull crevices and 81% of the cleaned biofouling was recovered.

Monitoring offshore infrastructure is a challenging task owing to the harsh ocean environment. To reduce human involvement in this task, this study proposes an autonomous surface vehicle (ASV)-based structural monitoring system for inspecting power cable lines under the ocean surface. The proposed ASV was equipped with multimodal sonar sensors, including a multibeam echosounder (MBES) and side-scan sonar (SSS) for mapping the seafloor, combined with a precisely estimated vehicle pose from navigation sensors. In particular, a globally consistent map was developed using the orthometric height as a vertical datum estimated based on the geoid height received from the GPS. Accordingly, the MBES and SSS generate a map of the target objects in the form of point clouds and sonar images, respectively. Dedicated outlier removal methods for MBES sensing were proposed to preserve the sparse inlier point cloud, and we applied the projection of the SSS image pixels to reflect the geometry of the seafloor. A field test was conducted in an ocean environment using real offshore cable lines to verify the efficiency of the proposed monitoring system.

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