Tianjin Port's autonomous tugboat completes live berthing test

As China Daily reporter Thomas Jay Hopkins watched from the cabin, an autonomous tugboat at Tianjin Port completed a return and berthing maneuver without human input, offering a close-up look at how intelligent navigation systems are moving into live port operations.

The vessel, Jin Gang Lun 36, is part of Tianjin Port's broader push to apply intelligent technology to some of the most demanding tasks in maritime transport, particularly the precise maneuvering required when large cargo ships enter or leave port.

Unlike oceangoing vessels, which are designed for long-distance navigation, ships measuring 200 to more than 300 meters long often lack the agility needed to operate safely in confined harbor waters, making tugboat assistance essential.

According to Li Qiunan, IT director of Tianjin Port Tugboat Lighter Co, Jin Gang Lun 36 is designed to perform those tasks using an intelligent navigation system that supports autonomous escorting, obstacle avoidance, and precision berthing.

The tugboat measures 34.6 meters long and 11.2 meters wide, with a design draft of 3.92 meters and an economic speed of up to 12.5 knots. Equipped with an intelligent navigation system and a voice assistant named 36, it can independently locate, lock onto, approach, and berth target vessels.

The system integrates marine radar, LiDAR, and fog-navigation night vision equipment. Intelligent algorithms use the data to analyze meteorological, hydrological, and obstacle information, allowing the vessel to adjust its route and movements as conditions change.

The autonomous system is intended to enable drivers to focus on monitoring external environmental changes. In emergencies, crew members retain immediate override authority so that human control remains available at all times.

Since trial operations began in Tianjin waters in 2024, Jin Gang Lun 36 has undergone multiple rounds of testing to address technical challenges associated with intelligent navigation in busy port environments.

Li Ziyuan, Wang Nanyi, and Yang Haolin contributed to this story.