American and Japanese researchers will develop robots and AI to help shipbuilders pivot when the built ship deviates from the planned design
Key takeaways
- As ships are built, internal parts-pipes, cables and equipment-can arrive out of order, and scheduling pressure can cause parts to be installed such that the remaining parts no longer fit as expected.
- Re-installing parts could delay construction, but robotic and AI assistants can help shipbuilders catch problems early and predict issues ahead of time, as well as suggest solutions.
- University of Michigan Engineering leads a team of American researchers developing the technology, with a $6.2M grant from the Japanese Ministry of Land, Infrastructure, Transport and Tourism.
Autonomous robots and AI models could help shipyard workers catch when a ship's built structure differs from design drawings, allowing workers to fix problems or adapt sooner. University of Michigan Engineering is leading the American arm of an international project to develop such a system.
Funded with a $6.2 million grant from the Japanese Ministry of Land, Infrastructure, Transport and Tourism, the collaboration will design and prototype AI and robot teammates to track what was actually built inside the growing ship and compare it to a digital twin of the intended structure. The system will then create reports of mismatches that workers can use to make adjustments.
"We want to build a co-pilot system that uses AI and robotics to take some of the detective work off workers' shoulders," said Alan Papalia, U-M assistant professor of naval architecture and marine engineering and the principal investigator of the American research team. "The system should automatically map what's installed, identify where reality is drifting from the design, and suggest workable alternatives when something needs to change."
Papalia's team includes researchers from U-M and the Massachusetts Institute of Technology. The project is funded through the first quarter 2027 and overseen by the Monohakobi Technology Institute, an R&D Center within NYK Line, a global shipping and logistics company based in Japan.
"It's very complementary to our other research projects led by Japanese universities, in which the main focus is robots for automation of hull construction and steel welding," said Hideyuki Ando, managing director of the Monohakobi Technology Institute.
"We wanted to partner with the University of Michigan because of their unique status as a high-output research university with a dedicated department for naval architecture and marine engineering."
Helping construction stay on track
The American team is developing technology to help shipyard workers with outfitting-the installation of pipes, cables, electrical systems and other equipment inside the ship. Hundreds of thousands of individual components have to be placed inside confined, changing spaces, and scheduling pressure often causes the outfitting schedule to be dictated by crew and part availability rather than an ideal build sequence.
In the shifting build schedule, workers can find that parts don't fit as they expected and the original drawings sometimes prove impractical as outfitting progresses. Compartments may have closed earlier than expected, and the shortest route to an electrical box or pipe may be blocked. If issues aren't caught early, some installations may need to be reworked, which could delay delivery of the ship.
To help workers pivot, the robots will be designed to roam the growing ship structure and collect LiDAR and camera data that will be fed to an AI model along with other human-made measurements. The AI model will then construct a digital model of the built structure to be compared with the intended design. With the digital model, the AI will look for deviations from the plan and predict problems that may arise based on how equipment has been installed.
When the model finds a problem-such as a pipe that no longer fits as expected or a build sequence that will likely be disrupted-the system will generate a list of potential solutions and the tradeoffs between them. With that information, workers can verify problems and decide how to resolve them. The entire robotic system will be automated to help alleviate some of the burden of verifying that construction is on track, but the AI model will also flag when and where it has insufficient sensor data, so that people can help fill in gaps as needed.
Training shipbuilding helpers
To train the AI to understand the robot's images of the ship and identify problems, the researchers will create a synthetic dataset by simulating the shipbuilding process many times. The researchers will also interview tradespeople at shipyards in the U.S. and Japan to ensure that the AI matches how skilled workers reason on the job and provides realistic suggestions.
Once trained, the AI could potentially run at an offline workstation, a remote server wirelessly connected to the robot or on the robot itself.
The robots and AI models will be tested with a new physical model of a ship section, which the researchers call the Shipbuilding Test Block. The model will be reconfigurable so that it can represent many different stages of outfitting, shipboard systems and shipbuilding issues.
The roles of American team members include:
- Development of robotic systems and algorithms for ship outfitting, led by Papalia
- Establishment of shipyard collaborations, managed by Dave Singer, a professor of naval architecture and marine engineering
- Interviews with tradespeople and the integration of human knowledge, led by Matt Collette, professor of naval architecture and marine engineering; Leia Stirling, professor of robotics and industrial and operations engineering; and Patricia Alves-Oliveira, assistant professor of robotics
- Design and production of the Shipbuilding Test Block, led by Thomas McKenney, associate professor of practice in naval architecture and marine engineering
- Development of AI models that can process multiple kinds of data to help find optimal solutions, led by Faez Ahmed, associate professor of mechanical engineering at MIT.
The complementary Japanese projects are led by Yokohama National University, Osaka University, Osaka Metropolitan University and the National Maritime Research Institute.
