An international team is working to develop a system within which 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.
With funding from a US$6.2million 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 a 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.
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‘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, a University of Michigan assistant professor of naval architecture and marine engineering. ‘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.’
‘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.
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The US 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.
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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 trade-offs 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.
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 USA and Japan to ensure that the AI matches how skilled workers reason on the job and provides realistic suggestions.
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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.