3D Printed Autonomous Boats Could Reduce Traffic and Self-Assemble into Other Structures

Autonomous cars are becoming a reality, as self-driving vehicles finally begin to leave the concept stage and actually arrive on the streets. It would make sense, then, that autonomous boats wouldn’t be far behind, especially in cities like Venice, Bangkok and Amsterdam where heavy amounts of travel take place on waterways within the cities. Imagine self-driving boats taking people around the city as well as delivering goods, reducing the amount of traffic on the roads and giving people a new way to commute and sight-see.

The boats designed by MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Senseable City Lab in the Department of Urban Studies and Planning (DUSP) can be quickly and easily 3D printed. They’re being prototyped as part of the Roboat project, which is a collaboration between the Senseable City Lab and the Amsterdam Institute for Advanced Metropolitan Solutions (AMS). In 2016, the researchers tested a prototype in Amsterdam’s canals. The rectangular boat moved forward, backward and laterally along a pre-programmed path.

The researchers also believe that the boats can be used in the future to perform city service overnight instead of during the daytime, reducing congestion on roads and canals.

“Imagine shifting some of infrastructure services that usually take place during the day on the road — deliveries, garbage management, waste management — to the middle of the night, on the water, using a fleet of autonomous boats,” said CSAIL Director Daniela Rus. “Again, some of the activities that are usually taking place on land, and that cause disturbance in how the city moves, can be done on a temporary basis on the water.”

The boats, which are rectangular 4×2 meter hulls equipped with sensors, microcontrollers, GPS modules, and other hardware, could also be programmed to self-assemble into floating bridges, concert stages, platforms for food markets, and other structures in only minutes. In addition, they could be equipped with environmental sensors to monitor water quality.

Updates to the Roboat project have been published in a paper that will be presented at this week’s IEEE International Conference on Robotics and Automation. Authors of the paper include Rus; Wei Wang, a joint postdoc in CSAIL and the Senseable City Lab; Luis A. Mateos and Shinkyu Park, both DUSP postdocs; Pietro Leoni, a research fellow, and Fábio Duarte, a research scientist, both in DUSP and the Senseable City Lab; Banti Gheneti, a graduate student in the Department of Electrical Engineering and Computer Science; and Carlo Ratti, a principal investigator and professor of the practice in the DUSP and director of the MIT Senseable City Lab.

The paper describes several new developments to the project, including a rapid fabrication technique, a more efficient and agile design, and advanced trajectory-tracking algorithms that improve control, precision docking and latching, and other tasks.

The boat was 3D printed in 16 pieces, which took about 60 hours, then fused together and sealed with several layers of fiberglass. It is rectangular in shape to allow it to move sideways and attach to other boats when assembling structures. Four thrusters are positioned in the center of each side instead of at the corners, generating both forward and backward forces.

The researchers also generated an efficient nonlinear model predictive control (NMPC) algorithm that allows the boat to track its position more quickly and accurately. To demonstrate the effectiveness of the algorithm, the researchers sent a smaller prototype of the boat along pre-planned paths in a swimming pool and in the Charles River. Over 10 test runs, they observed average tracking errors in positioning and orientation smaller than the tracking errors of traditional control algorithms.

The accuracy is due in part to the boat’s onboard GPS and IMU modules, which determine position and direction, respectively, down to the centimeter. The NMPC algorithm takes the data from those modules and weighs various metrics to steer the boat. The algorithm is implemented in a controller computer and regulates each thruster individually, updating every 0.2 seconds.

“The controller considers the boat dynamics, current state of the boat, thrust constraints, and reference position for the coming several seconds, to optimize how the boat drives on the path,” said Wang. “We can then find optimal force for the thrusters that can take the boat back to the path and minimize errors.”

The next step is to develop adaptive controllers to account for changes in mass and drag of the boat when transporting people and goods. The researchers are also adjusting the controller to account for waves and stronger currents.

“We actually found that the Charles River has much more current than in the canals in Amsterdam,” Wang said. “But there will be a lot of boats moving around, and big boats will bring big currents, so we still have to consider this.”

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