Printable autonomous boats promise reduced road congestion
The 3D-printed, driverless boats can provide transport for goods and people and self-assemble into other floating structures. According to the researchers, such boats could service some waterway-rich cities, reducing road traffic. In the future, they say, the driverless boats could even be adapted to perform city services overnight – instead of during the day – to further reduce congestion on both 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,” says CSAIL Director Daniela Rus, Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science, and co-author of a paper describing the technology.
The boats – comprising rectangular 4 x 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 a matter of hours, say the researchers. The boats could also be equipped with environmental sensors to monitor a city’s waters and gain insight into urban and human health.
To make the boats, the researchers 3D-printed a rectangular hull with a commercial printer, producing 16 separate sections that were spliced together. Printing took around 60 hours, at which point the completed hull was sealed by adhering several layers of fiberglass.
On-board electronics – including a power supply, Wi-Fi antenna, GPS, and a minicomputer and microcontroller – are integrated onto the hull. In addition, an indoor ultrasound beacon system and outdoor real-time kinematic GPS modules allow for centimeter-level localization, while an inertial measurement unit (IMU) module monitors the boat’s yaw and angular velocity, among other metrics.
The boat’s rectangular shape allows the vessel to move sideways and to attach itself to other boats when assembling other structures. Four thrusters generating forward and backward forces are positioned in the center of each side, instead of at the four corners, which, say the researchers, makes the boat more agile and efficient.
In addition, the researchers developed an efficient version of a nonlinear model predictive control (NMPC) algorithm – generally used to control and navigate robots within various constraints – that enables the boat to track its position and orientation more quickly and accurately. The algorithm incorporates simplified nonlinear mathematical models that account for a few known parameters, such as drag of the boat, centrifugal and Coriolis forces, and added mass due to accelerating or decelerating in water.
The researchers used an efficient predictive-control platform to run their algorithm, which they say can rapidly determine upcoming actions and increases the algorithm’s speed by two orders of magnitude over similar systems. While other algorithms execute in about 100 milliseconds, the researchers’ algorithm takes less than 1 millisecond.
To demonstrate the control algorithm’s efficacy, the researchers deployed a smaller prototype of the boat along preplanned paths in a swimming pool and in the Charles River. Over the course of 10 test runs, the average tracking errors — in positioning and orientation — were smaller than tracking errors of traditional control algorithms.
The researchers next plan to develop adaptive controllers to account for changes in mass and drag of the boat when transporting people and goods. They are also refining the controller to account for wave disturbances and stronger currents.
The work was conducted as part of the “Roboat” project, a collaboration between the MIT Senseable City Lab and the Amsterdam Institute for Advanced Metropolitan Solutions (AMS). In 2016, as part of the project, the researchers tested a prototype that cruised around the city’s canals, moving forward, backward, and laterally along a preprogrammed path.
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