cs.ROOct 5, 2026

An Autonomous, 3D Printed, Waterjet-Powered, Open-Source Robotic Trimaran for Environmental Inspection and Monitoring

Authors: Reuben O'Brien, Martin Lambrechtse-Reid, Minas Liarokapis

Organizations: New Dexterity Research Group, Department of Mechanical and Mechatronics Engineering, The University of Auckland, New Zealand

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Abstract

Versatile, autonomous robotic boats can offer excellent environmental inspection and monitoring solutions for remote, dangerous, hard to reach, or access protected water bodies. This paper introduces such a platform in the form of an autonomous, cost-effective, waterjet-powered robotic trimaran. Motivated by the need for an efficient aquatic monitoring, particularly in Aotearoa - New Zealand's diverse environments, the trimaran provides an efficient, low-cost, and easy to replicate alternative to resource-intensive research vessels. The proposed platform, costs $600-1,500 USD to develop (depending on the sensing system configuration), weighs under 5 kg, and excels in bathymetry and water quality testing. The trimaran can reach speeds of up to 2 m/s offering obstacle avoidance of natural features, such as rocks. Utilizing off-the-shelf components and 3D printing technology, the proposed platform offers excellent reproducibility and robustness while operating in shallow waters with its jet propulsion system. The paper presents in detail the design characteristics, the sensing system employed, testing results focusing on bathymetry, and highlights the ability of vessel and the potential for future research and data collection.

Figures & tables

Figure 1: The proposed autonomous, 3D printed, waterjet-powered, open-source robotic trimaran.
Figure 2: Exploded view of the proposed autonomous, 3D-printed, waterjet-powered, open-source trimaran platform.
Figure 3: The side and top views of the proposed trimaran platform. Subfigure (a), presents a side view highlighting the inverted bow. Subfigure (b), presents the bottom of the hull, displaying the tapered stern and jet inlet.
PartQtyCost (USD)Weight (kg)
Powertrain
BLDC motor 3660 3180 kv1$98.690.24
ESC 120 A1$105.790.15
LiPo 3S 80C 5000 mAh1$175.210.46
LiPo 6S 80C 5000 mAh1$75.000.74
Drive shaft coupling1$5.56-
Table I: Trimaran Components
Figure 4: Righting lever of the trimaran hull is seen at a range of deck inclination levels (roll angles). This highlights that the peak lever is at 38.2 deg and the point of negative lever (unrecoverable roll angle) is at 120 deg.
Figure 5: Free surface wave pattern generated by the trimaran at a speed of 2 m/s, ignoring the affects of viscosity and wave breaking (idealised calculation). The simulations shows how the waves from each hull interact, with a peak of 50 mm and a trough of 40 mm from the water line.
Figure 6: A block diagram of all the key electronics that make up the proposed autonomous trimaran platform. There are three main categories: i) Power (red), containing batteries and power modules, ii) Control (blue), representing the core component: the flight controller. Lastly, iii) Actuation (yellow) and Sensing (green), representing the motors along with the vision and algorithmic processing components.
Figure 7: GPS tracking of the autonomous trimaran at the Newmarket lake in Auckland, New Zealand (36°51’52.2"S, 174°47’00"E) is shown on subfigure (a) with the measured path against the target path. This route contained 9 straights of 15 to 20 m with nine 2 m long joining sections. Subfigure (b) details the sonar data collected over the path showing a depth range of 0.3-0.7 m. The data displayed in subfigures (c,d) are from the route traverse. Subfigure (c) is detailing the pitch range of the hull (0 to 9 degrees), with a steady 4 degrees as it maintained the target speed of 2 m/s. Subfigure (d) highlights the minimal roll (-6 to 6 degrees) of the hull as it performs 90 degree turns between the straights.
Figure 8: A comparison between the predicted power required at varying speeds against the measured power during testing.
Figure 9: Autonomous operation experiments. Subfigure (a) presents the trimaran operating amongst black swans, showcasing the small scale of the vessel. Subfigure (b), demonstrates that the bio-fouling present during testing, was of no hindrance. Subfigure (c), shows that the platform can operate efficiently in low-light and at night time conditions thanks to the available lighting that allows other vessels to interpret the direction of travel and prevent a collision. Lastly, subfigure (d) shows the wake created from the hull which closely resembles the estimated wake during the simulation.
Figure 10: Obstacle avoidance of rocks located in the Western Springs lake in Auckland, New Zealand (36°51’55.7"S, 174°43’19.6"E) using the ’bendy-ruler’ algorithm from the 2D LiDAR data.

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