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Towards Autonomous, Optimal Water Sampling with Aerial and Surface Vehicles for Rapid Water Quality Assessment  Open Access

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Journal of the ASABE. 67(1): 91-98. (doi: 10.13031/ja.15796) @2024
Authors:   Anh Nguyen, John-Paul Ore, Celso Castro-Bolinaga, Steven G. Hall, Sierra Young
Keywords:   Mariculture, Robotics, Traveling salesperson problem, Vehicle routing.


A practical workflow for optimizing sampling tours for a team of surface and aerial vehicles was developed.

Proposed workflow considers unique sensing capabilities of surface vehicles when assigning sampling locations.

Likely optimal tours can be found in less than 30 s for practical water quality sampling requirements.

Abstract. Most current marine aquaculture operations are located in coastal estuarine areas within one mile of the shoreline, and water quality in these production areas can quickly become unfavorable due to hydrodynamic processes and excessive runoff. The deployment of autonomous, robotic systems can improve the speed and spatiotemporal resolution of water sampling and sensing in mariculture production areas to assess water quality in the context of food safety. Specifically, teams of both aerial and surface vehicles can be deployed simultaneously to capitalize on the benefits of each system; however, a method to optimally design a feasible sampling tour for each robot is needed to maximize sample capacity and ensure efficient water sampling missions. This research brief presents the problem formulation and a solution method to determine optimal tours for a team of aquatic surface and aerial vehicles while considering different vehicle sampling capacities and endurance constraints. This method was implemented to design sampling missions of 15, 20, and 30 samples in both a 0.25 km2 and 3.9 km2 site, using sampling capacity and endurance constraints corresponding to real-world robots used for water sampling in mariculture environments. Results indicate that this optimization problem can be solved in near-real time in the field and yields feasible sampling tours for surface and aerial vehicles under different constraints. This work is a practical step towards developing teams of collaborative robots to persistently monitor adverse mariculture growing conditions so producers can implement data-driven, timely management strategies.

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