Water Quality Sampling and Multi-Parameter Monitoring System Based on Multi-Rotor UAV Implementation

Zhang, Rihong; Wang, Zhenhao; Li, Xiaomin; She, Zhun; Wang, Baoe

doi:10.3390/w15112129

Cited by 4 publications

(1 citation statement)

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“…Specifically, for a given list of locations (or cities) and the distances between each pair of locations, the TSP seeks to find the shortest possible route that goes through each location exactly once and returns to the original location, or depot. The TSP is one of the most widely studied combinatorial optimization problems (Laporte, 1992;Osaba et al, 2020) and has been used to solve optimal paths for single water sampling vehicles (Zhang et al, 2023). A coverage orienteering problem, which is a generalized case of the TSP, has also been used to solve the water sampling problem for surface vehicles (Zhang et al, 2020).…”

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confidence: 99%

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

Nguyen,

Ore,

Castro-Bolinaga

et al. 2024

Journal of the ASABE

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Highlights 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. Keywords: Mariculture, Robotics, Traveling salesperson problem, Vehicle routing.

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