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Energy Efficient Distributed Adaptive Sampling using Networked Autonomous Underwater Vehicles

While water quality monitoring in real-time is important, it is often difficult to execute due to the constraints of using a single vehicle. In this paper, the author utilizes multiple BlueROV2s to create a distributed adaptive sampling algorithm to perform water quality monitoring in the Raritan River.

Abstract: Near-real-time water quality monitoring in rivers, lakes, and water reservoirs of different physical variables is critical to protect the aquatic life and to prevent further propagation of the potential pollution in the water. In order to measure the physical values in a Region of Interest (ROI), adaptive sampling is helpful as an energy- and time-efficient technique since an exhaustive search of an area is not feasible. Adaptive sampling using one robot is subject to the many constraints, such as a single point of failure, energy and delay inefficiencies. A robot could run out of energy midway during adaptive sampling–when scanning a large area, the sampling could take a long time even with adaptive sampling. If the robot doing the sampling fails, then the entire data is lost. To rectify these issues, we propose a distributed adaptive sampling algorithm using multiple robots. The algorithm ensures energy efficiency and time efficiency while also ensuring higher accuracy in the measurement by first specifying regions of interest, afterward, allocating a team of robots to search these regions more thoroughly without colliding or redoing work of another robot. By nature, the algorithm also accounts for a robot failure. Experiments are conducted in the Raritan River, New Jersey to evaluate the proposed solution.

Author: Nadeem, M.

Journal: Rutgers University

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