A hexagonal grid-based sampling planner for aquatic environmental monitoring using unmanned surface vehicles

Li, Teng and Xia, Min and Chen, Jiahong and Gao, Shujun and De Silva, Clarence (2017) A hexagonal grid-based sampling planner for aquatic environmental monitoring using unmanned surface vehicles. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017. 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 . Institute of Electrical and Electronics Engineers Inc., CAN, pp. 3683-3688. ISBN 9781538616451

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Abstract

Unmanned Surface Vehicles (USV) with capabilities of mobile sensing, data processing, and wireless communication have been deployed to support remote aquatic environmental monitoring. This paper introduces a sampling planner for spatiotemporal survey of an aquatic environment using a USV-based sensing system. The sampling planner is proposed to distribute the Sampling Locations of Interest (SLoIs) over a geographical area and generate paths for the USVs to visit more SLoIs within their energy budgets. The sampling locations are chosen based on a cellular decomposition of uniform hexagonal cells. The SLoIs are visited and sensed by the USVs along a planned path ring, which is generated through a Spanning Tree-based Planning (STP) approach. To ensure that each SLoI measures within a certain time interval, multiple USVs are assigned to travel along the sub-paths that are divided from the generated path ring. In this paper, first an execution example presents the effectiveness of the proposed method. Then, the performance of the proposed sampling planner is demonstrated based on two application scenarios using USVs for aquatic environmental monitoring. The experimental results are presented in this paper.

Item Type:
Contribution in Book/Report/Proceedings
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2606
Subjects:
ID Code:
138936
Deposited By:
Deposited On:
19 Nov 2019 10:20
Refereed?:
Yes
Published?:
Published
Last Modified:
24 Nov 2020 11:58