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Bio-inspired robotic fish enabled motion tomography

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Abstract

Water current is an important factor in the operation of marine robotic vehicles (MRVs). When cruising in a confined area, the perception of the flow field of this area greatly helps MRVs in path planning and improves energy efficiency. Traditional current observations rely on information obtained through buoys and satellites. It is expensive and time-inefficient. Therefore, using the position and velocity information of the vehicle to predict the flow field can significantly improve the time-efficiency and reduce the cost. Motion tomography is a method that uses vehicle navigation information to estimate current-induced flow and generate a flow field map. This method provides a time-efficient and convenient way to monitor water currents. Bio-inspired robotic fish is an ideal agent for shallow water environmental sensing tasks due to its high maneuverability in grassy environments, low noise propulsion, and multi-functional capabilities. Using trajectory of robotic fish to estimate the flow field can significantly benefit transportation and environmental study. To improve the estimation accuracy, we add an active heading control (AHC) to moderate the passive heading change caused by the flow field. With the position and direction data collected from multiple trips, a vectorized flow map could accurately estimate the flow field.

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Please contact the corresponding author: Zheng Chen at zchen43@central.uh.edu for accessing the data published this paper.

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Acknowledgements

This research is supported by Texas Commission on Environmental Quality through Subsea Systems Institute Award #582-15-57593 and the Center for Carbon Management in Energy at the University of Houston. This project was paid for [in part] with federal funding from the Department of the Treasury through the State of Texas under the Resources and Ecosystems Sustainability, Tourist Opportunities, and Revived Economies of the Gulf Coast States Act of 2012 (RESTORE Act). The content, statements, findings, opinions, conclusions, and recommendations are those of the author(s) and do not necessarily reflect the views of the State of Texas or the Treasury.

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of Houston, 4726 Calhoun Rd, Room N207, Houston, TX, 77204, USA

    Wenyu Zuo & Zheng Chen

  2. Department of Electrical and Computer Engineering, Georgia Tech University, Atlanta, GA, USA

    Fumin Zhang

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  1. Wenyu Zuo
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  2. Fumin Zhang
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Correspondence to Zheng Chen.

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Zuo, W., Zhang, F. & Chen, Z. Bio-inspired robotic fish enabled motion tomography. Int J Intell Robot Appl 7, 474–484 (2023). https://doi.org/10.1007/s41315-023-00284-0

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