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28 September 2020 Simulation Study for Obstacle Avoidance of Autonomous Underwater Vehicles
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Li, Y. and Zhang, M., 2020. Simulation study for obstacle avoidance of autonomous underwater vehicles. In: Yang, D.F. and Wang, H. (eds.), Recent Advances in Marine Geology and Environmental Oceanography. Journal of Coastal Research, Special Issue No. 108, pp. 104–108. Coconut Creek (Florida), ISSN 0749-0208.

Fish can track their prey and detect their surroundings in a complex water environment because of their unique underwater perception advantage. In this paper, on the basis of the sensing flow-field environment mechanism of the fish lateral line system, by analyzing the sonar system of an autonomous underwater vehicle (AUV) and the sensing principle of the fish lateral line system, the obstacle model of two shapes is established by using ANSYS software with computational fluid dynamics (CFD) analysis method. Two characteristic variables of different flow velocity and obstacle size are combined to simulate the flow field. The flow field of the fluid through different shape obstacles is analyzed, and the variation of the flow velocity to the flow field around the obstacle is compared, with the aim to establish a method to actively perceive and analyze the surrounding flow-field information through the characteristic parameters (velocity, size, shape) of the underwater obstacle itself. This paper provides a novel idea and practical sensing means for underwater detection technology, which can fill the void left by sonar and vision systems in a chaotic water environment. Improving the adaptive ability of AUV flow field and providing the theoretical basis for realizing the bionic lateral line engineering in the future, this research has an important impetus to the development of AUVs.

©Coastal Education and Research Foundation, Inc. 2020
Yixin Li and Minge Zhang "Simulation Study for Obstacle Avoidance of Autonomous Underwater Vehicles," Journal of Coastal Research 108(sp1), 104-108, (28 September 2020).
Received: 9 January 2020; Accepted: 22 March 2020; Published: 28 September 2020

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