Department of Mechanical and Electrical Engineering & Key Laboratory of Ocean Engineering of Shang Dong Province, Ocean University of China, Qingdao 266100, China.
Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow G1 1XQ, UK.
Sensors (Basel). 2020 Mar 9;20(5):1512. doi: 10.3390/s20051512.
At present, autonomous underwater vehicles (AUVs) cannot perceive local environments in complex marine environments, where fish can obtain hydrodynamic information about the surrounding environment through a lateral line. Inspired by this biological function, an artificial lateral line system (ALLS) was built on a moving bionic carrier using the pressure sensor in this paper. When the carrier operated with different speeds in the flow field, the pressure distribution characteristics surrounding the carrier were analyzed by numerical simulation, where the effect of the flow angle between the fluid velocity direction and the carrier navigation direction was considered. The flume experiment was carried out in accordance with the simulation conditions, and the analysis results of the experiment were consistent with those in the simulation. The relationship between pressure and fluid velocity was established by a fitting method. Subsequently, the pressure difference method was investigated to establish a relationship model between the pressure difference on both sides of the carrier and the flow angle. Finally, a back propagation neural network model was used to predict the fluid velocity, flow angle, and carrier speed successfully in the unknown fluid environment. The local fluid environment perception by moving carrier carrying ALLS was studied which may promote the engineering application of the artificial lateral line in the local perception, positioning, and navigation on AUVs.
目前，自主水下航行器（AUV）在复杂海洋环境中无法感知局部环境，而鱼类可以通过侧线获得周围环境的水动力信息。受此生物功能的启发，本文在移动仿生载体上使用压力传感器构建了人工侧线系统（ALLS）。当载体在流场中以不同速度运行时，通过数值模拟分析载体周围的压力分布特征，考虑了流场中流体速度方向与载体航行方向之间的夹角的影响。根据模拟条件进行水槽实验，实验分析结果与模拟结果一致。通过拟合方法建立压力与流体速度之间的关系。然后，研究了压力差方法，建立了载体两侧压力差与流场夹角之间的关系模型。最后，使用反向传播神经网络模型成功地在未知流体环境中预测了流体速度、流场夹角和载体速度。研究了携带 ALLS 的移动载体的局部流体环境感知，这可能会促进人工侧线在 AUV 局部感知、定位和导航中的工程应用。
