基于鱼类侧线感知机理的流场辨识方法及仿真研究

在研究鱼类侧线感知机理的基础上,综合运用了流体动力学,边界层理论,耦合仿生学等理论,建立类似于鱼神经丘模型的神经网络模型,从数值计算和仿真分析两方面对鱼体侧线系统进行模拟,并将研究结论应用于自治水下机器人(Autonomous underwater vehicle,AUV)的导航及目标定位,为AUV导航和环境辨识提供新思路。计算机仿真结果表明,基于鱼类侧线感知机理构建的感知模型可以有区分度地对一个新的水流工况进行识别,可为AUV的导航及目标定位提供支持。

Simulation Research in Water Condition Recognition Method Based on Fish Lateral Line Sensing Mechanism

Based on fish lateral line sensing mechanism, integrated fluid dynamics, boundary layer theory and coupling theory were used to establish a neural network model similar to the fish neuromas model. By simulating fish lateral line system numerically, the results provide guidelines applying to autonomous underwater vehicle (AUV) navigation and target location. A new method for AUV navigation and environment perception is provided. Simulation results show that based on the sensing mechanism of fish lateral line, a perception model is built to help AUV identify a new water condition distinguishably. This discovery can be used in navigation and positioning of AUV.