A lateral line system is a flow-responsive organ system, with which fish can effectively sense the surrounding flow field, thus serving functions in flow-aided fish behaviors. Inspired by such a biological characteristic, artificial lateral line systems (ALLSs) have been developed for promoting technological innovations of underwater robots. In this article, we focus on investigating state estimation of a freely swimming robotic fish in multiple motions, including rectilinear motion, turning motion, gliding motion, and spiral motion. The state refers to motion parameters, including linear velocity, angular velocity, motion radius, etc., and trajectory of the robotic fish. Specifically, for each motion, a pressure variation (PV) model that links motion parameters to PVs surrounding the robotic fish is first built; then, a linear regression analysis method is used for determining the model parameters. Based on the acquired PV model, motion parameters can be estimated by solving the PV model inversely using the PVs measured by the ALLS. Finally, a trajectory estimation method is proposed for estimating trajectory of the robotic fish based on the ALLS-estimated motion parameters. The experimental results show that the robotic fish is able to estimate its trajectory in the aforementioned multiple motions with the aid of ALLS, with small estimation errors.

中文翻译：

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基于人工侧线系统的鳍驱动水下机器人在线状态估计

侧线系统是一种流动响应器官系统，鱼可以通过侧线系统有效地感知周围的流场，从而在鱼的流动辅助行为中发挥作用。受这种生物学特性的启发，人工侧线系统（ALLS）被开发出来，以促进水下机器人的技术创新。在本文中，我们重点研究自由游泳机器鱼在多种运动中的状态估计，包括直线运动、转弯运动、滑翔运动和螺旋运动。状态是指运动参数，包括线速度、角速度、运动半径等，以及机器鱼的轨迹。具体来说，对于每个运动，首先建立一个将运动参数与机器鱼周围的 PV 联系起来的压力变化 (PV) 模型；然后，使用线性回归分析方法来确定模型参数。基于获取的 PV 模型，可以通过使用 ALLS 测量的 PV 逆求解 PV 模型来估计运动参数。最后，提出了一种基于ALLS估计的运动参数估计机器鱼轨迹的轨迹估计方法。实验结果表明，机器鱼能够借助ALLS在上述多次运动中估计其轨迹，估计误差很小。提出了一种基于ALLS估计运动参数估计机器鱼轨迹的轨迹估计方法。实验结果表明，机器鱼能够借助ALLS在上述多次运动中估计其轨迹，估计误差很小。提出了一种基于ALLS估计的运动参数估计机器鱼轨迹的轨迹估计方法。实验结果表明，机器鱼能够借助ALLS在上述多次运动中估计其轨迹，估计误差很小。