Online State Estimation of a Fin-Actuated Underwater Robot Using Artificial Lateral Line System

Abstract

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.