LQR Control and Optimization for Trajectory Tracking of Biomimetic Robotic Fish Based on Unreal Engine

A realistic and visible dynamic simulation platform can significantly facilitate research on underwater robots. This paper uses the Unreal Engine to generate a scene that resembles real ocean environments, before building a visual dynamic simulation platform in conjunction with the Air-Sim system. On this basis, the trajectory tracking of a biomimetic robotic fish is simulated and assessed. More specifically, we propose a particle swarm optimization algorithm-based control strategy to optimize the discrete linear quadratic regulator controller for the trajectory tracking problem, as well as tracking and controlling discrete trajectories with misaligned time series through introducing a dynamic time warping algorithm. Simulation analyses of the biomimetic robotic fish following a straight line, a circular curve without mutation, and a four-leaf clover curve with mutation are carried out. The obtained results verify the feasibility and effectiveness of the proposed control strategy.

Automated summary

This paper presents a visual dynamic simulation platform for underwater robots using the Unreal Engine and Air-Sim system. A particle swarm optimization algorithm-based control strategy is proposed to optimize trajectory tracking of a biomimetic robotic fish. The feasibility and effectiveness of the control strategy are verified through simulation analyses of different trajectory patterns.