Multi-objective optimization of the kinematic parameters of fish-like swimming using a genetic algorithm method

Abstract

This paper investigates the kinematic optimization of fish-like swimming. First, an experiment was performed to detect the motion of the fish tail foil of a fish robot. Next, the kinematic swimming model was verified experimentally using an image processing method. The model includes two rotational motions: caudal foil motion and foil-pitching motion. The kinematic model allows us to evaluate the influence of motion trajectory in the optimization process. To optimize the propulsive efficiency and thrust, a multi-objective genetic algorithm was employed to handle with kinematic, hydrodynamic, and propulsion models. The results show that the caudal length has a significant effect on the performance of the flapping foil in fish-like swimming, and its influence on the motion trajectory may increase the propulsive efficiency to as high as 98% in ideal conditions. The maximum thrust coefficient can also reach approximately 3 in ideal conditions.