Abstract
Fish can use hydrodynamic stimuli, decoded by lateral line systems, to explore the surroundings. Eyeless species of the genus Sinocyclocheilus have evolved conspicuous horns on their heads, whereas the specific function of which is still unknown. Meanwhile, the eyeless cavefish exhibits more sophisticated lateral line systems and enhanced behavioral capabilities (for instance rheotaxis), compared with their eyed counterparts. Here, the influence of head horn on the hydrodynamic perception capability is investigated through computational fluid dynamics, particle image velocimetry, and a bioinspired cavefish model integrated with an artificial lateral line system. The results show strong evidence that the head horn structure can enhance the hydrodynamic perception, from aspects of multiple hydrodynamic sensory indicators. It is uncovered as that the head horn renders eyeless cavefish with stronger hydrodynamic stimuli, induced by double-stagnation points near the head, which are perceived by the strengthened lateral line systems. Furthermore, the eyeless cavefish model has ≈17% higher obstacle recognition accuracy and lower cost (time and sensor number) than eyed cavefish model is conceptually demonstrated, by incorporating with machine learning. This study provides novel insights into form-function relationships in eyeless cavefish, in addition paves the way for optimizing sensor arrangement in fish robots and underwater vehicles.