Ambient Flow Perception of Freely Swimming Robotic Fish Using an Artificial Lateral Line System

Abstract

Robotic fish hold significant promise as efficient underwater systems, yet their inability to accurately perceive ambient flow hinders their deployment in real-world scenarios. Inspired by the natural lateral line system(LLS), a flow-responsive organ in fish that plays a crucial role in behaviors such as rheotaxis, this paper introduces the first Artificial Lateral Line System (ALLS)-based ambient flow classifier for robotic fish that allows robotic fish to perceive flow fields while swimming freely. To be specific, using just 5 pressure sensors and 3.5 minutes of swimming data, we trained a Long Short-Term Memory (LSTM) network, achieving a classification accuracy of 81.25% across 8 flow speed categories, ranging from 0.08 m/s to 0.18 m/s. A key innovation of this work is the formulation of ambient flow perception as a classification task, which not only enables the robotic fish to extract meaningful information but also enhances the robustness and generalizability of the perception framework. Extensive experiments further identify critical factors such as affecting the effectiveness of the ambient flow classifier, offering valuable insights for future development.