FlowSight: Vision-Based Artificial Lateral Line Sensor for Water Flow Perception

Abstract

This paper presents a novel vision-based artificial lateral line (ALL) sensor, FlowSight, enhancing the perception capabilities of underwater robots. Through an autonomous vision system, FlowSight allows for simultaneous sensing the speed and direction of local water flow without relying on external auxiliary equipment. Inspired by the lateral line neuromast of fish, a flexible bionic tentacle is designed to sense water flow. Deformation and motion characteristics of the tentacle are modeled and analyzed using bidirectional fluid-structure interaction (FSI) simulation. Upon contact with water flow, the tentacle converts water flow information into elastic deformation information, which is captured and processed into an image sequence by the autonomous vision system. Subsequently, a water flow perception method based on deep neural networks is proposed to estimate the flow speed and direction from the captured image sequence. The perception network is trained and tested using data collected from practical experiments conducted in a controllable swim tunnel. Finally, the FlowSight sensor is integrated into the bionic underwater robot RoboDact, and a closed-loop motion control experiment based on water flow perception is conducted. Experiments conducted in the swim tunnel and water pool demonstrate the feasibility and effectiveness of FlowSight sensor and the water flow perception method.