Real-time position and orientation estimation in dual self-propelled swimmers using artificial lateral line systems with intermittent swimming strategy

Abstract

In nature, fish leverage lateral lines to detect subtle variations in flow velocity and pressure. Inspired by this, artificial lateral line systems (ALLS) have been developed, with notable success in underwater target recognition and localization. However, due to the inherent complexity of fluid dynamics, the perceptual capabilities of current systems remain significantly inferior to those of real fish, typically limited to detecting simple objects under fixed spatial configurations between the perceiver and target. In this work, the perception capabilities of ALLS are extended to a more challenging scenario, wherein a free-swimming robotic boxfish is enabled to estimate, in real time, both the position and orientation of a free-swimming robotic koi carp. To improve signal fidelity, a bio-inspired intermittent swimming pattern is introduced to reduce the impact of the perceiver’s own oscillations on the flow field and suppress sensor noise. A hybrid network architecture is proposed to extract informative features from complex vortex-induced pressure signals, wherein an attention mechanism is incorporated to facilitate enhanced spatiotemporal feature extraction across sensor channels. This architecture outperforms conventional models in both accuracy and efficiency. Extensive experiments on both Computational Fluid Dynamics and real-world platforms demonstrate that the perceiver can precisely infer the target’s position and orientation from local pressure data alone. These results affirm the robustness of the proposed method and shed light on the intermittent behaviors in real fish, offering new avenues for bio-inspired robotic perception.