Xin Na

Papers

Total Citations

H-Index

About

Xin Na’s research lies at the intersection of bio-inspired sensing, hydrodynamics, and robotics, with a focus on replicating the remarkable flow-sensing capabilities of fish lateral line systems. His most-cited work, "Maximized Hydrodynamic Stimulation Strategy for Placement of Differential Pressure and Velocity Sensors in Artificial Lateral Line Systems" (2022, 24 citations), addresses a critical challenge in biomimetic engineering: optimizing sensor placement to mimic canal neuromasts (pressure gradient detection) and superficial neuromasts (velocity detection). By developing a maximized hydrodynamic stimulation strategy, Na provides a systematic framework for designing artificial lateral lines (ALLs) that can accurately perceive complex flow fields—a breakthrough with direct applications in underwater robotics, autonomous navigation, and environmental monitoring. This work has already influenced subsequent studies on sensor fusion and flow-based control, earning recognition for its practical impact. Beyond this paper, Na’s contributions span the broader field of bio-inspired sensing, where his innovative approaches to sensor placement and signal processing continue to advance the state of the art. For students and researchers, Na’s work exemplifies how biological principles can be translated into engineering solutions, offering a compelling blueprint for future innovations in aquatic robotics and flow-field perception.