Max Robotics — Global Service Robot Directory + US Certification

About

Yuan-Jie Chen is a rising star in the field of biomimetic underwater robotics, with a research focus on bio-inspired sensing and hydrodynamic performance. His work centers on understanding and replicating the propulsion mechanisms of aquatic species, particularly the cownose ray, to develop more efficient and maneuverable autonomous underwater vehicles. Chen’s most-cited paper (12 citations, 2024) establishes a kinematic model and hydrodynamic analysis of biomimetic pectoral fins, providing foundational insights into ray-inspired locomotion. Building on this, he has pioneered the use of advanced machine learning frameworks—such as an AVOA-optimized CNN-BILSTM-SENet model (2025, 3 citations)—to predict and optimize fin performance. A notable contribution is his optimization strategy for bio-inspired lateral line sensor arrays in Autonomous Underwater Helicopters (AUH), addressing the critical limitations of conventional optical and acoustic sensing in extreme underwater environments. By integrating biological principles with cutting-edge computational methods, Chen is advancing the autonomous sensing and propulsion capabilities of next-generation underwater robots, promising significant improvements in their operational efficiency and adaptability.