Max Robotics — Global Service Robot Directory + US Certification

About

Linxiang Wang is a pioneering researcher in bio-inspired robotics and computational fluid dynamics, with a focus on developing intelligent, efficient propulsion systems for soft-bodied underwater robots. His major contributions center on the design and optimization of the jellyfish-inspired mantle undulated propulsion robot (MUPRo), where he has advanced the field by integrating reduced-order modeling and deep learning to predict and enhance hydrodynamic performance. Wang’s work on nonintrusive reduced-order models (NIROM) using proper orthogonal decomposition (POD) and long short-term memory (LSTM) neural networks has enabled high-fidelity, computationally efficient simulations of fluid-structure interactions—a critical step toward real-time control of soft robots. His most cited papers, including "Propulsion optimization of a jellyfish-inspired robot based on a nonintrusive reduced-order model" (8 citations) and "Parameter optimization of the bio-inspired robot propulsion through deep learning" (7 citations), reflect the growing impact of his methods. Beyond underwater robotics, Wang has also contributed to climbing robots for transmission line maintenance, demonstrating versatility in applied robotics. His work bridges the gap between data-driven modeling and practical robot design, offering powerful tools for next-generation autonomous systems.