Yi‐Ming Hua

Papers

Total Citations

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About

Yi-Ming Hua is a researcher advancing the frontier of mobile robotics, with a particular focus on intelligent target tracking and lightweight neural architectures. Their most-cited work, "Mobile robot tracking control based on lightweight network" (2025), addresses a critical challenge in the field: enabling real-time target recognition and following on computationally constrained platforms. By designing efficient network models that balance accuracy with low computational overhead, Hua’s research directly supports practical deployments in logistics, security, and autonomous driving—applications where onboard processing power is limited. This contribution has already garnered early attention with 2 citations, signaling its relevance to researchers working on embedded AI and robot autonomy. Hua’s work bridges the gap between advanced deep learning techniques and the real-world constraints of mobile systems, offering scalable solutions for dynamic environments. As the demand for intelligent, resource-efficient robots grows, Yi-Ming Hua’s research stands as a promising foundation for future innovations in autonomous tracking and control.