Lunxin Zhong

Papers

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About

Lunxin Zhong is a robotics researcher focused on advancing autonomous navigation and control for mobile robots in dynamic, real-world environments. Their key contributions lie in path planning and tracking control, addressing critical limitations in existing approaches that assume static obstacles. In their most cited work, "Hybrid Path Planning Algorithm Based on Improved Dynamic Window Approach" (2021, 6 citations), Zhong proposed a novel hybrid algorithm that integrates an improved dynamic window approach to enable safe, efficient navigation in environments with moving obstacles—a significant step beyond traditional static obstacle methods. This work has laid groundwork for more adaptive robot behavior in complex settings. Additionally, in "Tracking Control of Wheeled Mobile Robot Based on RBF Network Supervisory Control" (2020, 2 citations), Zhong tackled persistent challenges in wheeled robot trajectory tracking by employing a radial basis function (RBF) neural network for supervisory control, moving beyond conventional nonholonomic constraint-based models. Though early in their career, Zhong’s research demonstrates a clear trajectory toward practical, robust solutions for autonomous systems, with potential applications spanning industry, agriculture, and defense.