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About

Zihong Lu is a robotics researcher whose work focuses on safe autonomous navigation in complex, unstructured environments. His key contributions lie at the intersection of motion planning, control theory, and perception, particularly for ground robots operating in challenging outdoor terrains. Lu’s most influential work, the “Dynamic Control Barrier Function-based Model Predictive Control to Safety-Critical Obstacle-Avoidance of Mobile Robot” (2023), has garnered 117 citations. This paper introduces a real-time LiDAR-based method that generates local grid maps, clusters obstacles using DBSCAN, and encloses them with minimum bounding ellipses, enabling safe avoidance of both static and dynamic obstacles. His equally significant “PUTN: A Plane-fitting based Uneven Terrain Navigation Framework” (2022, 75 citations) addresses the critical challenge of navigating rough, 3D unstructured environments—a step beyond traditional indoor 2D navigation. By proposing a plane-fitting approach, Lu’s framework allows ground robots to traverse uneven terrain reliably. Together, these works demonstrate Lu’s impact on safety-critical robotics, offering practical solutions for real-world deployment in agriculture, search-and-rescue, and autonomous exploration.