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Minglei Shao is a leading researcher in autonomous robotics, specializing in sensor-based path planning and navigation for nonholonomic and multi-link robotic systems. Their foundational work, "Development of autonomous navigation method for nonholonomic mobile robots based on the generalized Voronoi diagram" (2010, 9 citations), introduced innovative path planning and tracking methods that combine pure pursuit with generalized Voronoi diagrams (GVD), effectively solving motion tracking challenges for real-world nonholonomic robots. Shao further advanced the field by extending GVD concepts to more complex robotic structures, notably through the development of the two-identical-link hierarchical generalized Voronoi graph (2015, 7 citations) and the L2-generalized Voronoi graph for sensor-based exploration of unknown planar environments (2015, 2 citations). These contributions provide safe, sensor-driven roadmaps that maximize obstacle clearance, enabling robots to navigate autonomously using only local distance measurements. Shao's work bridges theoretical graph-based planning with practical sensor constraints, offering critical insights for researchers in mobile robotics, autonomous systems, and intelligent navigation. With a cumulative citation impact of 20, Shao's research continues to influence the design of safer, more efficient autonomous robots.