Zhifeng Su

Nanchang Hangkong University, Inspection Technologies (United Kingdom)

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About

Zhifeng Su is a researcher focused on advancing autonomous navigation and simultaneous localization and mapping (SLAM) for mobile robots operating in complex, unknown environments. Their work centers on improving the computational efficiency and real-time performance of core robotic algorithms, particularly through the optimization of particle filter-based SLAM methods. Su’s most-cited paper, "Optimization Design and Experimental Study of Gmapping Algorithm" (2020, 16 citations), addresses the high computational cost and poor real-time performance of traditional particle filters by refining the Rao-Blackwellized particle filter approach, directly enhancing indoor robot localization and mapping. Building on this, their recent work "Research and Application of Robot Path Planning Algorithm Based on ROS" (2024) tackles autonomous navigation in dynamic settings by engineering a mobile robot system on the Robot Operating System (ROS) and improving global path planning algorithms. These contributions demonstrate Su’s commitment to bridging theoretical optimization with practical robotic applications, offering scalable solutions for real-world deployment. Their research is particularly valuable for students and engineers seeking efficient, ROS-integrated approaches to SLAM and path planning.