Papers
3
Total Citations
27
H-Index
3
About
Fei Su is a robotics researcher specializing in autonomous navigation and exploration, with a focus on enabling unmanned ground vehicles to intelligently map unknown environments. Their core contributions lie in advancing the use of Reduced Approximated Generalized Voronoi Graphs (RA-GVGs) for real-time path planning and next-best-view selection. Su’s 2020 paper on improving autonomous exploration using RA-GVGs has garnered 13 citations, establishing a foundation for efficient indoor robotics navigation. Building on this, their 2022 work on real-time global action planning for 3D space exploration (10 citations) extends these techniques to complex, three-dimensional terrains, addressing the critical challenge of guiding robots through previously unknown environments. Su’s research directly tackles the computational bottleneck of next-best-view localization, offering practical frameworks that balance exploration efficiency with map accuracy. Their work is particularly notable for bridging theoretical graph-based methods with deployable robotic systems, making it highly relevant for researchers in field robotics, autonomous systems, and spatial intelligence. With a consistent focus on improving exploration frameworks, Fei Su continues to contribute valuable tools for the next generation of autonomous mobile robots.
Research Focus
Key Achievements
Top Papers
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