Papers
2
Total Citations
13
H-Index
2
About
Yujie Guo is a robotics researcher whose work focuses on advancing the autonomy and adaptability of self-reconfigurable robots, with a particular emphasis on path planning and optimization for complex environments. Her major contributions lie in developing novel algorithms that combine heat conduction-based methods with discrete optimization techniques, enabling robots like the hTetro system to dynamically alter their morphology and navigate challenging terrains—such as staircases—with enhanced efficiency. Her most-cited paper, "Path Planning for Reconfigurable hTetro Robot Combining Heat Conduction-Based and Discrete Optimization" (2021), has garnered 7 citations, while her follow-up work on combined grid and heat conduction optimization for staircase cleaning robots (2022) has received 6 citations. These studies demonstrate her ability to bridge theoretical optimization with practical robotic applications, addressing real-world needs in planetary exploration, rescue missions, and maintenance. Guo’s research is particularly notable for its interdisciplinary approach, merging heat transfer principles with robotics to solve path planning problems that are both computationally efficient and physically feasible. Her work is a valuable resource for students and researchers interested in reconfigurable systems, offering a clear pathway from algorithmic design to tangible robotic performance.
Research Focus
Key Achievements
Top Papers
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