Zeng Xiuyun

Papers

Total Citations

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About

Zeng Xiuyun is a researcher whose work centers on advancing autonomous navigation and motion planning for robotic systems. Her primary contributions lie in the development of more efficient and robust path planning algorithms, with a particular focus on improving the Rapidly-exploring Random Tree (RRT) method. Her most cited paper, "Robot Path Planning Based on Improved RRT Algorithm" (2021), addresses critical limitations of traditional RRT approaches—such as slow convergence and non-optimal paths—by introducing enhancements that reduce computational overhead and generate smoother, more feasible trajectories for real-world robots. While her citation count is still growing, this work represents a foundational step in making sampling-based planning more practical for dynamic environments. Zeng’s research is especially relevant to fields like autonomous vehicles, service robotics, and industrial automation, where reliable and fast path generation is essential. Her ongoing efforts contribute to bridging the gap between theoretical algorithm design and real-time robotic applications, positioning her as an emerging voice in the robotics and artificial intelligence community.