Yuan Zheng

Papers

Total Citations

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About

Yuan Zheng is a robotics researcher whose work focuses on advancing autonomous navigation and path planning, particularly addressing the persistent challenge of local minima in artificial potential field algorithms. His most cited paper, "Improvements on the virtual obstacle method" (2020, 14 citations), tackles a fundamental problem in mobile robotics: the tendency for robots to become trapped in local minimum points during path planning. By refining the virtual obstacle approach, Zheng has contributed to more robust and reliable navigation systems that allow robots to escape dead ends and continue toward their goals. This work builds on the widely used artificial potential field framework, enhancing its practical applicability in real-world environments. While his citation count reflects a growing influence in the field, Zheng's contributions are notable for addressing a core limitation that has long hindered the effectiveness of potential field-based algorithms. His research demonstrates a commitment to solving practical, real-world robotics problems through thoughtful algorithmic improvements, making his work valuable for researchers and engineers developing autonomous systems for applications ranging from warehouse logistics to search-and-rescue operations.