Yizhi Wu

Papers

Total Citations

H-Index

About

Yizhi Wu is a researcher in robotics and autonomous navigation, with a primary focus on path planning algorithms for mobile robots operating in unknown environments. Their most-cited work, "Convolutionally evaluated gradient first search path planning algorithm without prior global maps" (2021), introduces a novel approach that enables robots to navigate efficiently without relying on pre-existing global maps—a critical capability for real-world applications like search-and-rescue or exploration. This algorithm leverages convolutional evaluation of gradient information to prioritize search directions, reducing computational overhead while maintaining robust obstacle avoidance. With 13 citations, this paper has already garnered attention for its practical relevance in the field of simultaneous localization and mapping (SLAM) and reactive navigation. Wu’s contributions address a key bottleneck in autonomous systems: balancing real-time decision-making with limited sensor data. Their work is particularly notable for bridging the gap between theoretical path planning and deployment in unstructured settings, offering a scalable solution for drones, ground vehicles, and other autonomous agents. As the demand for intelligent, map-free navigation grows, Wu’s research continues to influence both academic studies and industrial robotics development.