Haodi Yao
Papers
3
Total Citations
9
H-Index
2
About
Haodi Yao is a robotics researcher whose work lies at the intersection of autonomous navigation, multi-robot coordination, and efficient on-device perception. His research focuses on enabling robots—from quadrotors to formation-flying teams—to operate intelligently in dynamic, real-world environments. Yao’s most cited paper, "An Improved APF-based Path Planning Algorithm for a Quadrotor Intercepting Autonomous Ground Robots" (2019, 4 citations), tackles the complex challenge of a quadrotor intercepting moving targets while avoiding obstacles, advancing practical interception and pursuit strategies. In "Data Links Enhanced Relative Navigation for Robotic Formation Applications" (2020, 2 citations), he addresses the critical problem of relative positioning in multi-robot teams, proposing a method that leverages communication links to improve formation accuracy—a key enabler for cooperative tasks like search-and-rescue or surveillance. Most recently, his work "EdgePoint: Efficient Point Detection and Compact Description via Distillation" (2024, 3 citations) introduces a lightweight neural network for fast interest point detection and compact description, specifically designed for edge devices in applications like multi-robot SLAM and collaborative localization. This contribution is particularly notable for bridging the gap between high-performance computer vision and the computational constraints of real-world robotic systems.
Research Focus
Key Achievements
Top Papers
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