Zonghao Mu
Papers
2
Total Citations
28
H-Index
2
About
Zonghao Mu is a robotics researcher whose work lies at the intersection of perception, planning, and knowledge representation for autonomous systems. His primary research areas include sensor fusion for underwater localization and knowledge-driven task planning for robots. Mu’s most impactful contribution is his work on visual-pressure fusion for underwater robot localization, which addresses a critical challenge in monocular Visual Inertial Odometry (VIO)—the large scale errors introduced by slow underwater motion. By integrating pressure sensor data, his method enables more accurate and robust positioning, a fundamental capability for subsea exploration and inspection. This work has garnered 23 citations, reflecting its value to the marine robotics community. In parallel, Mu has pioneered the integration of Behavior Trees with Knowledge Graphs for robot task planning, a novel approach that automates strategy generation and enhances plan robustness. This research, with 5 citations, offers a scalable alternative to traditional planning methods, enabling robots to reason about their environment and tasks more intelligently. Mu’s contributions are shaping the future of autonomous robotics, particularly in challenging, unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Robot Planning based on Behavior Tree and Knowledge Graph5 citations · 2022