Weibo Huang

Papers

Total Citations

H-Index

About

Weibo Huang is a leading researcher in mobile robotics, specializing in autonomous navigation, semantic perception, and motion planning for dynamic environments. His work addresses critical challenges in enabling robots to operate intelligently in complex, real-world settings. Huang’s most influential contribution is his work on sensor-based complete coverage path planning (CCPP) for cleaning robots, which has garnered 47 citations. This research introduced algorithms that allow robots to efficiently traverse every accessible area while dynamically replanning paths when obstacles appear, a significant advancement over static methods. Building on this, he developed MISD-SLAM, a multimodal semantic SLAM system (25 citations) that enhances robot localization and mapping by integrating semantic understanding to handle moving objects and high-level scene interpretation. Most recently, Huang has pioneered the use of deep reinforcement learning for motion planning in retail environments, as seen in his SPSD framework for supermarket robots. His work consistently bridges low-level sensor data with high-level semantic reasoning, pushing the boundaries of how robots perceive and act in unpredictable spaces. Huang’s research is essential reading for anyone interested in the future of autonomous service robots.