Papers
10
Total Citations
98
H-Index
5
About
Zehui Meng’s research lies at the intersection of robotic perception, autonomous navigation, and intelligent manipulation, with a focus on enabling robots to operate effectively in complex, unstructured environments. His major contributions include developing a unified Convolutional Neural Network for simultaneous scene recognition and object detection on mobile manipulators, a framework that has garnered 20 citations. He also pioneered trajectory generation methods for ground robots navigating beyond 2D planes, addressing passive and active height variations, and created an intelligent system for autonomous exploration and active SLAM in unknown environments (15 citations). Meng’s work on active path clearing through environment reconfiguration and semantics-boosted navigation with path creation has advanced robotic adaptability in cluttered, human-centric spaces. His notable achievements include integrating relational reasoning and Voronoi local graph planning for target-driven navigation, as well as detection and state estimation of moving objects on moving bases for indoor navigation. With over 100 total citations across his most-cited papers, Meng’s research has significantly impacted the fields of mobile robotics, computer vision, and autonomous systems, offering practical solutions for real-world deployment.
Research Focus
Key Achievements
Top Papers
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