Papers
6
Total Citations
83
H-Index
4
About
Leijie Zhang is a robotics researcher whose work bridges the gap between legged locomotion and intelligent environment perception. His primary research areas include quadruped robotics, 3D LiDAR-based terrain analysis, and human-robot interaction for rehabilitation. Zhang’s most influential contribution is his comprehensive review of quadruped robots and environment perception (52 citations), which systematically analyzed major platforms like HyQ, StarlETH, and ANYmal, establishing a foundational reference for researchers in legged robotics. He developed a novel slope detection method using 3D LiDAR point cloud data specifically tailored for quadruped robots, enabling adaptive gait adjustments in unknown terrains—a critical advancement for autonomous navigation. Zhang also pioneered a human-following approach using binocular cameras that integrates kernelized correlation filters with multi-scale detection, achieving robust tracking in both indoor and outdoor environments. In the rehabilitation domain, he designed a control system combining brain-computer interfaces with virtual reality technology to make gait training more engaging for patients. His work on slope location and orientation estimation further refined LiDAR calibration techniques for the Velodyne VLP-16, enhancing environmental mapping accuracy. Through these contributions, Zhang has advanced the practical deployment of quadruped robots in unstructured environments while exploring innovative human-machine interfaces for medical applications.
Research Focus
Key Achievements
Top Papers
- 1A review of quadruped robots and environment perception52 citations · 2016
- 2A human-following approach using binocular camera11 citations · 2017
- 3A slope detection method based on 3D LiDAR suitable for quadruped robots8 citations · 2016
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