Papers
6
Total Citations
62
H-Index
4
About
Zheng Yao is a researcher whose work spans robotics, autonomous systems, and wireless sensor networks, with particular expertise in simultaneous localization and mapping (SLAM), multi-robot coordination, and indoor positioning technologies. His most influential contribution, "An Online Multi-Robot SLAM System Based on Lidar/UWB Fusion" (2021), has garnered 40 citations and demonstrates his leadership in advancing collaborative robotic systems — showing how multi-robot configurations outperform single-robot setups in exploration speed and task complexity. This work exemplifies his broader focus on sensor fusion, combining LiDAR and Ultra-Wideband (UWB) technologies to push the boundaries of robot autonomy. Yao has also made meaningful contributions to motion trajectory planning using ROS frameworks and developed OW-LOAM, an observation-weighted approach to LiDAR odometry that refines state-of-the-art mapping methods. His deep reinforcement learning research further explores autonomous deployment of local positioning systems in GNSS-denied environments — critical for search-and-rescue scenarios. Earlier in his career, Yao addressed software management challenges in wireless sensor networks through profile-based techniques, reflecting a long-standing interest in adaptive, dynamic systems. Together, his body of work positions him as a versatile contributor to intelligent robotics and networked autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1An Online Multi-Robot SLAM System Based on Lidar/UWB Fusion40 citations · 2021
- 2
- 3Research on Motion Trajector Planning of Industrial Robot Based on ROS6 citations · 2022
- 4
- 5OW-LOAM: Observation-Weighted LiDAR Odometry and Mapping3 citations · 2022
- 6