Papers
11
Total Citations
186
H-Index
7
About
Zhiying Tan is a leading researcher in mobile robotics, specializing in sensor fusion, autonomous navigation, and humanoid mechanism design. Their work addresses critical challenges in robot localization, perception, and control, particularly for complex indoor and rescue environments. Tan’s most influential contribution is the adaptive federated Kalman filter (AFKF) for indoor mobile robot positioning, which overcomes the limitations of single-sensor systems and has garnered 50 citations. They also developed a novel calibration method using an improved manta ray foraging optimization algorithm, achieving 35 citations by significantly reducing robotic arm positioning errors. In perception, Tan pioneered a fusion algorithm combining sparse LiDAR point clouds with image data for robust object detection (33 citations). Their work on path tracking control, integrating model predictive control with adaptive neural-fuzzy inference systems, has advanced omni-directional service robot performance. Tan has also contributed to humanoid robotics, designing a six-degree-of-freedom bioinspired torso, and published a comprehensive review of 2D LiDAR SLAM algorithms. With over 180 total citations across ten publications, Tan’s research consistently pushes the boundaries of sensor integration and autonomous decision-making, making them a key figure in modern robotics.
Research Focus
Key Achievements
Top Papers
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- 3Object Detection Based on Fusion of Sparse Point Cloud and Image Information33 citations · 2021
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- 5A Review of 2D Lidar SLAM Research13 citations · 2025
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- 9A LiDAR and camera fusion-based approach to mapping and navigation4 citations · 2021
- 10Recent Advances in Mobile Robot Localization in Complex Scenarios3 citations · 2023