Papers
3
Total Citations
29
H-Index
3
About
Tang We is a rising figure in autonomous robotics, with a focused expertise in multi-sensor fusion, 3D LiDAR mapping, and cooperative multi-robot navigation. His most impactful contribution addresses a fundamental challenge in field robotics: constructing accurate static environmental maps in the presence of dynamic objects. His 2023 paper on this topic, which has garnered 15 citations, introduces a novel method that filters out interference from moving entities, ensuring map consistency for reliable localization and path planning. Building on this, We developed a robust localization framework that fuses LiDAR, IMU, and GNSS data using a two-step particle adjustment strategy, effectively mitigating GNSS signal degradation in urban canyons—a critical advancement for real-world deployment. His work extends to large-scale systems, where he proposed a cooperative path planning method that combines fuzzy comprehensive evaluation with behavior regulation to coordinate multi-robot teams efficiently. With a total of 29 citations across his most-cited works, Tang We is establishing himself as a key contributor to the practical realization of autonomous navigation in complex, unstructured environments, bridging the gap between theoretical algorithms and reliable, real-world performance.
Research Focus
Key Achievements
Top Papers
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