Yinchuan Wang
Papers
3
Total Citations
9
H-Index
2
About
Yinchuan Wang is a robotics researcher focused on enabling safe, autonomous navigation for mobile robots in complex, unstructured environments. His work centers on three interconnected challenges: risk-aware path planning, robust localization on non-level terrain, and computationally efficient odometry for resource-constrained platforms. Wang’s most cited paper, “History-Aware Planning for Risk-free Autonomous Navigation on Unknown Uneven Terrain” (2024, 4 citations), introduces a layered pipeline that dynamically extends a tree structure during navigation, allowing a robot to anticipate and avoid hazards without a pre-built map. This work directly addresses the critical gap between local reactive control and global planning in mapless settings. In “Low-drift LiDAR-only Odometry and Mapping for UGVs in Environments with Non-level Roads” (2022, 3 citations), he tackles pose drift and map distortion caused by ascents and descents, a common failure point for ground vehicles. Most recently, his 2025 paper “DUAL-LIO” (2 citations) achieves a practical balance between accuracy and computational cost by leveraging dual-inertia and body constraints for legged robots. Wang’s contributions are particularly notable for their systematic, layered approach—breaking down the autonomy stack into interoperable modules that can be deployed on real-world robots with limited payloads. His work is directly relevant for students and engineers building field-deployable UGVs and legged systems.
Research Focus
Key Achievements
Top Papers
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