Yuhui Jin
Papers
3
Total Citations
12
H-Index
2
About
Yuhui Jin is a researcher advancing the field of autonomous robotics through the application of deep reinforcement learning to critical navigation and control problems. Jin’s primary research areas include coverage path planning, trajectory tracking, and intelligent control in unknown or unstructured environments. In their most-cited work, “Deep Reinforcement Learning Based Coverage Path Planning in Unknown Environments” (2024, 8 citations), Jin introduced a robust solution using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. This approach enables a robot to efficiently cover a designated area with minimal redundancy, overcoming the limitations of traditional path planning methods in dynamic, unknown settings. Building on this foundation, Jin’s subsequent work (2024, 2 citations) further refined the TD3-based framework for enhanced real-world applicability. In a parallel contribution, “Trajectory Tracking Using Frenet Coordinates with Deep Deterministic Policy Gradient” (2024, 2 citations), Jin proposed a novel control method that integrates the DDPG algorithm with Frenet coordinate systems. By transforming vehicle state information from Cartesian to Frenet coordinates, this work achieves more precise and adaptive trajectory tracking. Jin’s research demonstrates a clear trajectory of innovation, merging reinforcement learning with geometric control to push the boundaries of autonomous navigation.
Research Focus
Key Achievements
Top Papers
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