Xuehan Zheng
Papers
5
Total Citations
46
H-Index
4
About
Xuehan Zheng is a rising researcher in the fields of autonomous robotics, reinforcement learning, and biomimetic underwater systems. Their work focuses on solving critical challenges in mobile robot path planning and control, particularly for complex and dynamic environments. Zheng’s major contributions include developing an improved Soft Actor-Critic algorithm for mobile robot path planning (20 citations), which enhances navigation efficiency in obstacle-laden settings. They have also pioneered the use of Unreal Engine and AirSim to create realistic simulation platforms for biomimetic robotic fish, enabling advanced trajectory tracking and control studies (11 citations). Further, Zheng has advanced underwater robotics by proposing a Nonlinear Model Predictive Control algorithm for trajectory tracking and obstacle avoidance in robotic fish (8 citations), and by investigating how counterflow regions affect hydrodynamic performance (4 citations). Their work also extends to underwater perception, with a novel YOLOv5-based hybrid detection algorithm (3 citations) that improves target recognition in aquatic environments. With a growing citation impact and a focus on bridging simulation and real-world deployment, Zheng is establishing a reputation for innovative, cross-disciplinary solutions that push the boundaries of autonomous and bio-inspired robotics.
Research Focus
Key Achievements
Top Papers
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