Yuhang Gai
Papers
6
Total Citations
91
H-Index
5
About
Yuhang Gai is a leading researcher in robotic assembly and manipulation, with a focus on solving complex, real-world challenges in aircraft manufacturing. His work centers on three key areas: automatic manipulation planning, high-precision assembly control, and the visual recognition of deformable objects. Gai’s most impactful contribution is his 2020 algorithm for bidirectional searching with geometric constrained sampling, which has garnered 37 citations and revolutionized automatic cable assembly in aircraft by enabling robots to navigate tight, cluttered spaces. He has further advanced the field through piecewise decoupling control for peg-in-hole assembly (20 citations) and a local replanning algorithm that adapts to deformable linear objects (13 citations). In 2024, Gai introduced a local connection reinforcement learning method for efficient peg-in-hole assembly (11 citations), pushing toward more adaptive robotic systems. His work on visual recognition of multi-branch wire harnesses—using sequential segmentation and probabilistic estimation—addresses a critical bottleneck in aircraft assembly, where small, complex structures are notoriously difficult for robots to perceive. With a growing citation record and a clear trajectory toward integrating learning-based methods with traditional control, Gai is shaping the future of autonomous manufacturing.
Research Focus
Key Achievements
Top Papers
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