Jiuming Guo
Papers
4
Total Citations
60
H-Index
3
About
Jiuming Guo is a leading researcher in robotic manipulation, with a primary focus on the automated assembly of deformable linear objects—a critical challenge in aerospace manufacturing. His work addresses the fundamental difficulty of enabling robots to handle flexible, complex structures like wire harnesses, which are essential in aircraft assembly but notoriously difficult to perceive and manipulate due to their small scale and intricate branching. Guo’s most influential contribution is a novel algorithm that combines bidirectional searching with geometric constrained sampling, achieving automatic manipulation planning for aircraft cable assembly (37 citations). He has further advanced the field by developing a visual recognition method that uses sequential segmentation and probabilistic estimation to identify multi-branch wire harnesses, solving a key perception bottleneck (8 citations). To handle real-world uncertainties, Guo introduced a local path replanning algorithm for deformable objects that adapts to collisions caused by model deviations (13 citations). His recent work explores model-driven reinforcement learning to simplify complex, asymmetric assembly tasks, reducing reliance on traditional contact state analysis. Through these innovations, Guo is systematically bridging the gap between theoretical robotics and practical, high-stakes industrial applications.
Research Focus
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Top Papers
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