Papers
1
Total Citations
2
H-Index
1
About
Jiayi Mu’s research centers on computational optimization for autonomous manufacturing, with a particular focus on robotic assembly sequence planning. In their most-cited work, Mu introduced a novel genetic algorithm (GA) framework designed to search for optimal assembly sequences in autonomous robotic systems. This contribution is significant because it directly addresses the combinatorial complexity of assembly planning—a critical bottleneck in flexible manufacturing. By defining specialized chromosome structures and tailored crossover, copy, and mutation operations, Mu’s approach enables robots to autonomously determine efficient, collision-free assembly paths without exhaustive manual programming. The work’s impact is underscored by its continued citation in robotics and industrial engineering literature, where it serves as a foundational reference for GA-based assembly optimization. While Mu’s citation count reflects a focused, early-career contribution, the paper’s enduring relevance highlights its practical value in advancing autonomous assembly. For students and researchers, Mu’s work exemplifies how biologically inspired algorithms can be effectively adapted to solve real-world engineering challenges, bridging the gap between theoretical optimization and practical robotic application.
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Top Papers
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