Papers
2
Total Citations
5
H-Index
2
About
Jialong Dai is a researcher advancing the frontiers of intelligent manufacturing and robotics, with a focused expertise in multi-robot coordination and deep learning for aerospace applications. His work addresses critical challenges in automating the assembly of large-scale aerospace structures, where precision, task allocation, and interference avoidance are paramount. In his highly cited 2022 paper, Dai introduced a novel multi-robot cooperative assembly task planning framework that optimizes flexibility and efficiency for complex aerospace components, effectively mitigating issues of imbalanced task allocation and robot interference. This contribution has garnered 3 citations, establishing a foundation for scalable automation in high-stakes manufacturing environments. More recently, in 2025, Dai developed a benchmark feature detection method integrating deep learning into mobile robot automatic drilling systems, tackling the persistent problem of material interference and background noise that compromises system accuracy. With 2 citations, this work underscores his commitment to bridging simulation and real-world deployment. Dai’s research is pivotal for next-generation aerospace manufacturing, where his innovations promise to enhance reliability and throughput in robotic systems.
Research Focus
Key Achievements
Top Papers
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- 2