Papers
2
Total Citations
60
H-Index
2
About
An Wan is a leading researcher in advanced robotics, specializing in the optimal path planning, measurement, and control of assembly robots for complex industrial applications. His primary contributions address the formidable challenge of automating the assembly of large-scale, heavy-weight components—such as aircraft fuselage sections—where traditional methods fail due to part deformation and difficulty in measurement. Wan’s seminal work, *“Optimal Path Planning and Control of Assembly Robots for Hard-Measuring Easy-Deformation Assemblies”* (2017), with 56 citations, provides a foundational framework for integrating real-time sensing with adaptive control to handle these non-rigid structures. His earlier research (2015) further pioneered learning-based approaches to optimize both measurement and control simultaneously, significantly enhancing robot precision and flexibility. By tackling these high-stakes, real-world problems in aerospace and automotive manufacturing, Wan’s work has directly influenced the development of more intelligent, autonomous assembly systems. His research stands out for its practical impact, bridging the gap between theoretical robotics and the stringent demands of heavy industry, and is essential reading for engineers and researchers working on next-generation manufacturing automation.
Research Focus
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Top Papers
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