Run-kun Wang
Papers
1
Total Citations
10
H-Index
1
About
Run-kun Wang is a researcher specializing in intelligent manufacturing and robotic path optimization, with a particular focus on welding automation. His most-cited work, "Hybrid Global Optimum Beetle Antennae Search - Genetic Algorithm Based Welding Robot Path Planning" (2019, 10 citations), introduces a novel hybrid algorithm that integrates the Beetle Antennae Search (BAS) with a Genetic Algorithm (GA) to solve complex path planning challenges in body-in-white spot welding. By embedding GA into each iteration of BAS, Wang significantly improves global search capability and convergence efficiency, directly enhancing welding productivity and reducing cycle times. This contribution addresses a critical bottleneck in automotive manufacturing—coordinating multi-robot welding tasks to minimize idle time and energy consumption. Wang’s work exemplifies the practical application of bio-inspired computing to industrial robotics, offering a scalable solution for high-volume production environments. His research not only advances algorithmic theory but also provides tangible tools for engineers seeking to optimize robotic workflows. With growing interest in smart factories, Wang’s hybrid optimization approach stands as a valuable reference for researchers and practitioners in robotics, manufacturing, and computational intelligence.
Research Focus
Key Achievements
Top Papers
- 1