Shao-Kang Lin
Papers
2
Total Citations
30
H-Index
2
About
Shao-Kang Lin is a leading researcher in swarm robotics, cyber-physical systems (CPSs), and bio-inspired intelligent control. His work focuses on developing adaptive, collision-free coordination strategies for networked mobile robots, bridging the gap between artificial intelligence and real-world robotic autonomy. In his highly cited 2019 paper, Lin introduced a collision-free fuzzy formation control framework for swarm robotic CPSs, leveraging a robust orthogonal firefly algorithm (OFA) that fuses the Taguchi method with ranking mutation—a hybrid AI approach that significantly enhances formation stability and safety. This work has garnered 21 citations, underscoring its influence in swarm intelligence and autonomous systems. Lin further advanced the field with his biologically-inspired self-evolving control method for networked mobile robots, integrating a Kalman filter with a self-learning radial basis function neural network (KF-RBFNN) to enable real-time adaptation and learning. With 9 citations, this contribution highlights his commitment to merging neural adaptation with distributed control. Lin’s research is pivotal for students and engineers exploring resilient, intelligent coordination in multi-robot systems, offering foundational algorithms for next-generation autonomous swarms in dynamic, uncertain environments.
Research Focus
Key Achievements
Top Papers
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- 2