Papers
5
Total Citations
27
H-Index
3
About
John Karigiannis is a leading researcher at the intersection of industrial robotics, non-destructive testing (NDT), and artificial intelligence. His work primarily focuses on automating complex manufacturing processes through multi-agent systems, reinforcement learning, and deep neural networks. Karigiannis’s most impactful contribution is the development of a multi-robot system for automated fluorescent penetrant indication inspection (FPI), a critical NDT method in aerospace. By integrating deep neural nets, his 2021 paper (12 citations) addresses the long-standing challenge of automating manual visual inspection, significantly improving defect detection reliability. He has also pioneered reinforcement learning for self-homing of industrial robotic manipulators (2022, 8 citations), enabling collision-free autonomous return to home positions—a key safety and efficiency breakthrough in manufacturing cells. Earlier foundational work on fuzzy rule-based neuro-dynamic programming and hierarchical multi-agent architectures (2010, 3 citations; 2008, 2 citations) established frameworks for developmental robot skill acquisition, mimicking biological self-organization. Karigiannis’s research bridges theoretical AI with practical industrial automation, earning recognition for advancing both the intelligence and autonomy of robotic systems in high-stakes manufacturing environments.
Research Focus
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