Papers
2
Total Citations
5
H-Index
2
About
P. Kesaba is a researcher focused on advancing intelligent control and kinematic modeling for industrial robotics, with particular emphasis on soft computing techniques. Their work bridges the gap between traditional robotic motion analysis and modern machine learning approaches, aiming to improve the efficiency and accuracy of robotic systems. Kesaba’s major contributions include pioneering the application of transfer learning (TL) to forward kinematic estimation, as demonstrated in their 2022 study on a 6-DOF robot. By integrating TL with artificial neural networks, they showed that training time can be significantly reduced while minimizing dependency on large target-domain datasets—a novel approach for robotic prediction tasks. Earlier, Kesaba explored fuzzy logic systems for predicting the position analysis of industrial robots (2015), laying groundwork for adaptive, rule-based control. Although their citation counts are currently modest (3 and 2 citations respectively), these works represent foundational steps in applying transfer learning to robotics, a rapidly growing field. Their research offers practical pathways for more agile and data-efficient robotic systems, making it relevant for students and engineers seeking to integrate AI with mechanical automation.
Research Focus
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Top Papers
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