S. Poongodi

Papers

Total Citations

H-Index

About

S. Poongodi is a leading researcher at the intersection of robotics, cybersecurity, and artificial intelligence, with a particular focus on securing real-time autonomous systems. Her most-cited work, "Robotics in Real-Time Applications Using Bayesian Hyper-Tuned Artificial Neural Network" (2023), addresses a critical and underexplored vulnerability: the increasing cybersecurity threats to robotic systems, specifically targeting Real-Time Location Systems (RtLSs). Poongodi demonstrates that as mobile robots rely on RtLSs for navigation and safe operation, these systems have become prime attack vectors—a phenomenon she argues has been dangerously overlooked. By introducing a Bayesian hyper-tuned artificial neural network, she provides a novel, adaptive defense mechanism that enhances the resilience of robotic systems against such threats. With 2 citations already, this foundational paper is gaining traction among researchers in robotics security and AI-driven anomaly detection. Poongodi’s work is essential for students and engineers developing next-generation autonomous systems, as it bridges the gap between real-time operational safety and robust cybersecurity, ensuring that robots can navigate not only physical spaces but also hostile digital environments.