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About

A. N. Amudhan is a researcher specializing in computer vision, robotics, and real-time embedded systems, with a particular focus on object detection and trajectory tracking. Their most impactful work, "RFSOD: a lightweight single-stage detector for real-time embedded applications to detect small-size objects" (2021, 17 citations), introduces an efficient deep learning model designed for resource-constrained platforms, enabling accurate small-object detection in real-time scenarios—a critical advancement for autonomous systems and edge computing. Amudhan has also made significant contributions to mobile robotics, developing controllers for omnidirectional robots using system identification and fuzzy logic with visual feedback (8 and 6 citations, respectively), enhancing trajectory tracking performance for applications in healthcare, industry, and agriculture. Additionally, their innovative work on shuttlecock detection and fall point prediction using neural networks (6 citations) demonstrates a unique application of predictive algorithms in sports analytics. With a growing citation record, Amudhan’s research bridges the gap between lightweight AI architectures and practical robotic control, offering scalable solutions for real-world deployment. Their work is particularly valuable for students and engineers seeking to implement efficient vision-based systems in embedded and robotic platforms.