Manoj Velmurugan

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About

Manoj Velmurugan is a rising researcher at the intersection of tiny machine learning and autonomous aerial robotics, with a focus on enabling real-time perception on resource-constrained platforms. His most cited work, *EdgeFlowNet*, tackles the critical challenge of dense optical flow estimation for tiny mobile robots, achieving an impressive 100 FPS at just 1 Watt—a breakthrough that makes safe, accurate navigation and obstacle avoidance feasible on platforms with severe computational and power limitations. This paper has garnered 5 citations, signaling early impact in the embedded robotics community. Velmurugan is also the lead developer of *VizFlyt*, an open-source, perception-centric pedagogical framework for autonomous aerial robots. Designed to train the next-generation workforce, VizFlyt provides a reliable testbed that bridges the gap between theory and hands-on practice in aerial robotics courses. By prioritizing perception in its curriculum, the framework equips students with practical skills for real-world drone applications. Together, Velmurugan’s work advances both the state-of-the-art in efficient on-robot vision and the educational tools needed to cultivate future talent in autonomous systems.