Papers
4
Total Citations
24
H-Index
3
About
Debashri Roy is a leading researcher at the intersection of wireless networking, edge computing, and autonomous robotics. Her work focuses on enabling reliable, low-latency connectivity for robots in dynamic environments, particularly factory floors and unstructured outdoor settings. Roy’s major contributions include the development of L-NORM, a framework that integrates machine learning with network orchestration to ensure seamless robot mobility and connectivity, and RagNAR, a ray-tracing-based navigation system that enhances autonomous navigation while addressing critical privacy concerns. Her research has garnered attention, with her most-cited paper, "L-NORM," accumulating 10 citations, and her testbed designs for robot navigation through differential ray tracing receiving 7 citations. Roy’s work is notable for its practical impact, bridging the gap between theoretical advances and real-world deployment in manufacturing and service industries. Her innovative approaches to privacy-aware navigation and edge-based learning are shaping the future of autonomous systems, making her a key figure in the field of robotic connectivity and navigation.
Research Focus
Key Achievements
Top Papers
- 1
- 2Testbed Design for Robot Navigation through Differential Ray Tracing7 citations · 2024
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