Swapnoneel Roy
Papers
7
Total Citations
85
H-Index
4
About
Swapnoneel Roy is pioneering the intersection of multi-robot systems, artificial intelligence, and cybersecurity for autonomous information gathering. His research focuses on deploying coordinated robot teams for high-impact environmental monitoring tasks, particularly in precision agriculture, gas distribution mapping, and disaster response. Roy’s major contributions include developing deep mean field reinforcement learning frameworks that enable scalable, decentralized multi-robot sampling, and introducing novel blockchain-based architectures to secure robot-to-robot communication against data integrity threats—a critical but previously unaddressed challenge in the field. His work on secure multi-robot adaptive information sampling, with both periodic and opportunistic connectivity, has laid the foundation for trustworthy autonomous field operations. With over 85 total citations, his most influential paper, “Multi-Robot Information Gathering for Precision Agriculture” (54 citations), provides a comprehensive roadmap for the field. Roy has also advanced gas source localization using CNN-LSTM deep recurrent Q-learning and gas distribution mapping via deep reinforcement learning combined with Gaussian process regression. His research uniquely bridges theoretical rigor with practical experimentation, addressing real-world constraints like energy efficiency and untrusted devices, making him a leading voice in secure, intelligent multi-robot coordination.
Research Focus
Key Achievements
Top Papers
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- 3Secure Multi-Robot Adaptive Information Sampling7 citations · 2021
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