Shalabh Gupta
Papers
11
Total Citations
187
H-Index
9
About
Shalabh Gupta is a prominent robotics and autonomous systems researcher whose work spans intelligent sensing, underwater robotics, and human-robot interaction. His research has made significant contributions to autonomous underwater vehicle (AUV) technology, coverage path planning, and machine learning-based pattern recognition. Gupta's early foundational work on wavelet-based feature extraction using probabilistic finite state automata (2010, 55 citations) established his expertise in intelligent signal processing. He then pivoted toward autonomous marine systems, developing algorithms for adaptive oil spill cleanup (2013, 30 citations) and cooperative multi-agent exploration in unknown underwater environments (2012, 13 citations). His 3D terrain reconstruction frameworks for AUVs, integrating sonar, DVL, and IMU sensors, have advanced the field of autonomous seabed mapping (2016–2017). More recently, Gupta has pioneered underwater human-robot interaction, developing deep learning-based diver gesture recognition systems that eliminate costly waterproof hardware, progressing from CNN-based approaches (2019, 17 citations) to his sophisticated DARE encoder architecture (2023). His energy-constrained coverage path planning algorithm, ε⁺ (2020), further demonstrates his ability to address real-world operational challenges. Across his career, Gupta has consistently bridged theoretical innovation with practical marine robotics applications.
Research Focus
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