Titon Barua
Papers
1
Total Citations
4
H-Index
1
About
Titon Barua is a researcher at the forefront of autonomous underwater robotics, specializing in edge-deployed machine learning for real-time environmental perception. His primary research areas include underwater cave exploration, autonomous vehicle navigation, and efficient deep learning models for resource-constrained platforms. Barua’s most notable contribution is his work on caveline detection at the edge, where he investigates how to deploy object detection and segmentation models directly onto Autonomous Underwater Vehicles (AUVs). This enables real-time mapping and navigation in the challenging, unstructured environments of underwater caves—a task critical for scientific discovery and infrastructure inspection. His 2023 paper on this topic has already garnered 4 citations, signaling early impact in a niche but vital field. By bridging the gap between advanced machine learning and practical edge computing, Barua is helping to make AUVs more autonomous and responsive, reducing their reliance on human operators or cloud connectivity. His work holds promise for advancing marine archaeology, environmental monitoring, and underwater search-and-rescue operations, marking him as an emerging leader in robotic perception.
Research Focus
Key Achievements
Top Papers
- 1