Mosam Dabhi
Papers
2
Total Citations
40
H-Index
2
About
Mosam Dabhi is a roboticist whose research centers on autonomous exploration, perceptual modeling, and human-robot interaction. His most impactful contribution is the development of a real-time information-theoretic exploration framework using Gaussian Mixture Model (GMM) maps, published in 2019 and cited 36 times. This work addresses a critical challenge in robotics: enabling high-fidelity perceptual modeling while maintaining communication efficiency in bandwidth-constrained environments. By exploiting the compactness of GMM distributions, Dabhi’s approach allows robots to share rich environmental information without overwhelming network resources, advancing the state of the art in autonomous exploration for applications like search-and-rescue and planetary rovers. Earlier in his career, Dabhi demonstrated versatility with a 2015 paper on wireless network-controlled robots, integrating web-based interfaces, Android apps, and hand gesture control for intuitive teleoperation. This work, though less cited, showcases his interest in accessible human-robot interaction systems. Dabhi’s research bridges theoretical information theory with practical robotic systems, offering solutions that balance perceptual accuracy with real-world constraints. His work continues to influence researchers developing autonomous systems for communication-limited scenarios.
Research Focus
Key Achievements
Top Papers
- 1Real-Time Information-Theoretic Exploration with Gaussian Mixture Model Maps36 citations · 2019
- 2