Dina Karasneh
Papers
1
Total Citations
3
H-Index
1
About
Dina Karasneh is a leading researcher in robotics and artificial intelligence, with a primary focus on surface classification and terrain adaptation for autonomous ground vehicles. Her most impactful work introduces innovative deep learning architectures that leverage low-cost Inertial Measurement Unit (IMU) data—rather than expensive computer vision systems—to classify surface types in real time. By developing cascaded and parallel deep learning fusion models, Karasneh has demonstrated that robots can achieve enhanced mobility, stability, and adaptability across diverse environments using only internal sensor time-series data. Her 2025 paper on this topic has already garnered 3 citations, signaling growing recognition of its practical value in field robotics. This work is particularly notable for its cost-effectiveness and robustness, offering a scalable solution for robots operating in low-visibility or resource-constrained settings. Karasneh’s contributions bridge the gap between sensor fusion and deep learning, providing a foundation for future research in autonomous navigation and terrain-aware control systems. Her innovative approach positions her as a rising voice in the intersection of robotics, embedded systems, and intelligent control.
Research Focus
Key Achievements
Top Papers
- 1