Nora Karkar
Papers
3
Total Citations
7
H-Index
2
About
Nora Karkar is a robotics researcher advancing the frontiers of autonomous navigation for mobile robots and automated guided vehicles (AGVs). Her work focuses on developing intelligent, cost-effective solutions for path planning, obstacle detection, and trajectory control. Karkar’s most cited paper introduces a hybrid path-planning algorithm that synergizes the A-Star graph search with artificial potential field methods, enabling robots to compute optimal global routes while dynamically avoiding local obstacles. She further extends this work with a real-time obstacle detection system using YOLOv8 and RGB-D sensors, demonstrating that high-performance navigation can be achieved with affordable hardware like the Microsoft Kinect V1. Her research also addresses the critical challenge of trajectory tracking through a fuzzy dynamic feedback linearization approach, overcoming the computational and model-dependency limitations of traditional methods. With over 7 citations across her recent publications (2024–2025), Karkar is establishing herself as an emerging voice in practical, deployable robotics. Her contributions are particularly relevant for warehouse automation, service robotics, and any domain requiring robust, real-time navigation in dynamic environments.
Research Focus
Key Achievements
Top Papers
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