Ashraf Elnagar

University of Sharjah, Muscat College, University of Alberta

Papers

24

Total Citations

361

H-Index

9

About

Ashraf Elnagar is a leading figure in autonomous robotics and motion planning, whose work has fundamentally advanced how robots perceive and navigate dynamic environments. His research centers on predictive modeling, trajectory optimization, and intelligent path planning, with a particular focus on enabling robots to operate safely among moving obstacles. Elnagar’s most influential contribution is his pioneering framework for motion prediction, detailed in his highly cited 1998 paper (73 citations), which uses autoregressive models to forecast the future positions and orientations of moving objects without imposing motion constraints. He further refined this approach using Kalman filtering (52 citations), establishing a robust foundation for real-time obstacle avoidance. His innovative work also includes a novel application of Maxwell’s equations to eliminate the local minima problem in artificial potential fields (22 citations) and an art gallery-based algorithm for optimal guard placement in global environments (17 citations). More recently, Elnagar has contributed to swarm intelligence, co-authoring a comprehensive 2022 review on bat-inspired algorithms (37 citations). With a career spanning over two decades, his research has consistently pushed the boundaries of autonomous navigation, making him a key reference for students and engineers developing intelligent robotic systems.

Research Focus

Key Achievements

9
H-Index
24
Papers
361
Total Citations
15
Avg Citations/Paper
🏆 Most Cited Paper
Motion prediction of moving objects based on autoregressive model
73 citations · 1998
📈 Most Prolific Year: 2002 (5 Papers)
🤝 Key Collaborators: 15
🏛 Institutions: University of Sharjah, Muscat College, University of Alberta

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago