Abdel‐Nasser Sharkawy
Papers
25
Total Citations
579
H-Index
12
About
Abdel-Nasser Sharkawy is a prominent robotics researcher whose work sits at the intersection of artificial intelligence and human-robot interaction, with particular expertise in neural network applications for collaborative robotics systems. His research has made significant strides in two interconnected domains: collision detection for safe human-robot interaction and adaptive admittance control for physical human-robot cooperation. Sharkawy's most impactful contribution lies in developing neural network-based frameworks for detecting human-robot collisions using only intrinsic joint position sensors — eliminating the need for costly external sensing equipment. His foundational 2016 paper on collision detection, followed by increasingly sophisticated approaches through 2021, has collectively garnered over 275 citations, demonstrating the field's strong uptake of his methods. His 2019 multi-input-output neural network paper alone has attracted 91 citations, reflecting its practical significance. Beyond safety, Sharkawy has advanced variable admittance control, designing neural networks that dynamically adjust virtual damping and inertia parameters in real time, enabling more intuitive and responsive human-robot collaboration. His widely-cited 2022 survey on human-robot interaction further cements his role as a synthesizer of knowledge in this rapidly evolving field. His body of work provides both theoretical foundations and practical tools that continue to shape safer, smarter collaborative robotics.
Research Focus
Key Achievements
Top Papers
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- 4Human-Robot Collision Detection Based on Neural Networks53 citations · 2016
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- 8Neural networks design and training for safe human-robot cooperation30 citations · 2021
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