Mohamed Abdelkader | AI-Based Robot Perception | Research Excellence Award

Research Excellence Award

Mohamed Abdelkader
Prince Sultan University

Mohamed Abdelkader
Affiliation Prince Sultan University
Country Saudi Arabia
Scopus ID 57197062034
Documents 35
Citations 575
h-index 11
Subject Area AI-Based Robot Perception
Event International Robotics and Automation Awards
ORCID 0000-0002-0518-852X

Mohamed Abdelkader, affiliated with Prince Sultan University, is a researcher whose work focuses on AI-based robot perception, autonomous systems, computer vision, and intelligent robotic navigation. His scholarly contributions demonstrate the integration of artificial intelligence with robotic sensing technologies to improve perception, localization, and autonomous decision-making. His research output has attracted significant academic attention through publications indexed in Scopus and citations across robotics and artificial intelligence literature.[1]

Abstract

Mohamed Abdelkader has established an active research profile in AI-based robot perception through investigations involving autonomous navigation, computer vision, robotic sensing, and intelligent decision-making. His publications contribute to improving robotic awareness of dynamic environments by integrating perception algorithms with artificial intelligence techniques. These studies support advancements in autonomous systems capable of operating safely and efficiently across diverse applications. His scholarly productivity, citation record, and interdisciplinary collaborations demonstrate sustained contributions to robotics research while promoting practical innovation, scientific dissemination, and continued academic development within intelligent robotic technologies.[1][2]

Keywords

AI-Based Robot Perception, Computer Vision, Autonomous Robotics, Robotic Navigation, Machine Learning, Artificial Intelligence, Intelligent Systems, Mobile Robots, Sensor Fusion, Autonomous Vehicles.

Introduction

Artificial intelligence has significantly transformed robotic perception by enabling machines to interpret complex environments through visual and sensor-based information. Mohamed Abdelkader’s research reflects these developments by investigating perception frameworks that improve robotic autonomy, environmental understanding, and adaptive navigation while supporting reliable performance across intelligent robotic applications.[2]

Research Profile

Working at Prince Sultan University, Mohamed Abdelkader has developed a research portfolio centered on autonomous robotics, perception systems, and artificial intelligence. His publications emphasize practical solutions that combine computer vision, localization, mapping, and machine learning techniques to enhance robotic awareness and decision-making in real-world environments while encouraging interdisciplinary collaboration.[1]

Research Contributions

His research contributions include improving robotic perception algorithms, integrating intelligent sensing technologies, and advancing autonomous navigation capabilities. These studies support greater operational efficiency for robotic platforms while expanding the application of artificial intelligence in perception-driven systems used across industrial, academic, and emerging autonomous environments.[3]

Publications

The researcher’s publication record includes peer-reviewed articles addressing robot perception, intelligent navigation, machine learning, and computer vision. These publications have contributed to the growing body of literature in robotics by presenting methodologies that improve environmental understanding and autonomous performance while maintaining scientific rigor and reproducibility.[2]

Research Impact

With 35 indexed publications, 575 citations, and an h-index of 11, Mohamed Abdelkader has achieved measurable scholarly visibility within robotics and artificial intelligence research. These metrics indicate continued academic engagement and demonstrate that his research findings have influenced related investigations in autonomous robotic perception and intelligent systems.[1]

Award Suitability

The Research Excellence Award recognizes researchers who demonstrate sustained scholarly productivity, impactful publications, and meaningful scientific contributions. Mohamed Abdelkader’s achievements in AI-based robot perception, supported by recognized research metrics and interdisciplinary innovation, align with the objectives of honoring excellence in robotics and automation research.[4]

Conclusion

Mohamed Abdelkader’s research reflects consistent advancement in AI-driven robotic perception through scientifically validated investigations and collaborative innovation. His academic record demonstrates meaningful contributions to autonomous robotics while supporting future developments in intelligent perception technologies and practical robotic applications across multiple research domains.[1][4]

External Links

References

  1. Elsevier. (n.d.). Scopus author details: Mohamed Abdelkader, Author ID 57197062034. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57197062034
  2. Google Scholar. (n.d.). Scholar profile of Mohamed Abdelkader.
    https://scholar.google.com/citations?user=hk5GW30AAAAJ&hl=en&oi=sra
  3. MDPI. (2022). OCTUNE: Optimal Control Tuning Using Real-Time Data with Algorithm and Experimental Results.
    https://doi.org/10.3390/s22239240
  4. International Robotics and Automation Awards. (2026). Research Excellence Award.
    https://roboticsandautomation.org/

Assoc. Prof. Dr. Mingfeng Lu | Computer Vision | Research Excellence Award

Assoc. Prof. Dr. Mingfeng Lu | Computer Vision | Research Excellence Award

Beijing Institute of Technology | China

Assoc. Prof. Dr. Mingfeng Lu is a Senior Laboratory Technician and Associate Professor at the School of Integrated Circuit and Electronics, Beijing Institute of Technology. He earned his BS and MS degrees in electronics engineering and circuits and systems from BIT, and a PhD in optical engineering. His research focuses on optical metrology, monocular visual measurement, and the application of modern signal processing and artificial intelligence techniques, including fractional Fourier transform, chirp Fourier transform, computer vision and deep learning. He has published extensively in SCI-indexed journals, holds multiple patents, and serves as a core member and co-principal investigator on national and municipal research projects.

Citation Metrics (Scopus)

240
180
120
60
0

Citations
225

Documents
58

h-index
8

Citations

Documents

h-index


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