Mohammad Abdul Qayum
Papers
2
Total Citations
6
H-Index
2
About
Mohammad Abdul Qayum’s research lies at the intersection of robotics, artificial intelligence, and human-robot collaboration, with a focus on creating intelligent, cooperative systems for industrial and creative applications. His work explores how mobile robots can perceive their environment—detecting object shape, color, and size—and use machine learning to coordinate tasks autonomously. In his most-cited paper (2023, 4 citations), Qayum demonstrates how cooperative robots can perform complex industrial tasks by integrating sensory data with decision-making algorithms. Earlier, in a 2017 study (2 citations), he introduced an interactive system where agents employ Principal Component Analysis (PCA) to recognize patterns and plan creative, cooperative actions. Though his citation counts are modest, Qayum’s contributions are notable for their emphasis on real-world experimentation and the fusion of perception, learning, and teamwork in robotics. His work offers a practical foundation for developing robots that can adapt to dynamic environments and collaborate with humans, making it a valuable resource for students and researchers interested in embodied AI and multi-agent systems.
Research Focus
Key Achievements
Top Papers
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- 2