Md Moniruzzaman
Papers
3
Total Citations
173
H-Index
3
About
Md Moniruzzaman is a researcher focused on advancing robotic teleoperation, particularly for mobile and ground vehicles operating under high-latency conditions. His work lies at the intersection of robotics, computer vision, and deep learning, where he addresses the critical challenge of latency-induced performance degradation in remote operation. His most impactful contribution is the comprehensive survey "Teleoperation methods and enhancement techniques for mobile robots: A comprehensive survey" (2021), which has garnered 162 citations and serves as a foundational reference for researchers in the field. Moniruzzaman has pioneered the use of deep learning for teleoperation enhancement, notably proposing a novel approach for long future frame prediction using optical flow-informed neural networks (2022, 8 citations). He further advanced this concept with structure-aware image translation-based prediction (2023, 3 citations), which improves image quality for high-speed ground vehicle teleoperation. By introducing synthetic frame generation to mitigate latency effects, Moniruzzaman has opened new avenues for real-time remote control. His work is particularly notable for applying deep learning to a domain where it had not previously been used, demonstrating significant potential for enhancing operator performance in challenging environments.
Research Focus
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Top Papers
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