Zahid Hasan

North South University, University of Maryland, Baltimore County

Papers

Total Citations

H-Index

About

Zahid Hasan is a robotics and artificial intelligence researcher whose work spans autonomous navigation, multi-agent systems, and computer vision. His most cited paper, "Autonomous Warehouse Robot using Deep Q-Learning" (2021, 12 citations), introduces a reinforcement learning framework that enables warehouse robots to navigate dynamic environments, avoid obstacles, and optimize spatial utilization—addressing critical challenges in industrial automation. Hasan also contributed to distributed heterogeneous control of mini-flying machines and ground robots (2009, 3 citations), pioneering coordination strategies for mixed aerial-ground robotic teams. More recently, his work on "An Online Continuous Semantic Segmentation Framework With Minimal Labeling Efforts" (2023, 2 citations) tackles the persistent problem of dataset imbalance in semantic segmentation, proposing an adaptive pseudo-labeling method that reduces annotation burdens while improving model robustness. Though early in his career, Hasan’s research demonstrates a clear trajectory toward scalable, intelligent systems that operate under real-world constraints—from warehouse logistics to multi-robot collaboration. His integration of deep reinforcement learning with practical robotics applications positions him as an emerging voice in autonomous systems, with potential for significant future impact in both industrial and academic settings.