Mohammad Hassan Ranjbar

Papers

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About

Mohammad Hassan Ranjbar is a researcher at the forefront of intelligent robotics and autonomous systems, with a core focus on integrating computer vision, machine learning, and path planning. His most-cited work, "Computer Vision-Based Path Planning for Robot Arms in Three-Dimensional Workspaces Using Q-Learning and Neural Networks" (2022), has garnered 38 citations, highlighting its significance in addressing critical limitations in 3D object localization and computational efficiency for robotic manipulation. Ranjbar’s major contribution lies in developing hybrid frameworks that combine reinforcement learning—specifically Q-learning—with neural networks to enable real-time, adaptive path planning in complex 3D environments. This approach overcomes the unreliability of traditional methods, offering smarter, more responsive solutions for applications ranging from industrial automation to assistive robotics. His work is notable for bridging the gap between theoretical AI algorithms and practical robotic control, making autonomous systems more robust and efficient. As a rising voice in the field, Ranjbar’s research continues to shape the future of intelligent robotics, promising safer and more precise human-robot collaboration.