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About

Ayman Alharbi is a rising researcher at the forefront of intelligent autonomous systems and artificial intelligence, whose work is already garnering significant attention. His primary research areas encompass reinforcement learning for robotics, intelligent process automation, and advanced neurocomputing for environmental monitoring. Alharbi’s major contributions include developing an improved Deep Q-Learning approach for navigating autonomous UAVs through complex 3D obstacle-cluttered environments, a critical advancement for modern drone mission planning. He has also conducted a systematic literature review on Intelligent Process Automation, exploring how AI and robotic process automation can modernize business operations and create human assistants. In the domain of underwater robotics, Alharbi has pioneered an intelligent Bayesian regularization backpropagation neurocomputing paradigm for accurately estimating state features of passive objects, with applications in surveillance and environmental monitoring. His most-cited works, including papers from 2024 and 2025, have each accumulated 10 citations, demonstrating early impact in his field. Alharbi’s innovative integration of reinforcement learning and neurocomputing positions him as a promising contributor to the next generation of autonomous, intelligent systems.