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About

Khalid Al Mutib is a robotics researcher whose career spans over two decades, with a sustained focus on autonomous mobile robot navigation, localization, and intelligent control systems. His work sits at the intersection of artificial intelligence and robotics, exploring how techniques such as neuro-fuzzy logic, evolutionary learning, and neural networks can enable robots to operate effectively in unstructured and dynamic environments. One of his notable contributions is the development of a neuro-fuzzy navigation strategy leveraging Transputer computation architecture, demonstrating an early commitment to real-time, sensor-driven robot control. His research on RFID-based localization using backpropagation artificial neural networks (BPANN) highlights his interest in practical, scalable positioning solutions for mobile platforms. Al Mutib has also advanced stereo vision-based mapping and path planning through the KSU-IMR robotic testbed, reflecting a systems-level approach to robotics research. His self-learning navigation framework using evolutionary learning addresses one of the field's most persistent challenges — adaptive autonomy in unknown environments. With publications accumulating citations across multiple venues, Al Mutib has built a meaningful body of work that continues to inform researchers tackling intelligent autonomous systems design.