Abduljabbar Khudhur Abduljabbar

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About

Abduljabbar Khudhur Abduljabbar is a researcher at the forefront of intelligent robotics and autonomous navigation, specializing in the integration of computer vision and reinforcement learning for mobile robot control. His work focuses on developing robust path planning and behavior-based systems that enable robots to operate effectively in dynamic, complex environments. Notably, his highly cited 2024 paper, "Q-Learning for Path Planning in Complex Environments: A YOLO and Vision-Based Approach," demonstrates a novel fusion of deep reinforcement learning with YOLO-based object detection to compute optimal, obstacle-free trajectories in real time. This work, garnering 8 citations, has significant implications for autonomous vehicles and service robotics. In his 2023 study on "High-Performance of Mobile Robot Behavior Based on Intelligent System," Abduljabbar pioneered a leader-follower framework that combines fuzzy logic control with YOLO convolutional neural networks, enabling precise formation-keeping and goal-reaching behaviors using only a single overhead camera. With a growing citation record, his contributions are shaping the next generation of adaptive, vision-guided robotic systems, making him a rising authority in intelligent automation and sensor-based robot control.