Azhar Kadhim Farhood

Southern Federal University, Southern Technical University

Papers

Total Citations

H-Index

About

Azhar Kadhim Farhood is a researcher advancing the frontiers of intelligent robotics and autonomous navigation. His primary contributions lie in developing neural network-based control systems for multi-robot coordination and mobile robot path planning in uncertain, dynamic environments. His most influential work, "Neural Network Based Control System for Robots Group Operating in 2-d Uncertain Environment" (2020, 16 citations), introduces a deep learning framework that enables a group of robots to collaboratively estimate environmental states, optimize path planning, and adapt trajectories through real-time interaction. This work addresses critical challenges in swarm robotics and distributed control. In a subsequent study (2021, 5 citations), Farhood refined a collision avoidance D* algorithm trained via convolutional neural networks, allowing a ground-based mobile robot to learn from experience and autonomously select optimal paths. These contributions demonstrate a clear trajectory from theoretical neural control architectures to practical, self-learning navigation systems. Farhood’s research holds significant promise for applications in search-and-rescue, industrial automation, and autonomous exploration, establishing him as a rising voice in the integration of deep learning with robotic decision-making.