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About

Rihem Farkh is a rising researcher in autonomous robotics and intelligent control systems, with a focus on integrating artificial intelligence into mobile robot navigation. Her work spans computer vision, deep learning, and hybrid control architectures, addressing real-world challenges in both industrial and medical settings. Notably, her 2022 paper on vision-based PID control for line-tracking robots has garnered 27 citations, establishing a foundation for reliable indoor navigation. She extended this research to medical applications during the COVID-19 pandemic, proposing intelligent autonomous robots to alleviate healthcare facility shortages—a contribution that earned 19 citations. Farkh’s innovative use of convolutional neural networks (CNNs) combined with PID control for mobile robots, cited 16 times, demonstrates her ability to merge classical control with modern AI. Her deep learning approaches for motion control systems (13 citations) and autonomous robot control (7 citations) further showcase her expertise. Most recently, in 2025, she introduced a hybrid LLM Q-learning/DQN framework for adaptive obstacle avoidance in embedded robotics, a pioneering work that bridges large language models with reinforcement learning for real-time decision-making on microcontrollers. Farkh’s research consistently pushes the boundaries of autonomous systems, offering practical, efficient solutions for next-generation robotics.