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About

Farnaz Adib Yaghmaie is a robotics and control systems researcher whose work centers on autonomous mobile robot navigation, nonlinear control theory, and reinforcement learning. Her most recognized contributions lie in developing novel navigation strategies for robots operating in dynamic, unstructured environments. Most notably, her "Escaping Algorithm," introduced in 2013 and extended in 2016, proposes a force-field-based approach enabling robots to perform simultaneous localization and mapping (SLAM) while autonomously avoiding moving obstacles without any predefined environmental information — earning her most-cited work with 10 citations. Complementing this, her "Potential Ban" method offers an alternative dynamic obstacle avoidance framework within the same navigation paradigm. On the control theory side, her 2012 work on feedback error learning control for nonholonomic mobile robots — combining sliding mode and adaptive control strategies — demonstrates her depth in tackling the inherent instability challenges of nonholonomic systems, accumulating 6 citations. More recently, Yaghmaie has expanded into reinforcement learning, authoring an accessible introduction bridging RL and classical control perspectives. Her body of work reflects a researcher committed to advancing intelligent, adaptive robotic systems capable of functioning reliably in real-world, unpredictable environments.