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About

Masoud Emam is a robotics researcher specializing in autonomous navigation and motion control for wheeled mobile robots. His primary research focuses on path following algorithms—a critical navigation approach that guides robots along predefined paths without strict time constraints. Emam has made notable contributions to the control of omni-directional four-wheeled mobile robots, demonstrating how path following can overcome limitations of traditional trajectory tracking controllers. His most cited work, "Path following of an omni-directional four-wheeled mobile robot" (2016, 8 citations), presents two effective path following methods for these versatile platforms. Emam has also advanced the field by addressing the challenging problem of sliding effects in car-like robots. Through his work on robust path following using Linear Matrix Inequality (LMI) formulations, including "Solving Path Following Problem for Car-Like Robot in the Presence of Sliding Effect via LMI Formulation" (2017, 3 citations) and its robust counterpart employing mixed H₂/H∞ control (2017, 2 citations), he has developed controllers that maintain accurate path tracking even under adverse conditions. These contributions are valuable for real-world applications where wheel slippage is inevitable, such as outdoor or uneven terrain navigation.