M. R. Meybodi

Papers

Total Citations

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About

M. R. Meybodi is a leading researcher in intelligent robotics and autonomous navigation, with a particular focus on multi-robot systems and sensor-based control. His work bridges reinforcement learning and fuzzy logic to solve complex navigation problems in unknown environments. One of his most influential contributions is a distributed value function reinforcement learning approach for multi-mobile robot navigation, which enables robots to coordinate and navigate using only local sensor information—a foundational idea that has garnered 8 citations and inspired further work in decentralized robotics. He also developed a novel robot navigation algorithm for wall following that integrates fuzzy logic with Kalman filtering, achieving robust obstacle avoidance and path tracking in uncertain settings (4 citations). This work has practical implications for autonomous exploration and service robotics. Meybodi’s research is characterized by its elegant fusion of learning and control theory, making his papers essential reading for students and engineers working on intelligent autonomous systems. His achievements demonstrate a sustained commitment to advancing the state of the art in mobile robot autonomy.