Andry Meylani

Papers

Total Citations

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About

Andry Meylani is a researcher whose work sits at the intersection of computational intelligence and robotics, with a particular focus on fuzzy logic systems for autonomous navigation. His most cited paper, “Different Types of Fuzzy Logic in Obstacles Avoidance of Mobile Robot” (2018), has garnered 6 citations and provides a critical comparative analysis of Type 1 (T1FLS) and Interval Type 2 (IT2FLS) fuzzy logic systems. In this study, Meylani simulated mobile robot obstacle avoidance across varied environments, start-target configurations, and obstacle densities, demonstrating how IT2FLS can outperform its predecessor in handling uncertainty. This contribution is significant for advancing more robust and adaptive autonomous systems. Though his citation count is modest, Meylani’s work represents a foundational step in applying advanced fuzzy logic to real-world robotic challenges. His research is particularly valuable for students and engineers exploring how computational intelligence can enhance machine decision-making in dynamic, unpredictable settings. Meylani’s focus on comparative performance analysis offers a clear, practical pathway for improving robot navigation algorithms.