Ahmadreza Meysami

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About

Ahmadreza Meysami is a robotics researcher whose work focuses on the critical challenge of autonomous navigation in complex indoor environments. His primary research areas include path planning, mesh representation, and automated guided vehicle (AGV) systems. Meysami’s major contribution lies in demonstrating how the choice between triangle and quadrangle mesh representations fundamentally impacts robot path efficiency in diverse spatial settings—from warehouses to narrow corridors and workshops with varying obstacle distributions. His 2022 study on this topic, which has garnered 18 citations, provides essential guidance for optimizing navigation in industrial settings, particularly when considering inflation layers for safety. Building on this foundation, his 2024 paper on efficient indoor large map global path planning, also with 18 citations, advances practical solutions for real-world robot navigation at scale. Together, these works establish Meysami as a thoughtful contributor to the field of mobile robotics, offering engineers and researchers actionable insights into how environmental representation directly shapes autonomous navigation performance.