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About

Amir Guizani is an emerging researcher specializing in swarm robotics, autonomous mobile systems, and Model-Based Systems Engineering (MBSE), with a growing body of work that bridges theoretical design methodologies and practical industrial applications. His research addresses one of the central challenges in modern robotics: developing structured, scalable frameworks for designing and deploying complex autonomous systems, including unmanned aerial vehicles (UAVs) and automated guided vehicles (AGVs). Guizani's most notable contributions include the development of UavSwarmML, a SysML-based modeling language tailored for UAV swarm systems (15 citations), and an integrated MBSE methodology for AGV design rooted in swarm robotics principles (13 citations). His systematic literature review of collaborative SLAM for autonomous mobile robots (12 citations) has become a valuable reference for researchers navigating the multi-robot localization landscape. He has also pioneered top-down design approaches ensuring continuity across swarm robot design levels and introduced ROS2ML, a SysML profile aimed at streamlining autonomous mobile robot development. With a cumulative citation count exceeding 60 across eight publications, Guizani's work is gaining meaningful traction in the robotics and Industry 4.0 communities. His consistent focus on integrating model-driven engineering with swarm intelligence positions him as a promising voice in next-generation autonomous systems design.