Alberto Soragna

iRobot (United States), Sapienza University of Rome

Papers

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About

Alberto Soragna is a roboticist whose research lies at the intersection of autonomous navigation and real-time robotic middleware. His most impactful work investigates the performance of the Robot Operating System 2 (ROS 2), specifically through a deep-dive into node composition and executor models. His highly cited 2023 paper, "Impact of ROS 2 Node Composition in Robotic Systems" (63 citations), benchmarks how different computational graph configurations affect system efficiency, providing crucial guidance for developers building complex, real-time robotic applications. This work is essential for understanding the trade-offs in modern robotic software architecture. Beyond middleware, Soragna has contributed to active SLAM (Simultaneous Localization and Mapping), proposing a method that uses connectivity graphs as priors to enable robots to autonomously explore and map unknown environments. His research directly addresses the challenge of creating fully autonomous mobile systems that can reason about where to go to build the most reliable map. By bridging the gap between low-level system performance and high-level autonomous decision-making, Soragna’s work provides foundational tools for the next generation of intelligent, self-navigating robots.