M. A. Sobolev

Institute of Automation and Electrometry, Russian Academy of Sciences, Siberian Branch of the Russian Academy of Sciences

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About

M. A. Sobolev is a researcher specializing in robotics, control systems, and multi-agent coordination, with a particular focus on formation control and navigation. His major contributions lie in developing robust, decentralized methods for controlling mobile robots and quadrotors in leader–follower formations. Notably, his work on the structural synthesis method enables follower robots to determine control actions using only relative position data, eliminating the need for leader motion parameters—a significant advancement for scalable, distributed systems. His most-cited paper, "Recurrent neural network and extended Kalman filter in SLAM problem" (11 citations), bridges neural networks with simultaneous localization and mapping, showcasing his versatility. Other influential works address robust formation control and relative measurement-based approaches, collectively cited over 25 times. Sobolev’s research is highly relevant for autonomous swarms, drone coordination, and real-world applications like search-and-rescue or environmental monitoring. His innovative control strategies continue to inspire new directions in decentralized robotics.