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About

Anna Skobeleva is a robotics researcher specializing in localization, multi-robot coordination, and cooperative control. Her work bridges the gap between theoretical control algorithms and practical robotic applications, with a particular focus on enabling robots to navigate and explore unknown environments. Her most cited paper, "Extended Kalman Filter for indoor and outdoor localization of a wheeled mobile robot" (2016, 12 citations), presents a robust sensor fusion algorithm that integrates data from wheel encoders, a gyroscope, accelerometer, and DGPS to achieve seamless indoor-outdoor localization—a critical capability for autonomous mobile robots. Skobeleva has also made notable contributions to cooperative extremum seeking, a technique for finding maxima of unknown functions using robot teams. Her 2018 paper on planar cooperative extremum seeking (6 citations) demonstrates guaranteed convergence using a three-robot formation, while her earlier 2017 work (2 citations) established the foundation with a two-robot formation for one-dimensional maps. These contributions advance the field of multi-agent systems, offering practical solutions for environmental monitoring, search-and-rescue, and source localization. Skobeleva’s research exemplifies how theoretical control methods can be experimentally validated to solve real-world robotic challenges.