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About

Anna Yershova’s research lies at the intersection of motion planning, sensing under uncertainty, and geometric sampling, with a particular focus on developing rigorous mathematical frameworks for robotics. Her most influential work, “Deterministic sampling methods for spheres and SO(3)” (87 citations), introduced novel techniques for generating uniform deterministic samples over spheres and the rotation group SO(3)—a critical contribution for motion planning, optimization, and verification in robotics and graphics. She further advanced this line of research with “Generating Uniform Incremental Grids on SO(3) Using the Hopf Fibration” (28 citations), leveraging elegant geometric structures to improve sampling efficiency. Yershova also made significant contributions to robotics with sensing uncertainty. Her work on “Mapping and Pursuit-Evasion Strategies For a Simple Wall-Following Robot” (55 citations) defined and analyzed minimalistic robots capable of navigating unknown polygonal environments using only local sensors. In “Bitbots: simple robots solving complex tasks” (21 citations), she characterized the information spaces of severely limited robots, demonstrating that surprisingly complex tasks can be solved despite extreme sensing constraints. Her research elegantly combines theoretical rigor with practical robotics challenges, establishing foundational methods for sampling on non-Euclidean spaces and for planning under uncertainty.