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About

Aakriti Upadhyay is a roboticist whose research lies at the intersection of topology, geometry, and motion planning. Her core contributions focus on leveraging topological data analysis—specifically Vietoris-Rips complexes and discrete Morse theory—to build smarter, more efficient roadmaps for robot navigation in complex environments. Her 2019 paper on approximating C-free space topology (8 citations) introduced a novel method for constructing sparse roadmaps that capture the underlying connectivity of free space, a foundational idea she has since extended. Upadhyay’s 2022 work on applying discrete Morse theory to optimize safe motion planning paths (7 citations) demonstrates her ability to translate abstract mathematical tools into practical algorithms for reducing computational overhead. She has also pioneered concepts in heterogeneous planning spaces (6 citations), recognizing that real-world environments are rarely uniform. Her incremental path planning algorithm using metric gluing (5 citations) offers a scalable, topology-aware approach to reusing prior computations. Most recently, her work on the minimal path violation problem (2024) addresses fault-tolerant motion planning for manipulators, enabling rapid recovery from component failures without full re-planning. With a growing citation record and a clear trajectory toward bridging theoretical topology with real-time robotics, Upadhyay is establishing herself as a leading voice in topology-driven motion planning.