Anke Schmeink

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About

Anke Schmeink is a researcher whose work sits at the intersection of machine learning, cyber-physical systems, and autonomous robotics, with a particular focus on intelligent localization and adaptive control in complex environments. Her research has made notable contributions to the challenge of indoor positioning, an increasingly critical capability for Industry 4.0 applications where precise spatial awareness of objects and agents within connected industrial systems is essential. In her 2018 paper on learning-based indoor localization, Schmeink explored data fusion techniques to support modern process automation, while her 2019 follow-up demonstrated practical online and offline learning approaches for sound-based positioning using accessible, low-cost hardware — making these solutions viable for real-world deployment. More recently, her 2025 work on the UR-EARL framework showcases her expanding research horizon, combining evolutionary algorithms with reinforcement learning to tackle the dual challenge of autonomous underwater vehicle body design and control system development. With citations accumulating across these diverse yet interconnected domains, Schmeink represents a versatile and forward-thinking researcher whose contributions bridge theoretical machine learning with practical engineering applications across terrestrial and underwater autonomous systems.