Carsten Scheele

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About

Carsten Scheele’s research lies at the intersection of robotics, artificial intelligence, and precision metrology, with a focus on enhancing the performance and autonomy of industrial robots. His work addresses critical challenges in robot calibration, behavior learning, and dynamic motion measurement. In his most cited paper (8 citations), Scheele pioneered the use of artificial neural networks combined with a coordinate measuring machine to enable robots to learn both static and dynamic positioning behaviors, employing the Levenberg-Marquardt algorithm for efficient calibration and training data collection. This contribution offers a data-driven pathway to improve robot accuracy without extensive manual programming. In subsequent work (2 citations), he developed a measurement solution for capturing industrial robot trajectories during reorientations—a complex scenario increasingly relevant in modern manufacturing. By providing reliable performance data, his research supports the growing demand for high-precision automation. Scheele’s work is notable for bridging machine learning with industrial measurement science, offering practical tools for engineers seeking to optimize robot behavior in real-world settings. His contributions, though early in citation impact, lay important groundwork for adaptive, self-calibrating robotic systems.