Alexander Poeppel
Papers
8
Total Citations
68
H-Index
4
About
Alexander Poeppel is a leading researcher in safe human-robot interaction and collaborative manufacturing for Industry 4.0. His work centers on developing robust, real-time sensor systems and flexible robot architectures that enable humans and robots to work together safely and efficiently. Poeppel’s most impactful contribution is the use of capacitive proximity sensors for environment-aware human detection, as demonstrated in his highly cited 2016 paper (41 citations), which established a foundational method for reliably sensing humans in a robot’s workspace. He advanced this concept by applying neural networks for robust distance estimation (2020, 9 citations), significantly improving sensor accuracy in dynamic environments. Beyond sensing, Poeppel has pioneered frameworks like SensorClouds for real-time multi-modal sensor data processing and RealCaPP for plug-and-produce distributed robot control, both published in 2023. His work on self-configuring robot systems based on ontologies and sensor-guided motions for component testing further underscores his commitment to adaptive, flexible production. With a growing body of work that directly addresses the core challenges of human-robot collaboration, Poeppel is shaping the future of safe, intelligent manufacturing.
Research Focus
Key Achievements
Top Papers
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- 6Sensor-guided motions for robot-based component testing3 citations · 2022
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- 8GoLive A modular Mixed Reality Simulation for Semantic Plug and Play2 citations · 2023