Two Contributions at INES Conference 2026 in Budapest
In July 2026, we will present two research contributions at the INES Conference in Budapest, covering advances in laboratory automation and indoor positioning systems.
Complex Multi-Robot Automation System for Enzymatic Assays
Biological assays such as enzymatic tests are essential in drug development, but traditional laboratory automation is often limited by system capacity and restricted integration of peripheral devices.
We present a hierarchical multi-robot automation architecture that combines a Biomek i7 liquid handler, a collaborative robot, and a mobile transport robot. The system enables decoupled execution of sample handling, assay processing, and logistics across multiple laboratory areas, allowing parallel and scalable workflows beyond single-instrument limitations.
Validated using a trypsin inhibition assay, the system demonstrates high reproducibility and robustness, achieving a median coefficient of variation of 4.13% compared to 7.13% for manual execution. The results highlight the potential of multi-robot coordination for scalable and fully automated laboratory processes.
Sensor Fusion of UWB and IMU for Optimized Indoor Positioning
Accurate indoor positioning is a key requirement for modern automation systems, especially in challenging environments. Ultra-wideband (UWB) technology provides robust distance measurements, but performance can degrade in non-line-of-sight conditions.
We propose a low-cost indoor positioning system combining UWB and inertial measurement unit (IMU) data using Kalman filter-based sensor fusion. The system includes automatic room-based anchor selection and Wi-Fi-based data transmission for real-time tracking.
Experimental results show that fusing UWB and IMU significantly improves positioning accuracy, particularly in NLoS scenarios, enabling more reliable indoor tracking across multiple zones.
Both contributions demonstrate enabling technologies for next-generation automation systems - from scalable robotic laboratory workflows to robust indoor localization through sensor fusion.
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