Design, Development, and Testing of a Vision-Based Calibration System for Liquid Handling Robot

Abstract

This thesis presents the development of a vision-based calibration routine for an open-source liquid handling system, addressing the challenge of integrating reliable labware detection into an existing robotic platform with minimal hardware redesign. The study adopts an experimental, prototype-driven approach, evaluating multiple proof-of-concept configurations before selecting a top-mounted camera solution as the most viable. The implemented system employs OpenCV’s ArUco marker detection to locate labware, combined with a closed-loop tracking routine that iteratively aligns the camera’s principal point with the detected tag. Alternative approaches, such as rule-based detection with a side-mounted camera, provided valuable insight into system constraints including lighting sensitivity, and variability of labware. The chosen final setup demonstrated reliable calibration output in the form of labware position and orientation data, stored in .JSON format for integration into higher-level control software. The system proved capable of reliable marker-based calibration under controlled conditions. Furthermore, the results highlight both the strengths of the approach such as modularity and adaptability, and its limitations, particularly possible interference due to glare and relatively high setup time. Future work includes integrating object detection models, multi-camera systems for improved pose estimation, and extending the system towards real-time quality control in laboratory automation