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Visual Detection of Tiny and Transparent Objects for Autonomous Robotic Pick-and-Place Operations

Timo Markert; Sebastian Matich; Daniel Neykov; Markus Muenig; Andreas Theissler; Martin Atzmueller
In: Proc. IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2022). IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE, 2022.


For the manufacturing of miniature force/torque sensors, extreme accuracy is required due to the tiny size of the strain gauges inside the sensors (2×2.5mm). The current method of manually assembling them by hand is difficult, time- intensive, and error-prone. To improve this, a system to pick up the tiny objects from a plate and place them on elementary cells is being devised using a 6-axis robot arm with custom end-effector and a camera with magnification lens. This paper focuses on the perception module by evaluating methods for detecting tiny and transparent objects and obtaining spatial information from 2D images. Additionally, it considers aspects of the camera-to-robot calibration process, which are necessary to transfer the accuracy of image recognition into the real world. An approach using image segmentation and blob detection is taken, precluding the need for machine learning models. This is possible due to the superb image quality achieved by the sufficiently advanced camera and lighting setup. As a conclusion, we propose a perception module, which is capable of pinpointing strain gauge positions within ±0.1mm and can also recognize different types of components based on physical dimensions. Our end-to-end approach for automatic pick-and-place operations integrates the perception module, camera-to-robot calibration, and a last-minute correction routine, which ultimately leads to an overall positioning accuracy of ±0.3mm.