This thesis aims to investigate and develop two positional calibration methods that can be applied to a linear pipetting robot. The goal of the calibration is to detect displacements that have been made to objects that are located in the the robot’s reference system and try to estimate their new position. One of the methods utilizes the pressure system that is mounted on the robot’s arm. The pressure system was able to detect surfaces by blowing air through a pipette against desired surfaces. Positional information of targeted objects are acquired by using the surface detection feature against an extruded square landmark that acts as a reference for estimating displacements. The other method uses a barcode scanning camera by using its images to detect and retrieve positional information on Aruco markers. Estimation of the targeted object is done by tracking the movement of the Arucos position and orientation. Tests were made in order to analyse the performance of both methods and to verify that the requirement of 0.1 mm accuracy and precision could be obtained. The tests were limited to analysing the methods performance on stationary targets to guarantee that the methods did not detect incorrect displacements. It was found that the camera method could fulfill the requirement when it came to estimating XY-coordinates by using multiple images and placing the Aruco marker within a reasonable distance to the targeted object. However, the camera method was not accurate when it came to estimating the Z-coordinates of objects. As for the pressure method, it was able to fulfill the requirement when it came to estimating Z-coordinates, but its ability to estimate the XY-coordinates of an object was not sufficient. A recommendation would be combine both methods so that they can compensate each other by using the camera method for estimating the XY-coordinates and the pressure method for estimating the Z-coordinates.


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    Title :

    Positional calibration methods for linear pipetting robot


    Contributors:

    Publication date :

    2020-01-01


    Type of media :

    Theses


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



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