SORTED : Serial manipulator with Object Recognition Trough Edge Detection
Abstract: Today, there is an increasing demand for smart robots that can make decisions on their own and cooperate with humans in changing environments. The application areas for robotic arms with camera vision are likely to increase in the future of artificial intelligence as algorithms become more adaptable and intelligent than ever. The purpose of this bachelor’s thesis is to develop a robotic arm that recognises arbitrarily placed objects with camera vision and has the ability to pick and place the objects when they appear in unpredictable positions. The robotic arm has three degrees of freedom and the construction is modularised and 3D-printed with respect to maintenance, but also in order to be adaptive to new applications. The camera vision sensor is integrated in an external camera tripod with its field of view over the workspace. The camera vision sensor recognises objects through colour filtering and it uses an edge detection algorithm to return measurements of detected objects. The measurements are then used as input for the inverse kinematics, that calculates the rotation of each stepper motor. Moreover, there are three different angular potentiometers integrated in each axis to regulate the rotation by each stepper motor. The results in this thesis show that the robotic arm is able to pick up to 90% of the detected objects when using barrel distortion correction in the algorithm. The findings in this thesis is that barrel distortion, that comes with the camera lens, significantly impacts the precision of the robotic arm and thus the results. It can also be stated that the method for barrel distortion correction is affected by the geometry of detected objects and differences in illumination over the workspace. Another conclusion is that correct illumination is needed in order for the vision sensor to differentiate objects with different hue and saturation.
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