Length estimation of moving objects with ANN and gripping with robotic arm

Kürsad Ucar, Hasan Erdinc Kocer


It is possible to obtain general information about objects with image processing. However, while measuring the size of objects, especially with 2D cameras, the calibrated systems have been worked with restrictions such as fixed length and distance. However, without depth information and for objects of arbitrary positions and lengths, calculating their dimensions is a rather difficult task. In this study, an ANN-based application was carried out to calculate the amount of movement and the length of the object by viewing the moving objects from the side and top with two cameras. Objects moving on the conveyor belt are detected by deep learning-based YOLO. The motion amount of the object was calculated in the second image with the template created on the detected objects. An ANN is trained with the amount of movement and position information measured by two cameras. At the end of the training, the network estimates the lengths of the objects with small errors. The speed of the objects was calculated according to the calculated length and the targets were grasped with a robot arm.


Artificial Neural Network;Grasping; Moving object; Object dimension

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Selcuk University Journal of Engineering Sciences (SUJES) ISSN:2757-8828

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