Bekir AKSOY, Koray ÖZSOY, Mehmet YÜCEL, Özge EKREM, Osamah Khaled Musleh SALMAN

Yapay Zekâya Dayalı Robot Kol ile Hareket ve Farklı Nesnelerin Sertlik Kontrolü

Çalışmada 3D baskı teknolojilerinden Fused Deposition Modeling (FDM) yazıcı kullanılarak robotik kol üretilmiştir. Üretilen robot kolun görüntü işleme teknikleri ve makine öğrenme algoritmaları kullanarak dokunsal algılama ve hareket planlaması araştırılmıştır. Bu çalışmanın amacı, robotik kolun kontrolsüz kuvvet uygulamasını engellemek ve dokunsal kavrama sorunlarını çözmek için görüntü işleme teknikleri ve derin öğrenme algoritmaları kullanılarak yenilikçi yaklaşımların araştırılması ve uygulanmasıdır. Bu çalışmada, CAD programı ile tasarımı gerçekleştirilmiş parçaların FDM tipi üç boyutlu yazıcı kullanılarak katı modelleri alınmış ve montaj için uygun hale getirilmiştir. Montajı tamamlanan robotik elin kontrol sistemi ise temel olarak Raspberry Pi kontrol kartı, servo motorlar, basınç sesörleri ve kameradan oluşmaktadır. Robotik kola ait her parmak ucuna yerleştirilen basınç sensörleri ile ürünün sertliği ölçülerek dokunsal algılama işlemi gerçekleştirilmiştir. Raspberrry pi kontrol kartı kullanılarak sensörlerden alınan veriler işlenmekte ve servo motorlara uygun hareket ve kavrama basınç bilgisi gönderilmektedir. Kamera kullanılarak elde edilen insan elinin olası hareketleri ile robotik kol için referans bir veri seti hazırlanmıştır. Veri setine ait görüntüler üzerinde Gaussian filtreleme yöntemi kullanılarak görüntü işleme sağlanmıştır. Bununla birlikte veri seti üzerinde makine öğrenme algoritmaları kullanarak robotik kolun hareket açısal konumu optimize edilmiş ve HitNet, CNN, Kapsül Ağları ve Naive Bayes derin öğrenme modelleri kullanılarak robot kolun hareket planlanması %90 doğruluk oranı ile sınıflandırılmıştır. Performans değerlendirme kriterlerine göre başarıları kıyaslanan derin öğrenme modelleri arasında, robotik kolun hareket planlaması için; HitNET algoritması ile 97.23%, CNN ile 97.48%, Capsnet algoritması ile %98,58 ve Naive Bayes modeli ile %98.61 doğruluk oranı elde edilmiştir. Performans değerlendirme kriterleri sonucunda; Naive Bayes modelinin %98.61 doğruluk, %98.63 özgüllük, %98.65 duyarlılık, 1.39 hata oranı ve %68.64 F-ölçüsü değeri ile diğer modellere göre daha başarılı sonuç verdiği gözlemlenmiştir.

Motion Control of the Robot Arm Manufactured with a Three-Dimensional Printer and Hardness Detection of Objects

In the study, a robotic arm was produced using a Fused Deposition Modeling (FDM) printer, one of the 3D printing technologies. Tactile sensing and motion planning of the produced robot arm was investigated by using image processing techniques and machine learning algorithms. This study aims to investigate and apply innovative approaches using image processing techniques and deep learning algorithms to prevent uncontrolled force application of the robotic arm and to solve tactile grip problems. In this study, solid models of the parts were designed by CAD program and manufactured using FDM type three-dimensional printer. The control system of the robotic hand consists of a Raspberry Pi control card, servo motors, pressure sensors, and a camera. Tactile sensing was performed by measuring the hardness of the product with pressure sensors placed on each fingertip of the robotic arm. Raspberry pi control card is receive the data from the sensors are process them, after that the appropriate motion and clutch pressure information is sent to the servo motors. A reference data set for the robotic arm was prepared with the possible movements of the human hand obtained using the camera. Image processing is provided by using the Gaussian filtering method on the images of the data set. In addition, the angular position of the robotic arm's motion was optimized using machine learning algorithms on the data set, and the motion planning of the robot arm was classified with 90% accuracy using HitNet, CNN, Capsule Networks, and Naive Bayes deep learning models. Among the deep learning models which were very successful are compared each other according to the performance evaluation criteria, for the motion planning of the robotic arm; The accuracy rate was 97.23% with the HitNET algorithm, 97.48% with CNN, 98.58% with the Capsnet algorithm and 98.61% with the Naive Bayes model. As a result of the performance evaluation criteria; It has been observed that the Naive Bayes model gives more successful results than other models with 98.61% accuracy, 98.63% specificity, 98.65% sensitivity, 1.39 error rate, and 68.64% F-measure value.

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