A novel optimization robust design of artificial neural networks to solve the inverse kinematics of a manipulator of 6 DOF

Ibarra-Pérez, Teodoro; Martínez-Blanco, Ma. del Rosario; Olivera-Domingo, Fernando; Ortiz-Rodríguez, J.M.; Gómez, Jesús Gómez

doi:10.1109/icit46573.2021.9453701

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…The mechanical components were designed based on the modification of an open-source, six-degree-of-freedom Thor Robot, which was released in 2017 [12]. Since then, the model has undergone many modifications, adaptations, and applications by numerous researchers worldwide and more than 20 units have been built in at least 11 different countries [13][14][15][16][17][18][19]. FreeCAD, an open-source 3D modeling software was used to model the robotic arm.…”

Section: Mechanical Designmentioning

confidence: 99%

UIArm I: Development of a Low-Cost and Modular 4-DOF Robotic Arm for Sorting Plastic Bottles from Waste Stream

Adediran¹,

Ayodeji²,

Kazeem³

et al. 2023

JESA

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The study presents the development of an accessible, reliable, 3D printable, low-cost, and modular 4 degrees-of-freedom robotic arm for the automated sorting of plastic bottles from the waste stream. The UIArm I robot arm was designed based on the modification of an open-source Thor Robot model using Free-CAD with the components 3D printed using PLA and PETG. The forward kinematics was obtained by Denavit-Hartenberg (DH) method, while the analytical method was used for the inverse kinematics. The electrical components include stepper motors, servo motors, motor drivers, a printed circuit board (PCB), an Arduino Mega microprocessor, a light source for illumination, and a PC with a webcam. Python was used for programming the PC and C# for the Arduino microprocessor. TensorFlow, an end-to-end open-source, machine learning platform was used to develop the object detection algorithm based on a deep neural network. The object detection model achieved an accuracy of 91% for Pepsi plastic bottles which formed the bulk of training images. Other types of plastic bottles were detected with an 85% accuracy. The study has demonstrated the viability of a locally developed robotic arm for the automated sorting of plastic bottles.

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