Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 97-103 |
| Number of pages | 7 |
| Journal | Journal Europeen des Systemes Automatises |
| Volume | 56 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Feb 2023 |
| Externally published | Yes |
Keywords
- complex backgrounds
- deep learning
- garbage sorting
- machine vision
- robotic grasping
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications
- Industrial and Manufacturing Engineering
- Electrical and Electronic Engineering