Mobile Robot for Object Detection using an IoT System
| Autor: | Luis Angel Hernandez-Fernandez, Cesar Manuel Hernandez-Mendoza, Gerardo Asael Lopez-Alfaro, Luz Maria Rodriguez-Vidal, Ulises Vidal-Espitia, Juan Pablo Serrano-Rubio |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Web server business.industry Computer science Mobile robot 02 engineering and technology Python (programming language) computer.software_genre Convolutional neural network Object detection 020901 industrial engineering & automation Human–computer interaction Web page 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing The Internet business computer computer.programming_language |
| Zdroj: | 2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC). |
| DOI: | 10.1109/ropec50909.2020.9258697 |
| Popis: | The Internet of Things (IoT) is a technological revolutionary paradigm which has enabled communication with a wide number of devices through the Internet. In this paper we present the design of a mobile robot which uses an Internet of Things architecture to be controlled remotely. This robot includes a camera which is used to obtain landscape images from the mobile robot. A web server allows to store the commands to control the movements of the mobile robot in a MySQL database as well as the transmission of commands between the server and the mobile robot; by using web pages with PHP and an Android APP. Our electronic system is based on WiFi Module ESP8266 which offers advantages to incorporate open software architectures. The landscape images are processed in a computer using python and machine learning libraries to identify interesting objects on the images. The machine learning algorithms recognise on the landscape images two type of objects classes: agave plants and stones. We use a convolutional neural network for the object detection known as You Only Look Once (YOLO) which achieves 81.2% of accuracy for recognising both classes. |
| Databáze: | OpenAIRE |
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