Robot Server Architecture for Optimizing the Solar Panel Power Output
Document Type
Article
Publication Date
8-1-2016
Publication Title
Transactions on Machine Learning and Artificial Intelligence
Volume
4
Issue
4
First page number:
9
Last page number:
17
Abstract
Solar panel facilities for generating electricity have increased exponentially in the recent years. Dust and bird droppings on the solar panels inhibit the energy production. Having people to inspect them and, if needed, clean them is expensive and increases the energy cost. In this research paper we introduce a robot-server architecture for the purpose of inspecting the panels and cleaning them if there is a need for it. The general architecture of the robot consists of a mechanical part, an electromechanical part, an electronic part, and a software part. The mechanical and electromechanical parts consist of an all-terrain vehicle, two electric brushless motors, a telescopic vision system, and telescopic cleaning system with a brush, stepper motors controlling the telescopic vision system, and the telescopic vacuum system with a brushless electric motor. The electronic system consists of three electronic speed controllers, navigation sensors, a computer board, a hard disk, a transceiver, and an antenna for wireless communication. The software consists of a scalable operating system, an intelligent vision system with pattern recognition, a communication software system, an intelligent navigation system, and a file server with a database, TLS security, network communication software based on UDP, and internet communication based on web sockets and TCP-IP. In addition to that for street solar lights we designed a PCB board with a sensor that activates a mechanism similar to windshield wipers that cleans the glass of the solar panels powering the lights automatically when needed.
Repository Citation
Zamora, E.,
Ramos, M.,
Moutafis, K.,
Yfantis, E. A.
(2016).
Robot Server Architecture for Optimizing the Solar Panel Power Output.
Transactions on Machine Learning and Artificial Intelligence, 4(4),
9-17.
http://dx.doi.org/10.14738/tmlai.44.2141