Greenhouse Mobile Robot Navigation Using Wheel Revolution Encoding and Learning Algorithm

Repetitive and dangerous tasks such as harvesting and spraying have made robots usable in the greenhouses. The mechanical structure and navigation algorithm are two important parameters in the design and fabrication of mobile greenhouse robots. In this study, a four- wheel differential steering mobi...

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Autores principales:	A Heidari, J Amiri Parian
Formato:	article
Lenguaje:	EN FA
Publicado:	Ferdowsi University of Mashhad 2021
Materias:	agricultural robot encoder sensor vehicle navigation wheeled mobile robot Agriculture (General) S1-972 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/036def3f772b44af860e0d31a0e1b673
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id	oai:doaj.org-article:036def3f772b44af860e0d31a0e1b673
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spelling	oai:doaj.org-article:036def3f772b44af860e0d31a0e1b6732021-11-14T06:35:35ZGreenhouse Mobile Robot Navigation Using Wheel Revolution Encoding and Learning Algorithm2228-68292423-394310.22067/jam.v11i1.80722https://doaj.org/article/036def3f772b44af860e0d31a0e1b6732021-03-01T00:00:00Zhttps://jame.um.ac.ir/article_34521_f1df90c9905b55217e78456fc8d996ab.pdfhttps://doaj.org/toc/2228-6829https://doaj.org/toc/2423-3943Repetitive and dangerous tasks such as harvesting and spraying have made robots usable in the greenhouses. The mechanical structure and navigation algorithm are two important parameters in the design and fabrication of mobile greenhouse robots. In this study, a four- wheel differential steering mobile robot was designed and constructed to act as a greenhouse robot. Then, the navigation of the robot at different levels and actual greenhouses was evaluated. The robot navigation algorithm was based on the path learning, so that the route was stored in the robot memory using a remote control based on the pulses transmitted from the wheels encoders; then, the robot automatically traversed the path. Robot navigation accuracy was tested at different surfaces (ceramics, concrete, dense soil and loose soil) in a straight path 20 meters long and a square path, 4×4 m. Then, robot navigation accuracy was investigated in a greenhouse. Robot movement deviation value was calculated using root mean square error (RMSE) and standard deviation (SD). The results showed that the RMSE of deviation of autonomous method from manual control method in the straight path to the length of 20 meters in ceramic, concrete, dense soil and loose soil were 4.3, 2.8, 4.6 and 8 cm, and in the 4×4 m square route were 6.6, 5.5, 13.1 and 47.1 cm, respectively.A HeidariJ Amiri ParianFerdowsi University of Mashhadarticleagricultural robotencoder sensorvehicle navigationwheeled mobile robotAgriculture (General)S1-972Engineering (General). Civil engineering (General)TA1-2040ENFAJournal of Agricultural Machinery, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN FA
topic	agricultural robot encoder sensor vehicle navigation wheeled mobile robot Agriculture (General) S1-972 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	agricultural robot encoder sensor vehicle navigation wheeled mobile robot Agriculture (General) S1-972 Engineering (General). Civil engineering (General) TA1-2040 A Heidari J Amiri Parian Greenhouse Mobile Robot Navigation Using Wheel Revolution Encoding and Learning Algorithm
description	Repetitive and dangerous tasks such as harvesting and spraying have made robots usable in the greenhouses. The mechanical structure and navigation algorithm are two important parameters in the design and fabrication of mobile greenhouse robots. In this study, a four- wheel differential steering mobile robot was designed and constructed to act as a greenhouse robot. Then, the navigation of the robot at different levels and actual greenhouses was evaluated. The robot navigation algorithm was based on the path learning, so that the route was stored in the robot memory using a remote control based on the pulses transmitted from the wheels encoders; then, the robot automatically traversed the path. Robot navigation accuracy was tested at different surfaces (ceramics, concrete, dense soil and loose soil) in a straight path 20 meters long and a square path, 4×4 m. Then, robot navigation accuracy was investigated in a greenhouse. Robot movement deviation value was calculated using root mean square error (RMSE) and standard deviation (SD). The results showed that the RMSE of deviation of autonomous method from manual control method in the straight path to the length of 20 meters in ceramic, concrete, dense soil and loose soil were 4.3, 2.8, 4.6 and 8 cm, and in the 4×4 m square route were 6.6, 5.5, 13.1 and 47.1 cm, respectively.
format	article
author	A Heidari J Amiri Parian
author_facet	A Heidari J Amiri Parian
author_sort	A Heidari
title	Greenhouse Mobile Robot Navigation Using Wheel Revolution Encoding and Learning Algorithm
title_short	Greenhouse Mobile Robot Navigation Using Wheel Revolution Encoding and Learning Algorithm
title_full	Greenhouse Mobile Robot Navigation Using Wheel Revolution Encoding and Learning Algorithm
title_fullStr	Greenhouse Mobile Robot Navigation Using Wheel Revolution Encoding and Learning Algorithm
title_full_unstemmed	Greenhouse Mobile Robot Navigation Using Wheel Revolution Encoding and Learning Algorithm
title_sort	greenhouse mobile robot navigation using wheel revolution encoding and learning algorithm
publisher	Ferdowsi University of Mashhad
publishDate	2021
url	https://doaj.org/article/036def3f772b44af860e0d31a0e1b673
work_keys_str_mv	AT aheidari greenhousemobilerobotnavigationusingwheelrevolutionencodingandlearningalgorithm AT jamiriparian greenhousemobilerobotnavigationusingwheelrevolutionencodingandlearningalgorithm
_version_	1718429816206655488

Greenhouse Mobile Robot Navigation Using Wheel Revolution Encoding and Learning Algorithm

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