Comparing support vector machines and artificial neural networks in the recognition of steering angle for driving of mobile robots through paths in plantations
Carregando...
Data
2013-01-01
Autores
Jodas, Danilo S. [UNESP]
Marranghello, Norian [UNESP]
Pereira, Aledir S. [UNESP]
Guido, Rodrigo C. [UNESP]
Título da Revista
ISSN da Revista
Título de Volume
Editor
Elsevier B.V.
Resumo
The use of mobile robots turns out to be interesting in activities where the action of human specialist is difficult or dangerous. Mobile robots are often used for the exploration in areas of difficult access, such as rescue operations and space missions, to avoid human experts exposition to risky situations. Mobile robots are also used in agriculture for planting tasks as well as for keeping the application of pesticides within minimal amounts to mitigate environmental pollution. In this paper we present the development of a system to control the navigation of an autonomous mobile robot through tracks in plantations. Track images are used to control robot direction by pre-processing them to extract image features. Such features are then submitted to a support vector machine and an artificial neural network in order to find out the most appropriate route. A comparison of the two approaches was performed to ascertain the one presenting the best outcome. The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine and of an artificial neural network, which so far presented respectively around 93% and 90% accuracy in predicting the appropriate route. (C) 2013 The Authors. Published by Elsevier B.V. Selection and peer review under responsibility of the organizers of the 2013 International Conference on Computational Science
Descrição
Palavras-chave
Mobile robotics, Image processing, Support vector machines, Artificial neural network
Como citar
2013 International Conference On Computational Science. Amsterdam: Elsevier Science Bv, v. 18, p. 240-249, 2013.