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Hybrid Methodology for Path Planning and Computational Vision Applied to Autonomous Mission: A New Approach

Published online by Cambridge University Press:  25 July 2019

Fabrício O. Coelho ,
Milena F. Pinto ,
João Pedro C. Souza  and
André L. M. Marcato Open the ORCID record for André L. M. Marcato [Opens in a new window]
Fabrício O. Coelho*
Affiliation:
Electrical Engineering Department, Federal University of Juiz de Fora, Juiz de Fora, Brazil E-mails: milena.faria@engenharia.ufjf.br, andre.marcato@ufjf.edu.br
Milena F. Pinto
Affiliation:
Electrical Engineering Department, Federal University of Juiz de Fora, Juiz de Fora, Brazil E-mails: milena.faria@engenharia.ufjf.br, andre.marcato@ufjf.edu.br
João Pedro C. Souza
Affiliation:
Faculty of Engineering, Faculdade de Engenharia da Universidade do PortoPorto, Portugal. E-mail: joao.pedro@engenharia.ufjf.br
André L. M. Marcato
Affiliation:
Electrical Engineering Department, Federal University of Juiz de Fora, Juiz de Fora, Brazil E-mails: milena.faria@engenharia.ufjf.br, andre.marcato@ufjf.edu.br
*
*Corresponding author. E-mail: fabricio.coelho2010@engenharia.ufjf.br
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Summary

In recent years, mobile robots have become increasingly frequent in daily life applications, such as cleaning, surveillance, support for the elderly and people with disabilities, as well as hazardous activities. However, a big challenge arises when the robotic system must perform a fully autonomous mission. The main problems of autonomous missions include path planning, localisation, and mapping. Thus, this research proposes a hybrid methodology for mobile robots on an autonomous mission involving an offline approach that uses the Direct-DRRT* algorithm and the artificial potential fields algorithm as the online planner. The experimental design covers three scenarios with an increasing degree of accuracy in respect of the real world. Additionally, an extensive evaluation of the proposed methodology is reported.

Keywords

Autonomous missionsArtificial intelligenceLocalisationMobile robotsPath planning
Type
Articles
Information
Robotica , Volume 38 , Issue 6 , June 2020 , pp. 1000 - 1018
Copyright
© Cambridge University Press 2019

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