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Robot Trajectories Comparison: A Statistical Approach

The task of planning a collision-free trajectory from a start to a goal position is fundamental for an autonomous mobile robot. Although path planning has been extensively investigated since the beginning of robotics, there is no agreement on how to measure the performance of a motion algorithm. Thi...

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Detalles Bibliográficos
Autores principales: Ansuategui, A., Arruti, A., Susperregi, L., Yurramendi, Y., Jauregi, E., Lazkano, E., Sierra, B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4262753/
https://www.ncbi.nlm.nih.gov/pubmed/25525618
http://dx.doi.org/10.1155/2014/298462
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author Ansuategui, A.
Arruti, A.
Susperregi, L.
Yurramendi, Y.
Jauregi, E.
Lazkano, E.
Sierra, B.
author_facet Ansuategui, A.
Arruti, A.
Susperregi, L.
Yurramendi, Y.
Jauregi, E.
Lazkano, E.
Sierra, B.
author_sort Ansuategui, A.
collection PubMed
description The task of planning a collision-free trajectory from a start to a goal position is fundamental for an autonomous mobile robot. Although path planning has been extensively investigated since the beginning of robotics, there is no agreement on how to measure the performance of a motion algorithm. This paper presents a new approach to perform robot trajectories comparison that could be applied to any kind of trajectories and in both simulated and real environments. Given an initial set of features, it automatically selects the most significant ones and performs a statistical comparison using them. Additionally, a graphical data visualization named polygraph which helps to better understand the obtained results is provided. The proposed method has been applied, as an example, to compare two different motion planners, FM(2) and WaveFront, using different environments, robots, and local planners.
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spelling pubmed-42627532014-12-18 Robot Trajectories Comparison: A Statistical Approach Ansuategui, A. Arruti, A. Susperregi, L. Yurramendi, Y. Jauregi, E. Lazkano, E. Sierra, B. ScientificWorldJournal Research Article The task of planning a collision-free trajectory from a start to a goal position is fundamental for an autonomous mobile robot. Although path planning has been extensively investigated since the beginning of robotics, there is no agreement on how to measure the performance of a motion algorithm. This paper presents a new approach to perform robot trajectories comparison that could be applied to any kind of trajectories and in both simulated and real environments. Given an initial set of features, it automatically selects the most significant ones and performs a statistical comparison using them. Additionally, a graphical data visualization named polygraph which helps to better understand the obtained results is provided. The proposed method has been applied, as an example, to compare two different motion planners, FM(2) and WaveFront, using different environments, robots, and local planners. Hindawi Publishing Corporation 2014-11-25 /pmc/articles/PMC4262753/ /pubmed/25525618 http://dx.doi.org/10.1155/2014/298462 Text en Copyright © 2014 A. Ansuategui et al. https://creativecommons.org/licenses/by/3.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Ansuategui, A.
Arruti, A.
Susperregi, L.
Yurramendi, Y.
Jauregi, E.
Lazkano, E.
Sierra, B.
Robot Trajectories Comparison: A Statistical Approach
title Robot Trajectories Comparison: A Statistical Approach
title_full Robot Trajectories Comparison: A Statistical Approach
title_fullStr Robot Trajectories Comparison: A Statistical Approach
title_full_unstemmed Robot Trajectories Comparison: A Statistical Approach
title_short Robot Trajectories Comparison: A Statistical Approach
title_sort robot trajectories comparison: a statistical approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4262753/
https://www.ncbi.nlm.nih.gov/pubmed/25525618
http://dx.doi.org/10.1155/2014/298462
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