article

Vision-based maze navigation for humanoid robots

Authors:

Antonio Paolillo,

Angela Faragasso,

Giuseppe Oriolo,

Marilena VendittelliAuthors Info & Claims

Autonomous Robots, Volume 41, Issue 2

Pages 293 - 309

https://doi.org/10.1007/s10514-015-9533-1

Published: 01 February 2017 Publication History

Abstract

We present a vision-based approach for navigation of humanoid robots in networks of corridors connected through curves and junctions. The objective of the humanoid is to follow the corridors, walking as close as possible to their center to maximize motion safety, and to turn at curves and junctions. Our control algorithm is inspired by a technique originally designed for unicycle robots that we have adapted to humanoid navigation and extended to cope with the presence of turns and junctions. In addition, we prove here that the corridor following control law provides asymptotic convergence of robot heading and position to the corridor bisector even when the corridor walls are not parallel. A state transition system is designed to allow navigation in mazes of corridors, curves and T-junctions. Extensive experimental validation proves the validity and robustness of the approach.

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Cited By

Pandey PHe QPompili DTron R(2018)Light-Weight Object Detection and Decision Making via Approximate Computing in Resource-Constrained Mobile Robots2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS.2018.8594200(6776-6781)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1109/IROS.2018.8594200
Trinh SSpindler FMarchand EChaumette F(2018)A modular framework for model-based visual tracking using edge, texture and depth features2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS.2018.8594003(89-96)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1109/IROS.2018.8594003
Gorbenko APopov V(2018)Reduction of the uncertainty in feature trackingApplied Intelligence10.1007/s10489-018-1236-948:12(4626-4645)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s10489-018-1236-9

Vision-based maze navigation for humanoid robots
1. Computer systems organization
  1. Embedded and cyber-physical systems
2. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Planning and scheduling

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Published In

cover image Autonomous Robots

Autonomous Robots Volume 41, Issue 2

February 2017

231 pages

ISSN:0929-5593

Issue’s Table of Contents

Copyright © Copyright © 2017 Springer Science+Business Media New York.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 February 2017

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Cited By

Pandey PHe QPompili DTron R(2018)Light-Weight Object Detection and Decision Making via Approximate Computing in Resource-Constrained Mobile Robots2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS.2018.8594200(6776-6781)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1109/IROS.2018.8594200
Trinh SSpindler FMarchand EChaumette F(2018)A modular framework for model-based visual tracking using edge, texture and depth features2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS.2018.8594003(89-96)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1109/IROS.2018.8594003
Gorbenko APopov V(2018)Reduction of the uncertainty in feature trackingApplied Intelligence10.1007/s10489-018-1236-948:12(4626-4645)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s10489-018-1236-9

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