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Fuzzy Tuning for the Docking Maneuver Controller of an Automated Guided Vehicle

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Multi-Objective Machine Learning

Part of the book series: Studies in Computational Intelligence ((SCI,volume 16))

Abstract

In some environments, mobile robots need to perform tasks in a precise manner. For this reason, we require obtaining good controllers in charge of these control tasks. In this work, we present a real-world application in the domain of multi-objective machine learning, which consists of an Automated Guided Vehicle (AGV), specifically, a fork-lift truck must often perform docking maneuvers to load pallets in conveyor belts. The main purpose is to improve some features of docking task as its duration, accuracy and stability, satisfying determined constraints. We propose a machine learning technique based on a multi-objective evolutionary algorithm in order to find multiple fuzzy logic controllers which optimize specific objectives and satisfy imposed constraints for docking task in charge of following up an online generated trajectory.

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Author information

Authors and Affiliations

  1. Dept. Information and Communications Engineering, University of Murcia, Murcia, Spain

    J.M. Lucas, H. Martinez & F. Jimenez

Authors
  1. J.M. Lucas
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  2. H. Martinez
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  3. F. Jimenez
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Editor information

Editors and Affiliations

  1. Honda Research Institute Europe GmbH, Carl-Legien-Str. 30, Offenbach, 63073, Germany

    Yaochu Jin Dr.

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© 2006 Springer

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Lucas, J., Martinez, H., Jimenez, F. (2006). Fuzzy Tuning for the Docking Maneuver Controller of an Automated Guided Vehicle. In: Jin, Y. (eds) Multi-Objective Machine Learning. Studies in Computational Intelligence, vol 16. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-33019-4_25

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  • DOI: https://doi.org/10.1007/3-540-33019-4_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30676-4

  • Online ISBN: 978-3-540-33019-6

  • eBook Packages: EngineeringEngineering (R0)

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