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A Hybrid Intelligent System to Detect Anomalies in Robot Performance

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Hybrid Artificial Intelligent Systems (HAIS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12886))

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Abstract

Although self-diagnosis is required for autonomous robots, little effort has been devoted to detect software anomalies in such systems. The present work contributes to this field by applying a Hybrid Artificial Intelligence System (HAIS) to successfully detect these anomalies. The proposed HAIS mainly consists of imputation techniques (to deal with the MV), data balancing methods (in order to overcome the unbalancing of available data), and a classifier (to detect the anomalies). Imputation and balancing techniques are subsequently applied in for improving the classification performance of a well-know classifier: the Support Vector Machine. The proposed framework is validated with an open and recent dataset containing data collected from a robot interacting in a real environment.

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Authors and Affiliations

  1. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Av. Cantabria s/n, 09006, Burgos, Spain

    Nuño Basurto, Ángel Arroyo, Carlos Cambra & Álvaro Herrero

Authors
  1. Nuño Basurto
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  2. Ángel Arroyo
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  3. Carlos Cambra
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  4. Álvaro Herrero
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Corresponding author

Correspondence to Nuño Basurto .

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Editors and Affiliations

  1. University of Deusto, Bilbao, Spain

    Hugo Sanjurjo González

  2. University of Deusto, Bilbao, Spain

    Iker Pastor López

  3. University of Deusto, Bilbao, Spain

    Pablo García Bringas

  4. University of A Coruña, A Coruña, Spain

    Héctor Quintián

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Basurto, N., Arroyo, Á., Cambra, C., Herrero, Á. (2021). A Hybrid Intelligent System to Detect Anomalies in Robot Performance. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2021. Lecture Notes in Computer Science(), vol 12886. Springer, Cham. https://doi.org/10.1007/978-3-030-86271-8_35

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  • DOI: https://doi.org/10.1007/978-3-030-86271-8_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86270-1

  • Online ISBN: 978-3-030-86271-8

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