A Hybrid Intelligent System to Detect Anomalies in Robot Performance

  1. Nuño Basurto Hornillos 1
  2. Ángel Arroyo Puente 1
  3. Carlos Cambra Baseca 1
  4. Álvaro Herrero Cosío 1
  1. 1 Universidad de Burgos

    Universidad de Burgos

    Burgos, España

    ROR https://ror.org/049da5t36

Hybrid Artificial Intelligent Systems. 16th International Conference, HAIS 2021: Bilbao, Spain. September 22–24, 2021. Proceedings
  1. Hugo Sanjurjo González (ed. lit.)
  2. Iker Pastor López (ed. lit.)
  3. Pablo García Bringas (ed. lit.)
  4. Héctor Quintián Pardo (ed. lit.)
  5. Emilio Santiago Corchado Rodríguez (ed. lit.)

Publisher: Springer International Publishing AG

ISBN: 978-3-030-86271-8

Year of publication: 2021

Pages: 415-426

Congress: Hybrid Artificial Intelligent Systems (HAIS) (16. 2021. Bilbao)

Type: Conference paper


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.