Diagnosis of hidden faults in the RCLL

Marco De Bortoli, Stalin Munoz Gutierrez, Gerald Steinbauer-Wagner*

*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung


The importance of Artificial Intelligence and Flexible Production is increasing in industry. Factories are evolving from static automation to complex autonomous systems to better cope with the challenges introduced by globalization. The RoboCup Logistics League (RCLL) was created as a testbed for flexible production of on-demand orders. In such a flexible domain, reliable scheduling requires
execution monitoring of actions. In this paper, we propose, and experimentally evaluate, a diagnosis to deal with execution faults in the RCLL. The proposed solution is based on the monitoring of the execution state of actions of a temporal plan. Our approach consists of a simple fault model, cascade-faults and a knowledge base, followed by replanning. The experimental results support the use of this principle to face the inconsistencies occurring by a nonobservable fault during the execution of plans.
Titel32nd International Workshop on Principle of Diagnosis
PublikationsstatusAngenommen/In Druck - 13 Sept. 2021
Veranstaltung32nd International Workshop on Principle of Diagnosis: DX 2021 - Hamburg, Deutschland
Dauer: 13 Sept. 202115 Sept. 2021


Konferenz32nd International Workshop on Principle of Diagnosis
KurztitelDX 2021

ASJC Scopus subject areas

  • Ingenieurwesen (insg.)
  • Informatik (insg.)

Fields of Expertise

  • Information, Communication & Computing
  • Mobility & Production

Treatment code (Nähere Zuordnung)

  • Application


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