Diagnosis of hidden faults in the RCLL

Marco De Bortoli; Stalin Munoz Gutierrez; Gerald Steinbauer-Wagner

Diagnosis of hidden faults in the RCLL

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

^*Korrespondierende/r Autor/-in für diese Arbeit

Institut für Softwaretechnologie (7160)

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

Abstract

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.

Originalsprache	englisch
Titel	32nd International Workshop on Principle of Diagnosis
Seitenumfang	8
Publikationsstatus	Angenommen/In Druck - 13 Sept. 2021
Veranstaltung	32nd International Workshop on Principle of Diagnosis: DX 2021 - Hamburg, Deutschland Dauer: 13 Sept. 2021 → 15 Sept. 2021 https://www.hsu-hh.de/imb/en/dx-2021

Konferenz

Konferenz	32nd International Workshop on Principle of Diagnosis
Kurztitel	DX 2021
Land/Gebiet	Deutschland
Ort	Hamburg
Zeitraum	13/09/21 → 15/09/21
Internetadresse	https://www.hsu-hh.de/imb/en/dx-2021

ASJC Scopus subject areas

Ingenieurwesen (insg.)
Informatik (insg.)

Fields of Expertise

Information, Communication & Computing
Mobility & Production

Treatment code (Nähere Zuordnung)

Application

Zugriff auf Dokument

https://www.hsu-hh.de/imb/wp-content/uploads/sites/677/2021/09/DX-2021_paper_15.pdfLizenz: Nicht definiert

Dieses zitieren

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title = "Diagnosis of hidden faults in the RCLL",

abstract = "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 requiresexecution 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.",

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AU - De Bortoli, Marco

AU - Munoz Gutierrez, Stalin

AU - Steinbauer-Wagner, Gerald

PY - 2021/9/13

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N2 - 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 requiresexecution 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.

AB - 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 requiresexecution 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.

KW - Robotics

KW - Diagnosis

KW - Plan Execution Monitoring

KW - Deliberation

M3 - Conference paper

BT - 32nd International Workshop on Principle of Diagnosis

T2 - 32nd International Workshop on Principle of Diagnosis

Y2 - 13 September 2021 through 15 September 2021

ER -