2026-08-03T01:32:17Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:22093 2024-11-26T15:16:36Z ddc:004 open_access

A Hierarchical Monitoring and Diagnosis System for Autonomous Robots Steinbauer-Wagner, Gerald Fürbaß, Leo De Bortoli, Marco Travé-Massuyès, Louise Cognitive Architecture Autonomous Agents Dependability Hierarchical Monitoring and Diagnosis This paper addresses the capability of autonomous robots to achieve flexible goals in dynamic environments. In such a setting numerous challenges jeopardize the robustness of such systems. Thus, we propose a hierarchical diagnosis concept for layered control architectures, that can detect and deal with such challenges to maintain a consistent knowledge about the world and to allow reliable decision-making. Layered control systems use various knowledge representations and decision-making mechanisms teamed with specialized isolated fault-handling approaches. However, some issues can only be identified if the information from different layers is combined. Our approach addresses challenges like failing actions, uncertain observations, and unmodeled events by propagating observations and diagnoses results throughout the hierarchy. This enhances adaptability and dependability in various domains. In this paper, we present a prototype architecture following this approach. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Gerald Steinbauer-Wagner and Leo Fürbaß and Marco De Bortoli and Louise Travé-Massuyès 2024 Is Part Of OASIcs, Volume 125, 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.DX.2024.1 urn:nbn:de:0030-drops-220938 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2024.1 eng https://creativecommons.org/licenses/by/4.0/legalcode