Communication Dans Un Congrès Année : 2024

A Hierarchical Monitoring and Diagnosis System for Autonomous Robots

Résumé

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.
Fichier principal
Vignette du fichier
OASIcs.DX.2024.1 copie.pdf (846) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04934737 , version 1 (07-02-2025)

Identifiants

Citer

Gerald Steinbauer-Wagner, Leo Fürbaß, Marco de Bortoli, Louise Travé-Massuyès. A Hierarchical Monitoring and Diagnosis System for Autonomous Robots. 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024), Nov 2024, Vienne, Austria. pp.1, ⟨10.4230/OASIcs.DX.2024.1⟩. ⟨hal-04934737⟩
0 Consultations
0 Téléchargements

Altmetric

Partager

More