Bayesian networks; Fault diagnosis; Mobile robots; Sensors; Bayesia n networks; Bayesian; Condition; Fault detection and diagnosis; Faults diagnosis; Probabilistic inference; Real time performance; Real-time anomaly detections; Robotic systems; Sensory system; Control and Systems Engineering; Artificial Intelligence; Electrical and Electronic Engineering
Résumé :
[en] For mobile robots to operate in an autonomous and safe manner they must be able to adequately perceive their environment despite challenging or unpredictable conditions in their sensory apparatus. Usually, this is addressed through ad-hoc, not easily generalizable Fault Detection and Diagnosis (FDD) approaches. In this work, we leverage Bayesian Networks (BNs) to propose a novel probabilistic inference architecture that provides generality, rigorous inferences and real-time performance for the detection, diagnosis and recovery of diverse and multiple sensory failures in robotic systems. Our proposal achieves all these goals by structuring a BN in a multidimensional setting that up to our knowledge deals coherently and rigorously for the first time with the following issues: modeling of complex interactions among the components of the system, including sensors, anomaly detection and recovery; representation of sensory information and other kinds of knowledge at different levels of cognitive abstraction; and management of the temporal evolution of sensory behavior. Real-time performance is achieved through the compilation of these BNs into feedforward neural networks. Our proposal has been implemented and tested for mobile robot navigation in environments with human presence, a complex task that involves diverse sensor anomalies. The results obtained from both simulated and real experiments prove that our architecture enhances the safety and robustness of robotic operation: among others, the minimum distance to pedestrians, the tracking time and the navigation time all improve statistically in the presence of anomalies, with a diversity of changes in medians ranging from ≃20% to ≃500%.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
Castellano-Quero, Manuel ; Systems Engineering and Automation Department, University of Málaga, Málaga, Spain
Castillo-López, Manuel; Automation and Robotics Research Group, Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg
Fernández-Madrigal, Juan-Antonio; Systems Engineering and Automation Department, University of Málaga, Málaga, Spain
Arévalo-Espejo, Vicente; Systems Engineering and Automation Department, University of Málaga, Málaga, Spain
VOOS, Holger ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Automation
García-Cerezo, Alfonso; Systems Engineering and Automation Department, University of Málaga, Málaga, Spain
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
A multidimensional Bayesian architecture for real-time anomaly detection and recovery in mobile robot sensory systems
Date de publication/diffusion :
octobre 2023
Titre du périodique :
Engineering Applications of Artificial Intelligence
This work has been supported by the Spanish government through the national grant FPU16/02243 , by the University of Málaga, Spain through its local research program and the International Excellence Campus Andalucía Tech, Spain , and by the Spanish national research project RTI2018-093421-B-100 . The authors would also like to thank the anonymous reviewers for their valuable comments and suggestions, which have greatly helped to improve the quality of the paper.
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