RT Journal Article
T1 Improving Bayesian inference efficiency for sensory anomaly detection and recovery in mobile robots.
A1 Castellano Quero, Manuel
A1 Fernández-Madrigal, Juan Antonio
A1 García-Cerezo, Alfonso José
K1 Estadística bayesiana
K1 Robots móviles - Diseño y construcción
AB For mobile robots to operate in real environments, it is essential that basic tasks such as localization, mapping and navigation are performed properly. These tasks strongly rely on an adequate perception of the environment, which may be challenging in some cases due to the nature of the scene itself, the limited operation of some sensors, or even both. A mobile robot should be able to intelligently identify and overcome abnormal situations efficiently in order to avoid sensory malfunctioning. We propose in this work a novel methodology based on Bayesian networks, which enables to naturally represent complex relationships among sensors, to integrate heterogeneous sources of knowledge, to deduct the presence of sensory anomalies, and finally to recover from them by using the available information. The high computational cost of inference is addressed by a new algorithm that takes advantage of our model structure. Our proposal has been assessed in several simulations and has also been tested in a real environment with a mobile robot. The obtained results show that it achieves better performance and accuracy compared to other existing methods, while enhancing the robustness of the whole sensory system.
PB Elsevier
YR 2021
FD 2021
LK https://hdl.handle.net/10630/38034
UL https://hdl.handle.net/10630/38034
LA eng
NO https://openpolicyfinder.jisc.ac.uk/id/publication/4628
NO This work has been supported by the through the national grant FPU16/02243, Malaga through its local research program Excellence Campus Andalucia Tech, and by project RTI2018-093421-B-100.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026