Inference Over Bayesian Networks for the Diagnosis and Sensory Enhancement of Mobile Robots

Abstract

Mobile robots are nowadays present in countless real-world applications, aiding or substituting human beings in a wide variety of tasks related to scopes as diverse as industrial, military, medical, educational and many others. The use of mobile platforms in all these contexts is revolutionizing their respective fields, overcoming previous limitations and offering new possibilities. However, for a mobile robot to work properly, it is essential that its sensory apparatus provides correct and reliable information, which is often challenging due to the complexity of the physical world and its uncertain nature. To address that, this thesis explores the possibilities of the application of Bayesian networks (BNs) to the problem of sensory diagnosis and enhancement in the context of mobile robotics. Arised from the realm of artificial intelligence, Bayesian networks constitute a rigorous mathematical framework that enables both the integration of heterogeneous sources of information and the reasoning about them while taking their uncertainty into account. The thesis first analyzes different sensory anomalies in mobile robots and the impact of such abnormal behavior on the performance of these platforms. Given the wide variety of existing sensory devices, the analysis is focused on range sensors, since they are essential to many robotic tasks also grounded on probabilistic frameworks such as Bayesian estimators. Specifically, the thesis contributes with a rigorous statistical study of the influence of abnormal range observations on the performance of Bayesian filters, addressing the problem from a generic perspective thanks to the use of BNs. The conclusions obtained serve to illustrate the importance of sensory abnormalities beyond the pervasively studied issue of noisy observations. The treatment of sensory anomalies in mobile robots with Bayesian networks is then addressed.