9 research outputs found

    A multidimensional Bayesian architecture for real-time anomaly detection and recovery in mobile robot sensory systems

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    peer reviewedFor 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 ‚ČÉ20% to ‚ČÉ500%

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

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    The thesis contributes with a novel modeling paradigm, a so-called Bayesian sensory architecture, that enables the representation of any robotic sensory system, allowing the identification of anomalies and the recovery from them. The main drawback of this proposal is the potentially high computational cost of inference with Bayesian networks, which is addressed with a novel, approximate algorithm that leverages the structure of the proposed model. Both the sensory architecture and the corresponding inference algorithm are implemented for different robotic tasks, and are validated through different sets of both simulated and real experiments. One of the implementations is aimed at analyzing the performance of the proposed algorithm in terms of error and computation time. The results obtained from the experiments show that the cost of inference is significantly reduced, and that the approximate queries produced still serve to perform sensory diagnosis and recovery adequately. Another implementation is proposed for the problem of robotic navigation in human environments. In this case, the experimental results prove that the use of the architecture manages to increase the safety and efficiency of navigation. Lastly, a new inference approach based on the use of feedforward neural networks is implemented and tested for this problem, showing that it is possible to reduce, even more, the cost of inference with Bayesian networks, enabling real time operation.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

    Statistical Study of the Performance of Recursive Bayesian Filters with Abnormal Observations from Range Sensors

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    Range sensors are currently present in countless applications related to perception of the environment. In mobile robots, these devices constitute a key part of the sensory apparatus and enable essential operations, that are often addressed by applying methods grounded on probabilistic frameworks such as Bayesian filters. Unfortunately, modern mobile robots have to navigate within challenging environments from the perspective of their sensory devices, getting abnormal observations (e.g., biased, missing, etc.) that may compromise these operations. Although there exist previous contributions that either address filtering performance or identification of abnormal sensory observations, they do not provide a complete treatment of both problems at once. In this work we present a statistical approach that allows us to study and quantify the impact of abnormal observations from range sensors on the performance of Bayesian filters. For that, we formulate the estimation problem from a generic perspective (abstracting from concrete implementations), analyse the main limitations of common robotics range sensors, and define the factors that potentially affect the filtering performance. Rigorous statistical methods are then applied to a set of simulated experiments devised to reproduce a diversity of situations. The obtained results, which we also validate in a real environment, provide novel and relevant conclusions on the effect of abnormal range observations in these filters

    Towards low-level diagnosis and recovery of robotic sensors through inference with Bayesian networks

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    [Resumen] Las implementaciones existentes en la actualidad sobre sensores virtuales no emplean un marco com√ļn matem√°ticamente riguroso. Por ello, en este trabajo tenemos como objetivo homogeneizar el soporte te√≥rico de los sensores virtuales a bajo nivel, es decir, tratando directamente sus datos en bruto, de tal modo que puedan ser empleados en diagn√≥stico de fallos, recuperaci√≥n de datos y otras funcionalidades sin cambiar el paradigma de base. La inferencia bayesiana constituye una manera gen√©rica y rigurosa de abordar este problema; adem√°s, nos permite integrar conocimiento procedente de diversas fuentes (los propios dispositivos sensoriales, sentido com√ļn humano, datos del entorno, etc.) y se puede hibridar con otras metodolog√≠as como las redes neuronales o la l√≥gica borrosa. Dado que el potencial de esta soluci√≥n es considerablemente amplio, nos centramos aqu√≠ en el diagn√≥stico de aver√≠as, recuperaci√≥n de datos y funcionalidades de integraci√≥n de conocimiento externo. Nuestros resultados con un robot m√≥vil real equipado con dos sensores de proximidad y con otros dispositivos m√°s simples, demuestran que este marco tiene muchas posibilidades de mejorar el sistema sensorial de un robot por medio de t√©cnicas de razonamiento de alto nivel.[Abstract] Existing implementations for virtual sensors do not use a common, rigorous mathematical framework. In this work we aim to homogenize the theoretical support of virtual sensors at a low level, i.e., dealing with their raw data directly in such a way that they can be employed for fault diagnosis, data recovery and other functionalities without changing the base paradigm. Bayesian inference provides a general and principled way of addressing this; moreover, it allows us to integrate knowledge from diverse sources (the sensor devices themselves, human commonsense, environmental data, etc.) and could be hybridized with other approaches, such as neural networks or fuzzy logic. Since the potential of this solution is considerably wide, here we focus on the fault diagnosis, data recovery and external knowledge integration functionalities. Our results with a real mobile robot equipped with two rangefinder sensors and also common, simpler devices demonstrate that the framework has many possibilities for improving the sensory system of a mobile robot through high-level reasoning techniques.Ministerio de Ciencia, Innovaci√≥n y Universidades; FPU16/02243Ministerio de Econom√≠a, Industria y Competitividad; DPI2015-65186-

    Statistical Study of the Performance of Recursive Bayesian Filters with Abnormal Observations from Range Sensors.

    No full text
    Range sensors are currently present in countless applications related to perception of the environment. In mobile robots, these devices constitute a key part of the sensory apparatus and enable essential operations, that are often addressed by applying methods grounded on probabilistic frameworks such as Bayesian filters. Unfortunately, modern mobile robots have to navigate within challenging environments from the perspective of their sensory devices, getting abnormal observations (e.g., biased, missing, etc.) that may compromise these operations. Although there exist previous contributions that either address filtering performance or identification of abnormal sensory observations, they do not provide a complete treatment of both problems at once. In this work we present a statistical approach that allows us to study and quantify the impact of abnormal observations from range sensors on the performance of Bayesian filters. For that, we formulate the estimation problem from a generic perspective (abstracting from concrete implementations), analyse the main limitations of common robotics range sensors, and define the factors that potentially affect the filtering performance. Rigorous statistical methods are then applied to a set of simulated experiments devised to reproduce a diversity of situations. The obtained results, which we also validate in a real environment, provide novel and relevant conclusions on the effect of abnormal range observations in these filters

    Hacia la diagnosis y fusión de sensores robóticos a bajo nivel mediante inferencia en redes bayesianas

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    Las implementaciones existentes en la actualidad sobre sensores virtuales no emplean un marco comun matematicamente riguroso. Por ello, en este trabajo tenemos como objetivo homogeneizar el soporte teorico de los sensores virtuales a bajo nivel, es decir, tratando directamente sus datos en bruto, de tal modo que puedan ser empleados en diagnostico de fallos, recuperacion de datos y otras funcionalidades sin cambiar el paradigma de base. La inferencia bayesiana constituye una manera gen ŐĀerica y rigurosa de abordar este problema; ademas, nos permite integrar conocimiento procedente de diversas fuentes (los propios dispositivos sensoriales, sentido comun humano, datos del entorno, etc.) y se puede hibridar con otras metodolog ŐĀńĪas como las redes neuronales o la logica borrosa. Dado que el potencial de esta solucion es considerablemente amplio, nos centramos aqu ŐĀńĪ en el diagnostico de aver ŐĀńĪas, recuperacion de datos y funcionalidades de integracion de conocimiento externo. Nuestros resultados con un robot movil real equipado con dos sensores de proximidad y con otros dispositivos mas simples, demuestran que este marco tiene muchas posibilidades de mejorar el sistema sensorial de un robot por medio de t ŐĀecnicas de razonamiento de alto nivel.Universidad de M√°laga. Campus de Excelencia Internacional Andaluc√≠a Tech

    A multidimensional Bayesian architecture for real-time anomaly detection and recovery in mobile robot sensory systems

    No full text
    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 ...Funding for open access charge: Universidad de M√°laga/CBU
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