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• A real-time gas distribution mapping method, called GW-GMRF, is proposed. • This method estimates simultaneously a gas and a wind map for unexplored areas. • Each gas map has an associated uncertainty. • Very few observations can lead to reliable and accurate estimates. • Several experiments and comparisons with other methods are presented. This work addresses 2D gas and wind distribution mapping with a mobile robot for real-time applications. Our proposal seeks to estimate how gases released in the environment are distributed from a set of sparse and uncertain gas-concentration and wind-flow measurements; such that by exploiting the high correlation between these two magnitudes we may extrapolate their value for unexplored areas. Furthermore, because the air currents are completely conditioned by the environment, we assume a priori knowledge of static elements such as walls and obstacles when estimating both distribution maps. In particular, this joint estimation problem is modeled as a multivariate Gaussian Markov random field (GMRF), combining gas and wind observations under a common maximum a posteriori estimation problem. It considers two lattices of cells (a scalar gas-concentration field and a wind vector field) which are correlated following the physical laws of gas dispersal and fluid dynamics. Finally, we report various experiments in which our proposal is compared to other stochastic gas and gas-wind modeling methods under simulation, to evaluate their performance against a computer fluid-dynamics generated ground-truth, as well as under real and uncontrolled conditions. [ABSTRACT FROM AUTHOR]

This work deals with the problem of volatile chemical classification with an electronic nose (e-nose), and particularly focuses on the case where the e-nose is not collecting samples in a stationary fashion but is carried by a moving platform (mobile robot, car, bike, etc.). We bring to light that, under these specific circumstances, substantial changes in the transient response of the gas sensors arise (something that has not been considered until now). We experimentally demonstrate that these changes in the sensor's response have an important impact on the classification accuracy if not properly considered, resulting in a decrease of up to 30% in some configurations. We back our conclusions with an extensive experimental evaluation consisting of a mobile robot inspecting a long indoor corridor with two chemical volatiles sources (ethanol and acetone) more than 240 times, at four different motion speeds. The paper also reveals the relevance of training the classifiers with data collected in motion, and proposes different training schemes suitable to this problem. [ABSTRACT FROM AUTHOR]

Abstract: Gas quantification based on the response of an array of metal oxide (MOX) gas sensors in an Open Sampling System is a complex problem due to the highly dynamic characteristic of turbulent airflow and the slow dynamics of the MOX sensors. However, many gas related applications require to determine the gas concentration the sensors are being exposed to. Due to the chaotic nature that dominates gas dispersal, in most cases it is desirable to provide, together with an estimate of the mean concentration, an estimate of the uncertainty of the prediction. This work presents a probabilistic approach for gas quantification with an array of MOX gas sensors based on Gaussian Processes, estimating for every measurement of the sensors a posterior distribution of the concentration, from which confidence intervals can be obtained. The proposed approach has been tested with an experimental setup where an array of MOX sensors and a Photo Ionization Detector (PID), used to obtain ground truth concentration, are placed downwind with respect to the gas source. Our approach has been implemented and compared with standard gas quantification methods, demonstrating the advantages when estimating gas concentrations. [Copyright &y& Elsevier]

The Industry 4.0 paradigm is being increasingly adopted in the production, distribution and commercialization chains worldwide. The integration of the cutting-edge techniques behind it entails a deep and complex revolution – changing from scheduled-based processes to smart, reactive ones – that has to be thoroughly applied at different levels. Aiming to shed some light on the path towards such evolution, this work presents an Industry 4.0 based approach for facing a key aspect within factories: the health assessment of critical assets. This work is framed in the context of the innovative project SiMoDiM , which pursues the design and integration of a predictive maintenance system for the stainless steel industry. As a case of study, it focuses on the machinery involved in the production of high-quality steel sheets, i.e. the Hot Rolling Process , and concretely on predicting the degradation of the drums within the heating coilers of Steckel mills (parts with an expensive replacement that work under severe mechanical and thermal stresses). This paper describes a predictive model based on a Bayesian Filter , a tool from the Machine Learning field, to estimate and predict the gradual degradation of such machinery, permitting the operators to make informed decisions regarding maintenance operations. For achieving that, the proposed model iteratively fuses expert knowledge with real time information coming from the hot rolling processes carried out in the factory. The predictive model has been fitted and evaluated with real data from ∼ 118k processes, proving its virtues for promoting the Industry 4.0 era. • A Bayesian Filter is proposed for predicting the degradation state of machinery. • Such a model iteratively fuses expert knowledge with real time data from the factory. • A set of recipes for analyzing and processing plant and processes data are provided. • As a case of study, it is applied to the machinery within the Hot Rolling process. • The proposal has been assessed with data from +118k processes from a real factory. [ABSTRACT FROM AUTHOR]

Abstract Object recognition is a cornerstone task in autonomous and/or assistance systems like robots, autonomous vehicles, or those assisting to visually impaired, aiming to achieve a certain level of understanding of their surroundings. Probabilistic models, such as Conditional Random Fields (CRFs), have been successfully applied to this end given their ability to exploit contextual and situation information, e.g. a bowl is typically found in a cabinet and not in a night-stand. In this work we propose to evolve CRFs into Ontology-based Conditional Random Fields (ob CRFs), which define a multi-level structure where each level assigns a category with different granularity to the same set of objects. For example, a level could assign to an object the category appliance or furniture , while the next one could categorize it into the tv , microwave , cabinet , or table types. In this way, general categorizations can guide the classification into more specialized ones (and vice versa), improving recognition success, and mitigating the CRFs limitations when modeling a high number of object categories (shared, in general, by Machine Learning techniques). To set the categories in each level we propose to mimic the hierarchical structure of ontologies, where categories are naturally codified following a subsumption ordering. This leads us to the second advantage of ob CRFs : the multi-labeling of objects provides a richer understanding of the scene, which can be leveraged for accomplishing high-level tasks (e.g. object search or scheduling). Our approach has been tested with scenes from two state-of-the-art datasets: Robot@Home and Cornell-RGBD , outperforming the results provided by standard CRFs. Highlights • A novel model for object recognition called Ontology-based CRF is proposed. • It uses a multiple-level structure mimicking the subsumption ordering of Ontologies. • Each level jointly categorizes the same set of objects with different granularity. • Granularity ranges from specialized types (oven, fridge) to general ones (appliance). • The proposal has been assessed with the Robot@Home and Cornell-RGBD datasets. [ABSTRACT FROM AUTHOR]

In an ageing society, the at-home use of Socially Assistive Robots (SARs) could provide remote monitoring of their users' well-being, together with physical and psychological support. However, private home environments are particularly challenging for SARs, due to their unstructured and dynamic nature which often contributes to robots' failures. For this reason, even though several prototypes of SARs for elderly care have been developed, their commercialisation and wide-spread at-home use are yet to be effective. In this paper, we analyse how including the end users' feedback impacts the SARs reliability and acceptance. To do so, we introduce a Monitoring and Logging System (MLS) for remote supervision, which increases the explainability of SAR-based systems deployed in older adults' apartments, while also allowing the exchange of feedback between caregivers, technicians, and older adults. We then present an extensive field study showing how long-term deployment of autonomous SARs can be accomplished by relying on such a feedback loop to address any potential issue. To this end, we provide the results obtained in a 130-week long study where autonomous SARs were deployed in the apartments of 10 older adults, with the aim of possibly serving and assisting future practitioners, with the knowledge collected from this extensive experimental campaign, to fill the gap that currently exists for the widespread adoption of SARs. • Socially assistive robots are promising tools for older adults independent living. • Private apartments are challenging environments, impacting the robustness of robots. • Results from a 130 weeks study in 10 older adults apartments using autonomous robots. • Remote supervision increased the acceptance of socially assistive robots. • Lessons learned from managing a long-term deployment, towards long-term autonomy. [ABSTRACT FROM AUTHOR]