Matteo Reggente

A statistical approach to gas distribution modelling with mobile robots - The Kernel DM+V algorithm

Gas distribution modelling constitutes an ideal application area for mobile robots, which - as intelligent mobile gas sensors - offer several advantages compared to stationary sensor networks. In this paper we propose the Kernel DM+V algorithm to learn a statistical 2-d gas distribution model from a sequence of localized gas sensor measurements. The algorithm does not make strong assumptions about the sensing locations and can thus be applied on a mobile robot that is not primarily used for gas distribution monitoring, and also in the case of stationary measurements. Kernel DM+V treats distribution modelling as a density estimation problem. In contrast to most previous approaches, it models the variance in addition to the distribution mean. Estimating the predictive variance entails a significant improvement for gas distribution modelling since it allows to evaluate the model quality in terms of the data likelihood. This offers a solution to the problem of ground truth evaluation, which has always been a critical issue for gas distribution modelling. Estimating the predictive variance also provides the means to learn meta parameters and to suggest new measurement locations based on the current model. We derive the Kernel DM+V algorithm and present a method for learning the hyper-parameters. Based on real world data collected with a mobile robot we demonstrate the consistency of the obtained maps and present a quantitative comparison, in terms of the data likelihood of unseen samples, with an alternative approach that estimates the predictive variance.

Towards environmental monitoring with mobile robots

In this paper we present initial experiments towards environmental monitoring with a mobile platform. A prototype of a pollution monitoring robot was set up which measures the gas distribution using an ldquoelectronic noserdquo and provides three dimensional wind measurements using an ultrasonic anemometer. We describe the design of the robot and the experimental setup used to run trials under varying environmental conditions. We then present the results of the gas distribution mapping. The trials which were carried out in three uncontrolled environments with very different properties: an enclosed indoor area, a part of a long corridor with open ends and a high ceiling, and an outdoor scenario are presented and discussed.

The Aeroflex: A Bicycle for Mobile Air Quality Measurements

Fixed air quality stations have limitations when used to assess peoples real life exposure to air pollutants. Their spatial coverage is too limited to capture the spatial variability in, e.g., an urban or industrial environment. Complementary mobile air quality measurements can be used as an additional tool to fill this void. In this publication we present the Aeroflex, a bicycle for mobile air quality monitoring. The Aeroflex is equipped with compact air quality measurement devices to monitor ultrafine particle number counts, particulate mass and black carbon concentrations at a high resolution (up to 1 second). Each measurement is automatically linked to its geographical location and time of acquisition using GPS and Internet time. Furthermore, the Aeroflex is equipped with automated data transmission, data pre-processing and data visualization. The Aeroflex is designed with adaptability, reliability and user friendliness in mind. Over the past years, the Aeroflex has been successfully used for high resolution air quality mapping, exposure assessment and hot spot identification.

Using local wind information for gas distribution mapping in outdoor environments with a mobile robot

In this paper we introduce a statistical method to build two-dimensional gas distribution maps (Kernel DM+V/W algorithm). In addition to gas sensor measurements, the proposed method also takes into account wind information by modeling the information content of the gas sensor measurements as a bivariate Gaussian kernel whose shape depends on the measured wind vector. We evaluate the method based on real measurements in an outdoor environment obtained with a mobile robot that was equipped with gas sensors and an ultrasonic anemometer for wind measurements. As a measure of the model quality we compute how well unseen measurements are predicted in terms of the data likelihood. The initial results are encouraging and show a clear improvement of the proposed method compared to the case where wind is not considered.

The DustBot system : using mobile robots to monitor pollution in pedestrian area

The EU project DustBot addresses urban hygiene. Two types of robots were designed, the DustClean robot to autonomously clean pedestrian areas, and the DustCart robot for door-to-door garbage collection. Three prototype robots were built and equipped with electronic noses so as to enable them to collect environmental data while performing their urban hygiene tasks. Essentially, the robots act as a mobile, wireless node in a sensor network. In this paper we give an overview of the DustBot platform focusing on the Air Monitoring Module (AMM). We describe the data flow between the robots through the ubiquitous network to a gas distribution modelling server, where a gas distribution model is computed. We describe the Kernel DM+V algorithm, an approach to create statistical gas distribution models in the form of predictive mean and variance discretized onto a grid map. Finally we present and discuss results obtained with the DustBot AMM during experimental trials performed in outdoor public places: a courtyard in Pontedera, Italy and a pedestrian square in Orebro, Sweden.

The 3D-Kernel DM+V/W algorithm: Using wind information in three dimensional gas distribution modelling with a mobile robot

In this paper we present a statistical method to build three-dimensional gas distribution maps from gas sensor and wind measurements obtained with a mobile robot in uncontrolled environments. The particular contribution of this paper is to introduce and evaluate an algorithm for 3D statistical gas distribution mapping, that takes into account airflow information. 3D-Kernel DM+V/W algorithm uses a multivariate Gaussian weighting function to model the information provided by the gas sensors and an ultrasonic anemometer. The proposed algorithm is evaluated with respect to the ability of the obtained models to predict unseen measurements. The results based on 15 trials with a mobile robot in an indoor environment show improvements in the model performance when using the 3D kernel DM+V/W algorithm. Moreover the model is able to adapt to the dynamical changes of the environment learning the hyper-parameter from the sensors readings.

Estimating predictive variance for statistical gas distribution modelling

Recent publications in statistical gas distribution modelling have proposed algorithms that model mean and variance of a distribution. This paper argues that estimating the predictive concentration variance entails not only a gradual improvement but is rather a significant step to advance the field. This is, first, since the models much better fit the particular structure of gas distributions, which exhibit strong fluctuations with considerable spatial variations as a result of the intermittent character of gas dispersal. Second, because estimating the predictive variance allows to evaluate the model quality in terms of the data likelihood. This offers a solution to the problem of ground truth evaluation, which has always been a critical issue for gas distribution modelling. It also enables solid comparisons of different modelling approaches, and provides the means to learn meta parameters of the model, to determine when the model should be updated or re-initialised, or to suggest new measurement locations based on the current model. We also point out directions of related ongoing or potential future research work.

Three-dimensional statistical gas distribution mapping in an uncontrolled indoor environment

In this paper we present a statistical method to build three‐dimensional gas distribution maps (3D‐DM). The proposed mapping technique uses kernel extrapolation with a tri‐variate Gaussian kernel that models the likelihood that a reading represents the concentration distribution at a distant location in the three dimensions. The method is evaluated using a mobile robot equipped with three “e‐noses” mounted at different heights. Initial experiments in an uncontrolled indoor environment are presented and evaluated with respect to the ability of the 3D map, computed from the lower and upper nose, to predict the map from the middle nose.

Gas source localization in indoor environments using multiple inexpensive robots and stigmergy

Environmental monitoring is a rather new field in robotics. One of the main appealing tasks is gas mapping, i.e., the characterization of the chemical properties (concentration, dispersion, etc.) of the air within an environment. Current approaches rely on a robot using standard localization and mapping techniques to fuse gas measures with spatial features. These approaches require sophisticated sensors and/or high computational resources. We propose a minimalistic approach, in which one or multiple low-cost robots exploit the ability to store information in the environment, or “stigmergy”, to effectively compute an artificial potential leading toward the likely location of the gas source, as indicated by a highest gas concentration or fluctuation. The potential is computed and stored directly on an array of RFID tags buried under the floor. Our approach has been validated in extensive experiments performed on real robots in a domestic environment.