Combining non selective gas sensors on a mobile robot for identification and mapping of multiple chemical compounds

In this paper, we address the task of gas distribution modeling in scenarios where multiple heterogeneous compounds are present. Gas distribution modeling is particularly useful in emission monitoring applications where spatial representations of the gaseous patches can be used to identify emission hot spots. In realistic environments, the presence of multiple chemicals is expected and therefore, gas discrimination has to be incorporated in the modeling process. The approach presented in this work addresses the task of gas distribution modeling by combining different non selective gas sensors. Gas discrimination is addressed with an open sampling system, composed by an array of metal oxide sensors and a probabilistic algorithm tailored to uncontrolled environments. For each of the identified compounds, the mapping algorithm generates a calibrated gas distribution model using the classification uncertainty and the concentration readings acquired with a photo ionization detector. The meta parameters of the proposed modeling algorithm are automatically learned from the data. The approach was validated with a gas sensitive robot patrolling outdoor and indoor scenarios, where two different chemicals were released simultaneously. The experimental results show that the generated multi compound maps can be used to accurately predict the location of emitting gas sources

Drones and Sensors Ecosystem to Maximise the “Storm Effects” in Case of CBRNe Dispersion in Large Geographic Areas

The advancements in the field of robotics, specifically in the aerial robotics, combined with technological improvements of the capability of drones, have increased dramatically the use of these devices as a valuable tool in a wide range of applications. From civil to commercial and military area, the requirements in the emerging application for monitoring complex scenarios that are potentially dangerous for operators give rise to the need of a more powerful and sophisticated approach. This work aims at proposing the use of swarm drones to increase plume detection, tracking and source declaration for chemical releases. The several advantages which this technology may lead to this research and application fields are investigated, as well as the research and technological activities to be performed to make swarm drones efficient, reliable, and accurate

Classification of Two Volatiles Using an eNose Composed by an Array of 16 Single-Type Miniature Micro-Machined Metal-Oxide Gas Sensors

The artificial replication of an olfactory system is currently an open problem. The development of a portable and low-cost artificial olfactory system, also called electronic nose or eNose, is usually based on the use of an array of different gas sensors types, sensitive to different target gases. Low-cost Metal-Oxide semiconductor (MOX) gas sensors are widely used in such arrays. MOX sensors are based on a thin layer of silicon oxide with embedded heaters that can operate at different temperature set points, which usually have the disadvantages of different volatile sensitivity in each individual sensor unit and also different crossed sensitivity to different volatiles (unspecificity). This paper presents and eNose composed by an array of 16 low-cost BME680 digital miniature sensors embedding a miniature MOX gas sensor proposed to unspecifically evaluate air quality. In this paper, the inherent variability and unspecificity that must be expected from the 16 embedded MOX gas sensors, combined with signal processing, are exploited to classify two target volatiles: ethanol and acetone. The proposed eNose reads the resistance of the sensing layer of the 16 embedded MOX gas sensors, applies PCA for dimensional reduction and k-NN for classification. The validation results have shown an instantaneous classification success higher than 94% two days after the calibration and higher than 70% two weeks after, so the majority classification of a sequence of measures has been always successful in laboratory conditions. These first validation results and the low-power consumption of the eNose (0.9 W) enables its future improvement and its use in portable and battery-operated applications

Airborne chemical sensing with mobile robots

Airborne chemical sensing with mobile robots has been an active research areasince the beginning of the 1990s. This article presents a review of research work in this field,including gas distribution mapping, trail guidance, and the different subtasks of gas sourcelocalisation. Due to the difficulty of modelling gas distribution in a real world environmentwith currently available simulation techniques, we focus largely on experimental work and donot consider publications that are purely based on simulations

Enhancement of the Sensory Capabilities of Mobile Robots through Artificial Olfaction

La presente tesis abarca varios aspectos del olfato artificial u olfato robótico, la capacidad de percibir información sobre la composición del aire que rodea a un sistema automático. En primer lugar, se desarrolla una nariz electrónica, un instrumento que combina sensores de gas de bajas prestaciones con un algoritmo de clasificación para medir e identificar gases. Aunque esta tecnología ya existía previamente, se aplica un nuevo enfoque que busca reducir las dimensiones y consumo para poder instalarlas en robots móviles, a la vez que se aumenta el número de gases detectables mediante un diseño modular. Posteriormente, se estudia la estrategia óptima para encontrar fugas de gas con un robot equipado con este tipo de narices electrónicas. Para ello se llevan a cabos varios experimentos basados en teleoperación para entender como afectan los sensores del robot al éxito de la tarea, de lo cual se deriva finalmente un algoritmo para generar con robots autónomos mapas de gas de un entorno dado, el cual se inspira en el comportamiento humano, a saber, maximizar la información conocida sobre el entorno. La principal virtud de este método, además de realizar una exploración óptima del entorno, es su capacidad para funcionar en entornos muy complejos y sujetos a corrientes de vientos mediante un nuevo método que también se presenta en esta tesis. Finalmente, se presentan dos casos de aplicación en los que se identifica de forma automática con una nariz electrónica la calidad subjetiva del aire en entornos urbanos

A Robotic System for In-Situ Measurement of Soil Total Carbon and Nitrogen

Surges in the cost of fertilizer in recent times coupled with the environmental effects of their over-application have driven the need for farmers to optimize the amount of fertilizer they apply on the farm. One of the key steps in determining the right amount of fertilizer to apply in a given field is measuring the amount of nutrients present in the soil. To ascertain nutrient deficiencies, most farmers perform wet chemistry analysis of soil samples which requires a lot of time and is expensive. In this research project, a robotic system was designed and developed that could autonomously move to predetermined GPS waypoints and estimate total carbon (TC) and total nitrogen (TN) content in the soil in-situ using visible and near-infrared reflectance spectroscopy - a faster and cheaper method to determine soil nutrients in real-time. For the locomotion of the robotic system, a Husky robotic platform by Clearpath Robotics was used. A Gen2 robotic arm by Kinova Robotics was used for the precise positioning of the probe in taking soil spectral measurement. The probe was custom designed and built to be used in conjunction with the robotic arm as an end-effector. Two lightweight and inexpensive spectrometers by OceanInsight, namely, Flame VisNIR and Flame NIR+, were used to capture the spectral signatures of soil. The prediction was done with a spectroscopic calibration model and External Parameter Orthogonalization (EPO) was applied to remove the moisture effect from the soil spectra. The robotic system was tested at University of Nebraska-Lincoln (UNL) NU-Spidercam phenotyping facility. Two sets of spectra were obtained from the field campaign: in-situ and dry-ground spectra. The dry-ground spectra were used as library scans and Partial Least Square Regression (PLSR) was used for the modeling. The in-situ spectra were randomly divided into EPO calibration and validation sets. Satisfactory results were obtained from the initial prediction on dry-ground validation set, with R2 (coefficient of determination) of 0.77 and RMSE (Root Mean Squared Error) of 0.15% for TC and R2 of 0.64 and RMSE of 171 ppm for TN. There was a reduction in R2 and an increase in RMSE values for both TC and TN when prediction was done directly on the in-situ validation set. For TC, the R2 dropped and RMSE increased to 0.25 and 0.29% respectively, and for TN, the R2 dropped and RMSE increased to 0.19 and 259 ppm respectively. This was primarily due to the presence of moisture in the field samples. The R2 increased to 0.62 and RMSE decreased to 0.2% for TC, and the R2 increased to 0.51 and RMSE decreased to 200 ppm for TN, when EPO was applied on both the in-situ validation and dry-ground sets. These findings highlight the importance of accounting for moisture effects in the prediction of soil properties using the robotic system and demonstrate the potential of the system in enabling soil monitoring and analysis in-situ. Advisor: Yufeng G

Electronic noses for environmental monitoring applications

Electronic nose applications in environmental monitoring are nowadays of great interest, because of the instruments’ proven capability of recognizing and discriminating between a variety of different gases and odors using just a small number of sensors. Such applications in the environmental field include analysis of parameters relating to environmental quality, process control, and verification of efficiency of odor control systems. This article reviews the findings of recent scientific studies in this field, with particular focus on the abovementioned applications. In general, these studies prove that electronic noses are mostly suitable for the different applications reported, especially if the instruments are specifically developed and fine-tuned. As a general rule, literature studies also discuss the critical aspects connected with the different possible uses, as well as research regarding the development of effective solutions. However, currently the main limit to the diffusion of electronic noses as environmental monitoring tools is their complexity and the lack of specific regulation for their standardization, as their use entails a large number of degrees of freedom, regarding for instance the training and the data processing procedures