Abstract
This paper proposes the application of a low-cost gas sensor array in an assistant personal robot (APR) in order to extend the capabilities of the mobile robot as an early gas leak detector for safety purposes. The gas sensor array is composed of 16 low-cost metal-oxide (MOX) gas sensors, which are continuously in operation. The mobile robot was modified to keep the gas sensor array always switched on, even in the case of battery recharge. The gas sensor array provides 16 individual gas measurements and one output that is a cumulative summary of all measurements, used as an overall indicator of a gas concentration change. The results of preliminary experiments were used to train a partial least squares discriminant analysis (PLS-DA) classifier with air, ethanol, and acetone as output classes. Then, the mobile robot gas leak detection capabilities were experimentally evaluated in a public facility, by forcing the evaporation of (1) ethanol, (2) acetone, and (3) ethanol and acetone at different locations. The positive results obtained in different operation conditions over the course of one month confirmed the early detection capabilities of the proposed mobile system. For example, the APR was able to detect a gas leak produced inside a closed room from the external corridor due to small leakages under the door induced by the forced ventilation system of the building.
Highlights
The combination of mobile robots and environmental sensors enables the automatic supervision of environmental parameters in large areas, for example, to reduce energy consumption or guarantee human comfort
This paper proposes the application of an array of MOX gas sensors as a cost-affordable sensor embedded in an assistant personal robot (APR) initially designed to provide telepresence [28], transport of small objects, and other assistive services [29]
The main goal of the first measurement campaign was to confirm that the sensors had a limit of detection (LOD) good enough to have a sufficient response to ethanol and acetone in the different scenarios, and to build a partial least squares discriminant analysis (PLS-DA) classifier
Summary
The combination of mobile robots and environmental sensors enables the automatic supervision of environmental parameters in large areas, for example, to reduce energy consumption or guarantee human comfort. Palacín et al [2] showed the automatic supervision of temperature, humidity, and luminance to evaluate human comfort in largely frequented areas and to optimize the energy spent in heating, ventilation, and air conditioning (HVAC). Rossi et al [7] proposed a fully autonomous UAV gas sensing system based on the use of metal-oxide (MOX) sensors with an autonomy of 30 minutes, capable of providing wireless real-time feedback. Rossi et al [9] proposed a battery-powered, lightweight, and compact gas sensing board based on the use of two MOX sensors, which was suitable for any type of mobile carrier such as UAVs or wheeled robots
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