## 1 Introduction  
With the predicted growth in e-commerce [1, 2, 3] and the increasing demand for efficient and sustainable logistics processes, delivery robots are expected to play a pivotal role in the future of supply chain management [4, 5, 6]. Consequently, a significant increase in the number of mobile robots handling package deliveries, both inside and outside buildings, should be expected [7, 8]. This trend extends to multi-story residential and office buildings, where packages must be transported indoors from central receiving areas to their final destinations. Leveraging an operational indoor robot for environmental monitoring offers significant advantages due to its ability to collect spatially and temporally dispersed data. The ability of mobile robots to cover large areas is essential for assessing environmental conditions and detecting potentially hazardous situations [9, 10]. Unlike stationary sensors, the use of sensors embedded in mobile robots can cover larger areas more efficiently, enabling a more comprehensive evaluation of environmental parameters [11, 12, 13]. The use of mobile sensors is crucial in applications such as pollution detection and gas source localization, where extensive spatial coverage is required to identify contamination sources accurately [14, 15, 16, 17, 18, 19, 20]. Furthermore, the importance of real-time data collection in environmental monitoring cannot be overstated. Embedding advanced sensors in mobile robots designed to develop other primary tasks, such as indoor parcel delivery, enables continuous and reliable environmental data to be obtained, which is essential for timely decision-making in environmental management and emergency response scenarios [21]. There are several application examples of mobile robots used for environmental monitoring. [PERSON] et al. [22] evaluated the performance of a ventilation system using a mobile robot equipped with carbon dioxide and volatile organic compound sensors, comparing the results with those obtained from a stationary sensor network. Similarly, [PERSON] et al. [23] proposed a method to localize and map carbon monoxide gas concentrations using an unmanned aerial vehicle (UAV) equipped with a specialized sensor array. Other researchers have further advanced this concept by developing UAVs specifically designed for natural gas and methane leak detection in transport pipelines, demonstrating the effectiveness of these vehicles for aerial inspections [24; 25; 26; 27; 28]. The gas sensors used in such applications are usually tailored to detect single specific chemical compounds and often come with significant drawbacks. In general, they are typically expensive, require complex calibration procedures, and have no application to detect a broad range of gases or complex odors composed of various volatile chemical compounds. In recent years, the development of low-cost gas sensors, such as metal oxide (MOX) semiconductor sensors, has garnered significant attention, particularly for applications in early gas leak detection [29; 30; 31] as a feasible alternative to traditional gas sensors. Table 1 compares some relevant factors of the different gas sensing technologies available on the market, including laser-based chromatography, catalytic combustion detectors, electrochemical sensors, photoionization detectors, and MOX gas sensors. Comparatively, the main advantages of MOX gas sensors are their compact size, low power requirements, and low cost, which make this sensing technology suitable for embedded device designs and battery-operated mobile applications [32]. However, MOX gas sensors also have known limitations such as their limit of detection [33; 34; 35], lack of selectivity, and cross-sensitivity to environmental factors such as humidity and temperature [36; 37; 38]. The combination of multiple gas sensors into a single device, commonly referred to as an electronic nose (also known as an eNose), has become a widely adopted approach in various gas detection applications [53; 54].