## 4 Results of Early Detection of Ethanol Gas Leaks
### Ethanol Gas Leak Detection During an Indoor Parcel Delivery  
In this work, the information provided by the eNose embedded in the mobile delivery robot was registered at a rate of 0.5 samples/s for online and offline analysis. Figure 6 presents the evolution of the sensing resistance of the sixteen MOX gas sensors of the eNose during the delivery experiment presented in Figure 5. Additionally, Figure 6 highlights the data points gathered by the eNose that are inside Sections S1, S2, and S3 indicated on the mobile robot trajectory shown previously in Figure 5.  
Figure 6 also includes the projection of the data points inside the sections in the LDA space. Since each data point can measure Air or Ethanol and can be classified by the LDA projection either as Air or Ethanol, there are four possible classification outcomes: a sample expected to be Air and correctly classified as Air (blue dot with a blue border), a sample expected to be Air but misclassified as Ethanol (orange dot with a blue border), a sample expected to be Ethanol and correctly classified as Ethanol (orange dot with an orange border), a sample expected to be Ethanol but misclassified as Air (blue dot with an orange  
Figure 5: Trajectory of the indoor mobile robot during a package delivery projected over the 2D point cloud map of the floor of the building: (**a**) going from the Robotics Laboratory to the building’s reception center to load a package, and (**b**) transporting the package from the building’s reception center to the Robotics Laboratory. border). In summary, the color of the border represents the real class of the sample, and the internal color represents the result of the LDA classification. A closer look at the LDA classification results provides the following remarks. During subset 1 (S1), at the beginning of the gas measurement experiment, the mobile robot was in a clean Air environment and far from the Ethanol source. In this S1 region, all the samples gathered were projected into the Air region of the LDA projection and successfully classified as Air samples. Subset 2 (S2) comprises the gas samples collected before entering the room and while inside the room with the Ethanol source. In the S2 region, the samples gathered before entering the room were correctly classified as Air. When the robot entered the room the projection of the gas samples started transitioning to the Ethanol region. During this short period, the result of the LDA classification was Air although it must be Ethanol because the robot was close to the Ethanol source. A few seconds later, when the sensor stabilized, the result of the LDA classification was Ethanol as expected. Subset 3 (S3) includes the eNose measurements taken inside the room and while exiting the room with the Ethanol gas leak. In this situation, the mobile robot was moving away from the Ethanol source but still all samples were correctly classified as Ethanol. Finally, subset 4 (S4) contains the resistance values measured while returning to the Robotics Laboratory. At this point, the expectation was to detect Air, but the LDA classification suggested Ethanol for several seconds while the sensors were recovering. After some delay, the classification finally becomes Air. At this point, it must be noted that the visual interpretation of the projection of the eNose information on the LDA 2D space agrees with the physical interpretation of the  
Figure 6: Evolution of the information gathered by the eNose embedded in the delivery robot: (**Bottom**) evolution of the raw resistance values gathered from the sixteen MOX gas sensors of the eNose and (**Top**) LDA projection of the eNose data gathered in the subsets labeled as S1, S2, S3, and S4. results as the misclassifications are always produced in the boundary regions between the Air and Ethanol classes. It is also interesting to highlight that, despite the micro-dimensions of the MOX gas sensor, the response of the eNose still has some inertia.