# Automated Destination Renewal Process for Location-Based Robot Erands
## 4 Results and Discussion
(2014), who achieved a 67.7% accuracy using a weighted average with an RGB camera, our experiment resulted in a 63.26% accuracy. It is especially important to note that [PERSON]'s approach constructed a semantic set and restricted the results to the environment where the experiment was conducted. In contrast, our experiment did not specify a semantic set for the space, leading to a relatively lower accuracy. However, it was confirmed that environmental configuration and performance improvement for hierarchical spatial classification are still needed. After classifying and labeling each space, we conducted an errand scenario to retrieve a cup from one of the spaces. Specifically, we introduced a chair that was not present during the initial data collection step and tested whether node updates were performed smoothly based on the changes in information about surrounding obstacles. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Environment**} & \multicolumn{2}{c}{**Places205 Dataset**} & \multicolumn{2}{c}{**Engineering Building Image Set**} \\ \cline{2-5}  & **Top-1 Accuracy** & **Top-5 Accuracy** & **Top-1 Accuracy** & **Top-5 Accuracy** \\ \hline Conference room & 53.77\% & 86.21\% & 50.00\% & 85.71\% \\ Corridor & 83.92\% & 97.80\% & 63.83\% & 91.49\% \\ Hospital room & 65.21\% & 86.41\% & 66.67\% & 100.00\% \\ Office & 38.73\% & 83.74\% & 18.18\% & 68.18\% \\ Pantry & 65.59\% & 85.15\% & 40.00\% & 100.00\% \\ Reception & 43.24\% & 79.86\% & 50.00\% & 75.00\% \\ Staircase & 62.30\% & 85.73\% & 57.14\% & 100.00\% \\ \hline Average & 63.26\% & 87.38\% & 51.79\% & 88.65\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy results for space classification in indoor environments. Figure 9: Experimental results of space classification: (**a**) conference room, (**b**) corridor, (**c**) pantry, (**d**) hospital room, (**e**) office, and (**f**) reception. Figure 11 presents the results of the node update experiment after recognizing the location of the errand target object. The light green area represents the workspace where the mobile robot can interact with the object, taking into account the operating radius of the manipulator attached to the mobile robot. The light red area represents the area where the mobile robot may collide with surrounding obstacles, considering the radius of the mobile robot. In Figure 11a, the original nodes (red dots) indicate the selected positions for manipulating the object (cup) in the environment. When obstacles (chairs) are placed such that they overlap with the node's position, as shown in Figure 11b, it can be observed that the node's position is altered due to the presence of these obstacles. Through this experiment, we confirmed that when obstacles deviated from the previously explored information or moved in real-time, the system automatically explored and selected new working positions to reflect these changes. Figure 11: Example of node update of errand destination according to obstacle change: (**a**) map without a chair, (**b**) map with a chair. Figure 10: Experimental results of space segmentation and classification in each space.