# Robots in Partially Known and Unknown Environments:
## 5 Experiments and Results
### Adaptive Algorithm Analysis
#### 5.4.4 Results for Instances with Three or More Obstacles  
For the three or more obstacle instances, all the options were tested. The results obtained for 0% and 100% are the same for the critical and non-critical instances. The results obtained concerning the length for each instance are shown in Table 13.  
Each of the instances back_and_forth and claps have four obstacles. In these cases, all the obstacles are critical; hence, the difference between their results in the critical and non-critical instances is not significant. In these cases, the differences are more related to the map's structure. Despite this, it is possible to notice an improvement in the results as the percentage of known obstacles increases. Instances (e)-(h) have several obstacles. Some of these were identified as critical; hence, it is possible to notice differences in these cases. For the critical obstacles, it is possible to obtain paths of length closer to the ones obtained by the OfflineRMP algorithm. The results obtained concerning the percentage of re-planned paths for each instance are shown in Table 14.  
From this, it can be observed that, for the non-critical instances, the percentage of the re-planned route does not decrease significantly. This is because the available information was not related to the more important obstacles. Conversely, the percentage of re-planned routes decreases much more for the critical instances, requiring the OfflineRMP algorithm. Table 15 shows the average execution times for each instance. The execution times of AdaptiveRMP are low too. These execution times show the good performance of the algorithm. The highest computation times were reached with 100% of known obstacles, i.e., using OfflineRMP. Moreover, it can be noticed that the computation times of the critical instances increase to become closer to the OfflineRMP execution times. Conversely, the non-critical instances increase their execution times but not as much as the critical instances. This shows that the critical instances use the OnlineRMP algorithm much more. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{Non-Critical} & \multicolumn{4}{c}{Critical} \\
**ID** & **0\%** & **25\%** & **50\%** & **75\%** & **25\%** & **50\%** & **75\%** & **100\%** \\ \hline (a) & 658.1 \(\pm\) 1740.7 & 7165.9 \(\pm\) 1267.1 & 64  
The plots in Figures 20 and 21 show the average path lengths with their corresponding standard deviations found for different degrees of known objects of the map for critical and non-critical problem instances, respectively. A clear tendency to length reduction is observed in both sets of instances. The most noticeable differences can be observed for the complex instance with 50% known objects, where an in-depth analysis shows that the critical object kept in the critical case blocks most of the direct initial path. Figure 21: Length of route versus percentage of known objects: partial non-critical instances. Figure 20: Length of route versus percentage of known objects: partial critical instances. The plots in Figures 22 and 23 show the changes in re-planning percentages considering the different degrees of known objects studied for both the critical and non-critical problem instances, respectively. The number of re-plannings reduces as the percentage of known objects increases. In this case, the reductions are more evident for the critical problem instances, whereas, for the potholes and var_density instances, the re-plannings drop to zero. In Figure 24, the best results obtained by AdaptiveRMP for rockpile with 25%, 50%, and 75% for critical and non-critical instances are shown. In dark gray, we show the obstacles observed at the beginning of the process and in light gray the unknown obstacles. The blue and green dots show the starting and target positions, respectively. The blue lines show the paths obtained by OfflineRMP, while the orange corresponds to the path obtained by OnlineRMP.