# Robots in Partially Known and Unknown Environments:
## 5 Experiments and Results
### OnlineRMP Analysis  
To analyze the performance of the OnlineRMP approach, the instances described in Section 5.1 will be used. It is expected that OfflineRMP achieves better results in terms of length and smoothness given the amount of information received at the beginning and the use of post-processing, thus enabling a higher degree of freedom. OnlineRMP is expected to have shorter execution times to be able to quickly re-plan the remaining path in real time. While searching for a solution path, the robot gathers information about the environment through its sensors. This information enables path modifications if the robot is close to colliding. The new information the robot gathers is only that related to critical obstacles, those that are in the direct path from starting to target positions; this is why the information obtained from the obstacles is minimal. In Figure 15, the solution of a test instance is shown, obstacles are shown in gray, and the solution path is shown in red. In Figure 15a, all the obstacles are shown, while, in Figure 15b, only the information gathered by the robot during the execution is shown. At each execution of OnlineRMP, the robot decides the surrounding direction of each obstacle, left or right. Table 9 shows the results of OnlineRMP for each instance, average length, standard deviation, average time after thirty runs, and the average percentage of discovered obstacles by the robot. High standard deviations were obtained in all instances. This is due to the decisions that OnlineRMP has to take using minimal information on the geometry of the obstacles. A wrong decision can lead to a much higher path length. Each decision is randomly taken given the minimum information known at the moment. The execution times are around 0.3 s. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Instance** & **nPoints** & **RRT Length** & **SA Length** & **SA + PP Length** & **SA + PP Smoothness** & **Iteration** \\ \hline _back\_and\_forth_ & 40 \(\pm\) 2 & 6189 \(\pm\) 338 & 5149 \(\pm\) 35 & 4810 \(\pm\) 13 & 108 \(\pm\) 3 & 592 \(\pm\) 254 \\ bugtrap1 & 9 \(\pm\) 1 & 1039 \(\pm\) 131 & 776 \(\pm\) 0 & 750 \(\pm\) 2 & 132 \(\pm\) 5 & 179 \(\pm\) 219 \\ clasps & 33 \(\pm\) 1 & 4946 \(\pm\) 264 & 3771 \(\pm\) 21 & 3599 \(\pm\) 3 & 119 \(\pm\) 2 & 748 \(\pm\) 188 \\ complex & 23 \(\pm\) 1 & 3432 \(\pm\) 178 & 2885 \(\pm\) 9 & 2786 \(\pm\) 3 & 136 \(\pm\) 5 & 639 \(\pm\) 280 \\ potholes & 18 \(\pm\) 1 & 2595 \(\pm\) 235 & 2145 \(\pm\) 81 & 2055 \(\pm\) 65 & 108 \(\pm\) 38 & 602 \(\pm\) 320 \\ rockpile & 18 \(\pm\) 1 & 2389 \(\pm\) 58 & 2260 \(\pm\) 30 & 2102 \(\pm\) 30 & 123 \(\pm\) 53 & 745 \(\pm\) 323 \\ var\_density & 17 \(\pm\) 2 & 2288 \(\pm\) 261 & 1657 \(\pm\) 57 & 1615 \(\pm\) 50 & 113 \(\pm\) 17 & 781 \(\pm\) 192 \\ square\_spiral & 35 \(\pm\) 2 & 5201 \(\pm\) 179 & 4235 \(\pm\) 34 & 4125 \(\pm\) 15 & 113 \(\pm\) 4 & 595 \(\pm\) 225 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: OfflineRMP performance evaluation. It can be observed that OnlineRMP found longer paths than OfflineRMP as expected given the available information to make decisions. These differences are statistically significant with 95% confidence according to the paired Wilcoxon rank-sum test. The execution times of OnlineRMP are very competitive, enabling fast re-planning during the execution of the path. Figure 16 shows the best results found by OfflineRMP, and Figure 17 shows the best results found by OnlineRMP. The solutions obtained by OnlineRMP are biased to the target positions in most segments. Without knowing the entire environment, the only guide the robot knows is the target positions at each step. Only when the robot is close enough to an obstacle are surrounding strategies triggered.