# Social and Robust Navigation for Indoor Robots Based on Object Semantic Grid and Topological Map
## 6 Experimental Results
### Segmented Global Path Planning Results
#### 6.3.2 Success Rate  
Tables 4 and 5 respectively summarize the experimental results of the CDSR and NSR with different radius in the home scene and the office scene. In the experiment, the ROS move_base stack [40] with A* global planner and DWA local planner were implemented to compare with the segmented global path planning method. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Radius (m)** & **NA** & **MB** & **T** & **NAS (\%)** & **MBS (\%)** & **CR (\%)** \\ \hline \multirow{3}{*}{Home} & 0.18 & 24 & 19 & 24 & 100 & 79.17 & 20.83 \\  & 0.23 & 23 & 15 & 24 & 95.83 & 62.5 & 33.33 \\  & 0.28 & 12 & 8 & 24 & 50 & 33.33 & 16.67 \\ \hline \multirow{3}{*}{Office} & 0.18 & 28 & 23 & 28 & 100 & 82.14 & 17.86 \\  & 0.23 & 28 & 19 & 28 & 100 & 67.86 & 32.14 \\ \hline Average & - & 115 & 84 & 128 & 89.84 & 65.63 & 24.22 \\ \hline \hline \end{tabular}  
* NA: the crossing door number when using the proposed algorithm based on the ANPs. MB: the crossing door number when using the robot operating system (ROS) move_base stack. T: the total door number of all paths. NAS: crossing door success rate when using the proposed algorithm based on the ANPs (NA/T). MBS: crossing door success rate when using ROS move_base stack (MB/T). CR: the improvement when compared to ROS move_base (NAS-MBS). Average values are also included.  
\end{table} 
Table 4: Results of crossing door success rate. Figure 9: Auxiliary Navigation Points: (**a**) home scene. (**b**) office scene. Multiple conclusions can be extracted from these results:
* The proposed navigation algorithm based on the ANPs can significantly improve crossing door success rate and navigation success rate for the three robot sizes and two environments, reaching improvements of up to 33.33% (e.g., see 0.23 m radius in the home scene) and 50% (e.g., see 0.23 m radius in the office scene). * The first reason for the increase of success rate is that the proposed method can eliminate the influence of observation error on the navigation. In the process of approaching the door, the erroneous obstacle completely obstructed the feasible area crossing door, and the valid path could not be planned, thus giving up navigation. As shown in Figure 10a, when the robot with a radius of 0.23 m approached the bathroom's door (indicated in the red circle), the lidar observation data had noise, making the door area blocked by obstacles. Figure 10b shows our proposed method in the same scenarios. First, the robot was controlled to the auxiliary navigation point (the position where robot stands in Figure 10b) and observed the door area in the direction parallel to the trajectory passing direction. Thus, the shape of the door area was captured successfully. Figure 10c,d show the similar results of going to the laptop in the meeting room based on ROS move_base and our proposed algorithm when the robot was in the office environment with the radius of 0.23 m.
* The second reason for the increase of success rate is that, by setting ANPs on both sides of the door, the robot passes through the door in an approximately straight line, reducing the probability  
Figure 10: Generation and elimination of observation error. (**a**,**c**) Erroneous obstacle in the red circle blocks the path. (**b**,**d**) Error is eliminated when robot is at the ANP.