# Social and Robust Navigation for Indoor Robots Based on Object Semantic Grid and Topological Map
## 6 Experimental Results
### Automatically Selecting Navigation Goal Results  
This paper proposed the autonomous and social selection of navigation goal through natural language interaction and the object semantic grid and topological map. To verify the ability to select goal autonomously by language interaction, the experiment was designed as follows:  
The operator designated the navigation target by language instructions containing the target home, object, and spatial relationship. Twenty paths were set in the home environment (as shown in Table 1) and 18 paths in the office environment (as shown in Table 2). Especially, both of the front space and side space of every object are set as the goal space at least twice. If the value of cell (i, j) in the table is 1, the voice command is to move from object i to the front of object j, and then the robot will select a navigation point from the affordance space of object j. If the value is 2, the voice instruction is to move from object i to the side of object j, and then the robot will select a navigation point from the nonaffordance space of object j. In addition, the robot radius was set 0.18 m, and path planning algorithm and controller were set by default, so that the experiment focused on autonomously select goal by language interaction. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Object** & **Desk1** & **Desk2** & **Desk3** & **Microwave** & **Laptop** \\ \hline desk1 & - & 1 & 1 & 1 & 1 \\ desk2 & 1 & - & 1 & 1 & 1 \\ desk3 & 1 & 1 & - & 2 & 2 \\ microwave & 2 & 2 & 2 & - & 2 \\ laptop & 2 & 2 & 2 & 2 & - \\ \hline \hline \end{tabular}  
* 1 Desk in laboratory; 2 Desk in elevator hall; 3 Desk in corridor.  
\end{table} 
Table 2: Navigation goal in office. Figure 8: Experimental environments. (**a**) Room environment, (**b**) office environment, (**c**) object semantic grid and topological map of home, (**d**) object semantic grid and topological map of office, (**e**) door endpoints of home, and (**f**) door endpoints of office. Only parts of environmental results are illustrated in Table 3 due to space limitations. As shown in the column called \"word segmentation\", the target information are extracted from voice instructions (which is Chinese originally and translated into English). The coordinate of navigation goal point selected is shown in the column called \"target point selection\" and all of these pictures are generated by the RVIZ software during the navigation. The red rectangle is the nonaffordance space, the green rectangle is the affordance space, and the blue arrow is the goal pose. The navigation results are shown in the column called \"Navigation Result\". The human operator is also shown in the pictures to indicate whether the robot's final position satisfies the requirement of the human-robot coexisting environment. Multiple conclusions can be extracted from these results:  
* The target information, including target room, object, and spatial relationship, can be extracted accurately and clearly through language reorganization [10], word segmentation [12], keyword extraction, and concept query. For example, the command in the first row of Table 3 \"go to the front of the sink in the bathroom\" can be divided into \"go to the/front/of the/sink/in the/bathroom \", in which the target room is bathroom, target object is sink, and target spatial relationship is front. * The navigation goal is selected automatically and correctly, and it is excellent in terms of social norms and the object affordance theory through qualitative comparison. The goal is located in the affordance space if goal spatial relationship is front, while it is located in the nonaffordance space if goal spatial relationship is side. In addition, the goal location satisfies the hard constraint (not in the inflation area of cost map) and soft constraint (avoid other objects' affordance space) as described in Section 3. * The system helps the robot better integrated into the human-robot coexisting environment according to the navigation results.