# Social and Robust Navigation for Indoor Robots Based on Object Semantic Grid and Topological Map
## 7 Conclusions  
This paper proposes a social and robust indoor robot navigation system based on object semantic grid and topological map, which combines the two major modules: automatic selection of navigation goal and segmented global path planning. The proposed method aims at addressing the three critical problems. First, to select navigation goal more conveniently and socially, natural language is used as the human-robot interaction form, from which the target room, object, and spatial relationship can be extracted through speech recognition and word segmentation technology. Thus, the robot determines the target space and selects the navigation goal point from it. For example, we can get the target room \"meeting room,\" the target object \"laptop,\" and the target space decided as nonaffordance space, from the voice instruction \"go to the side of laptop in the meeting room.\" Furthermore, to improve the success rate and security of indoor navigation, we use the door endpoint coordinates and the room dominant direction kept in the topological map, to generate auxiliary navigation points on both sides of the door, to correct the robot trajectory. Finally, to improve the efficiency of global path planning, we segment the global path into every single topological space and plan the path respectively based on the auxiliary navigation points. The system is tested by a series of experiments in real indoor environments with multiple robot dimensions. Experimental results show that the navigation  
Figure 11: Heuristic search direction of A\({}^{*}\) is approximately optimal in the specific scenarios. (**a**) bed in master room to sink. (**b**) bed in master room to refrigerator. (**c**) refrigerator to sink.  
goals satisfy the social rules better. Auxiliary navigation point significantly improves the crossing door success rate (average 24.22%) and navigation success rate (average 30.56%) compared to ROS move_base. The number of traversed cells is greatly reduced than A* (average 69.85%) and Dijkstra (average 93.25%), especially in the complex and large-scale environment.  
In future work, we will improve the system and exploit deeply the ability of environment expression and reasoning, such as more associations between voice instructions and spatial relationships, so that system can be more diverse and social. At the same time, the navigation in the multi-connected topology area will also be considered by imposing the distance constraints and social constraints.  
Conceptualization, J.Z. and W.W.; methodology, J.Z.; software, J.Z.; validation, J.Z., X.Q., and Z.L.; formal analysis, [PERSON], X.Q., and Z.L.; investigation, [PERSON]; resources, [PERSON]; data curation, J.Z. and X.Q.; writing--original draft preparation, [PERSON]; writing--review and editing, J.Z. and W.W.; visualization, J.Z.; supervision, W.W.; project administration, J.Z.; funding acquisition, W.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of China under grant number 91748101 and the National Key Research and Development Program of China under grant number 2020 YFB1313600.  
The authors declare no conflict of interest.