# Social and Robust Navigation for Indoor Robots Based on Object Semantic Grid and Topological Map
## 4 Automatic Selection of Navigation Goal with Speech Interaction  
Natural language is the most common and social communication channel, so it is essential to enable the human-robot interaction via natural language to reduce the operation requirements to users. Figure 2: Object Semantic Grid and Topological Map. (**a**) Object semantic information and object occupied space. (**b**) Object goal space: red rectangle indicates the nonaffordance space (NAS), and the original color rectangle indicates the affordance space. (**c**) Spatial relationship of object goal space and object occupied space. (**d**) Semantic topological information. As shown in Figure 1, the automatic selection of navigation goal needs three inputs: natural language, object semantic grid map, and semantic topological map. The algorithm includes speech recognition, word segmentation, keyword extraction, concept query, goal space selection, and goal point selection, as shown in Algorithm A1 in Appendix A. Before the navigation, users issue voice instructions to the robot in the form of natural language, such as \"go to the front of fridge in the kitchen\" including room, object, and location information. First, the language instruction is converted into text through speech recognition, and the text is broken down into phrases through Chinese word segmentation module. For example, by implementing the Jieba algorithm [12], the sentence \"go to the front of fridge in the kitchen\" is broken into \"go to the/side/of/fridge/in the/kitchen\", as shown in Figure 3a. Since the purpose of keyword extraction is to extract phrases related to navigation goal, we build a database of phrases and their corresponding navigation behavior. Then we query the database for each phrase extracted from voice instruction, so as to determine the target room, target object, and target spatial relationship. For example, in the case of Figure 3a, the target room is the kitchen, target object is the fridge, and target spatial relationship is the side. To make sure whether navigation goal exists in the indoor environment, we need to carry out concept query in the map, which means looking for the target room and target object in the room lists and object lists saved in the object semantic grid and topological map, as shown in Figure 3b. If target exists, the navigation path will be planned. We visualize the target room and target object in the map, as shown in Figure 3c. To make the selection of robot target points more in line with social norms, we need to select the goal space from the object surrounding area, according to target spatial relationship. Based on [14], we divide the object surrounding area into affordance space and nonaffordance space. The affordance space refers to the area related to human activities (the front and yellow area of the object occupied area in Figure 3d), and the nonaffordance space refers to the area irrelevant to human activities (the diagonal and red area of the object occupied area in Figure 3d). In this context, nonaffordance space can be determined as goal space because the target spatial relationship is the side. Figure 3: Automatic selection of navigation goal: (**a**) Word segmentation and concept query. (**b**) Object semantic grid and topological map. (**c**) Target object and target room. (**d**) The affordance space and nonaffordance space of the object goal. (**e**) Navigation goal point. (**f**) Goal orientation. Moreover, it is necessary to filter the target points in the target area. The selection criteria are as follows. (1) Hard constraint: the target point should not be in the inflation area of cost map; (2) Soft constraint: the target point should avoid being in the other objects' affordance space. Among two criteria, the hard constraint is absolutely invalidate, while the soft constraint should be followed as far as possible.