# Social and Robust Navigation for Indoor Robots Based on Object Semantic Grid and Topological Map
## 1 Introduction  
Robotics, as a frontier field of technology, has profoundly affected all aspects of society. Various types of indoor service robots work in human life, such as nanny robots [1] that help the disabled or patients in their daily lives, medical robots [2] that assist the elderly in cognitive activities, guide robots [3] that lead tourists or clients, and the security robots [4] that defend the security. In order to ensure the indoor robots to complete different tasks and move autonomously, it is necessary to ensure the high safety, robustness, and natural human-robot interaction of the navigation system. Although the current robot navigation system has been greatly developed, there are still two critical problems: simple and social selection of navigation goal, efficient and secure path planning. Selection of navigation goal: The shortcomings of navigation goal selection for indoor robots are the operational complexity and goal point against the social norms. For example, the user is required to click the screen to choose a goal point in the robot operating system (ROS) [5], or set the target point in advance during the mapping [6]. Both of them are contrary to human nature of interacting with robots in natural language, because ordinary users do not have the theoretical knowledge related to robotics. In this contest, [PERSON] et al. [7] pointed out that natural language is arguably the most common and natural communication channel used and understood by humans, and dialogue systems are an integral part of modern approaches to reduce the user's operating requirements and enhance the user's interactive experience. Based on this, [PERSON] et al. [8] created a database for the robot's behavior andcorresponded to natural language so that the navigation plan can be inferred from natural language instructions. Likely, [PERSON] et al. [9] created the parsers based on voice commands and corresponded control expressions. Meanwhile, great progress has been made in natural language processing, including speech recognition, semantic understanding of specific scenes, etc. The iFLYTEK [10] and Baidu [11] speech recognition products, and Jieba word segmentation algorithm [12] have been available. They help us extract phrases from natural languages. Furthermore, considering the navigation goal points should be in line with social norms, [PERSON] et al. [13] thought semantic map provides an abstraction of space and a means for human-robot communication. On the other hand, interactive robots must be equipped with environment representations and reasoning methods [7]. [PERSON] et al. [14] provided us with the goal area selection strategy by dividing the object surrounding space into the affordance space (the space related to human activities) and nonaffordance space (the space unrelated to human activities). The authors of [15; 16] showed how to extract spatial descriptions and other spatial information from occupied grid map in human-robot conversations. Then [PERSON] et al. [17] integrated the hierarchical spatial information and semantic knowledge to extend the planner's capabilities. There is still a huge promotion space of the sociality and convenience of navigation goal selection. Passing through narrow doors: For the indoor environment, the robot's navigation paths often pass through narrow doors. Due to sensor noise and calibration errors in real environment, it is a likelihood that the path planner fails to perceive the valid path even if the feasible path exists. On the other hand, considering the deviation of positioning and motor control, path planners do not guarantee the greatest passing door safety, leading to potential security risks, such as collision and overturn. A series of studies have been conducted on the door detection to solve this problem. [PERSON] et al. [18] planned a route with a confocal curve (hyperbola, ellipse, and circle) to pass the door In [19], [PERSON] et al. built a new door status recognition system to determine the valid path.