# Social and Robust Navigation for Indoor Robots Based on Object Semantic Grid and Topological Map
## 1 Introduction
The similar low-cost navigation system was also proposed by [PERSON] et al. [20]. The above methods all detect the door's position and re-plan the trajectory of crossing the door, but they are performed on-the-fly during robot navigation and waste much calculation time. Furthermore, the robotics trajectory's safety was considered and integrated into navigation. [PERSON] et al. [21] controlled the robot to cross the door vertically by the adaptive controller. Considering that the waypoints can greatly improve the navigation success rate and robustness, [PERSON] et al. [22] proposed an automatic procedure to insert auxiliary navigation points within the problematic areas, which significantly improves the security and efficiency. The authors of [23; 24] used offline methods to establish a topological map of the indoor critical areas, realizing the smooth connection between navigation tasks. The efficiency was improved regarding the offline mapping. However, they still do not consider the safety of the trajectory of crossing door. Global Path planning: Global Path planning for indoor mobile robot navigation is a wide problem, and there exist many methods and algorithms. Dijkstra [25] and A* [26] are commonly implemented and tested in global path planning. The Dijkstra traverses cells in ascending order of the length from the start and calculate the optimal path, but it is very inefficient. A* add heuristic distance to the Dijkstra's cost algorithm, which provides the search direction pointing to goal point. Therefore, A* is more efficient and compute fewer potentials. However, heuristic does not take the room topology information into account, so there is a higher probability for A* to search in the wrong direction, and cause large computational time and low navigation speed. To improve the efficiency of A* global path planning, further modifications were conducted. [PERSON] et al. [27] tested various modifications of A* algorithm, including Basic, Theta*, Phi*, RSR, and JPS. RRTs algorithm [28], a technique for generating open-loop trajectories of non-linear systems with state constraints, was also applied to path planning of mobile robot. [PERSON] et al. [29] introduced two new extensions based on the existing RRT work, namely, waypoint cache and adaptive cost search; [PERSON] et al. [30] proposed particle RRT algorithm and explicitly considers the uncertainty in its domain, similar to the operation of particle filters. Although RRTs effectively improve the navigation efficiency, the path is generally not optimal and usually has corners. On the other hand, people tried to integrate the grid maps and topological maps in the mobile robot navigation. [PERSON] et al. [31] integrated both maps, partitioned the topological maps into coherent regions and gained better navigation efficiency. [PERSON] et al. [32] combined the details of metrical maps in small-scale space with the conciseness of topological maps in large-scale space. Both of the above methods implemented the advantages of the topological maps in the complex and large environments. Then [PERSON] et al. [33] used AI planning to carry out topological navigation in a complex indoor environment, to perform a sequence of navigation actions. It divided the navigation tasks into phases but ignored the grid maps and took a lot of time defining navigation action libraries. This paper proposes an indoor robot navigation system with object semantic grid and topological map, which enables robot navigation to automatically select goals and become more safe, robust, and efficient. First, based on the team's previous work [34; 35], the object semantic grid and topological map are built for the indoor environment. The map contains all objects' semantic information, occupied area, the semantic information of surrounding area. Moreover, it contains the coordinates of all door endpoints and the room dominant direction. Then, the natural language is used for human-robot interaction.