# Automated Destination Renewal Process for Location-Based Robot Erands
## 1 Introduction  
Recently, research on mobile robots has been actively conducted to provide various public services to humans in indoor environments such as hospitals, airports, shopping malls, and restaurants, moving away from structured industrial sites [1, 2, 3, 4, 5]. This is because as the core technology of robots has gradually become more intelligent and sophisticated, it has become possible to provide social services to humans based on locations in complex and diverse environments [6, 7, 8]. In this context, the grid map plays a vital role in location-based services (LBSs) as it allows the robot to determine its location, set service destinations, and navigate safely.  
In the case of simultaneous localization and mapping (SLAM), which generates a grid map, the unknown area is typically divided into a movable area and an obstacle area based on data from the robot's odometer and distance sensor for location estimation. However, errors in position estimation from inertial sensors or sensor measurements can lead to distortions in obstacle positions. To mitigate these errors, various techniques like loop closure are applied, but their precision may still be limited compared to precisely measured CAD drawings.  
The manual creation of maps by operators is time-consuming, especially for large-scale mapping tasks. Additionally, depending on the sensing environment, starting the map-building process from scratch can be inconvenient. Once the grid map is created, the robot obtains environmental awareness, including the recognition of static and dynamicobstacles and objects, as well as the location needed to respond appropriately to various LBSs. Finding the movement destination on the grid map typically involves estimating the target using either operator-registered locations or the reception strength of vision or wireless sensors installed indoors. However, these procedures, particularly when dealing with extensive and intricate tasks, may require laborious and time-consuming efforts similar to the map creation process. The failure to address exception handling for various task failures, including this one, can prevent successful service execution. Moreover, since general users lack the means to resolve these issues, the automation of robot destinations plays a crucial role in ensuring the success of LBSs.  
In this study, we propose a methodology to automate the multi-step navigation process for service robots in new environments to facilitate location-based delivery services for users. Initially, we use CAD drawings containing building dimensions and spatial information to acquire static obstacles for the new environment, and it converts to a grid map. Subsequently, we employ generalized Voronoi graph (GVG) thinning [9; 10; 11] to establish an initial target node for the robot to move and explore the indoor space. The mobile robot then utilizes distance and vision-based measurements from this node to detect objects and surrounding obstacles, updating the node on the grid map as needed. Following that, object recognition and morphological segmentation [12; 13] techniques are employed to classify the space and assign labels to information [14; 15]. Finally, the robot leverages the collected nodes and the associated spaces to which they belong to construct a topological stratification [16; 17; 18; 19; 20] based on correlations.