# Automated Destination Renewal Process for Location-Based Robot Erands
## 5 Conclusions  
Several attempts have been made to automate logistics and errand tasks. These include efforts in automating target recognition for logistics transport [37] and path planning for multiple robots [38]. However, there remain numerous areas that require. In this paper, we propose an automated process for a mobile robot to perform location-based errands in indoor environments, with the following key contributions:  
First, the essential map-building process, a precursor to LBS tasks in service robots, is partially automated through the utilization of CAD drawings. This approach complements the traditional manual method, thereby minimizing both the time and human resources typically required for preliminary tasks in robot operation. Second, by incorporating object recognition with its position, the system estimates optimal positions for robot manipulation based on identified objects, facilitating efficient node placement. Nodes are automatically updated even in variable obstacle environments. Third, the segmentation of indoor spaces using a morphological segmentation algorithm, along with classification utilizing the places205 module, enables the establishment of hierarchical connectivity. This structure, based on spatial relationships between segmented spaces, provides an effective approach for exploration in LBS scenarios like errand services. Additionally, through consecutive experiments evaluating the performance and recognition capabilities of each process, we have validated the automation potential of LBS tasks for service robots in presented environments.  
However, obtaining grid maps from CAD drawings still involves some manual steps, such as removing annotation layers or setting the scale for grid map conversion. There is also a risk of information distortion due to the incorrect recognition of space or objects. Notably, the top-1 accuracy in space classification shows a relatively low performance, highlighting the need for further improvement optimization. This indicates that the successful execution of an errand service requires more complex processes and system integration than currently proposed.  
Accordingly, future research will focus on enhancing the intelligence of performing users' errand services by developing grid map conversion technology that minimizes human intervention, employing 3D environment modeling techniques for map generation, and equipping robots with pick-and-place functionalities in various environments. Additionally, we aim to improve semantic information and increase the accuracy of the recognition module to ensure that actual users do not experience any inconvenience in performing errands.  
Conceptualization, W-J.L. and S.-S.Y; methodology, W-J.L. and S.-S.Y.; software, W-J.L.; formal analysis, W-J.L. and S.-S.Y.; investigation, W-J.L.; resources, W-J.L.; data curation, W-J.L.; writing--original draft preparation, W-J.L.; writing--review and editing, S-S.Y.; visualization, W-J.L.; supervision, S.-S.Y.; project administration, S.-S.Y. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Ministry of Trade, Industry and Energy (MOTIE, Korea) under the Technology Innovation Program (No. 20015052) and Industrial Technology Innovation Program (No. RS-2023-00232141, Development of a life assist mobile robot that understands people's daily behavior).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to laboratory regulations.  
The authors declare no conflicts of interest.