A4 Refereed article in a conference publication
Integration of SLAM and Navigation System with Customized Autonomous Mobile Robot
Authors: Pramanic, Md Sajib; Vartiainen, Paavo; Kortelainen, Matti; Karjalainen, Pasi; Heikkonen, Jukka; Kanth, Rajeev
Editors: Verma, Om Prakash; Wang, Lipo; Kumar, Vikas; Shrivastava, Vimal; Sharma, Tarun Kumar
Conference name: International Conference on MAchine inTelligence for Research & Innovations
Publisher: Springer Nature Switzerland
Publication year: 2026
Journal: Lecture Notes in Networks and Systems
Book title : Machine Intelligence for Research and Innovations : Proceedings of MAiTRI 2025, Volume 1
Volume: 1911
First page : 478
Last page: 487
ISBN: 978-3-032-22826-0
eISBN: 978-3-032-22827-7
ISSN: 2367-3370
eISSN: 2367-3389
DOI: https://doi.org/10.1007/978-3-032-22827-7_45
Publication's open availability at the time of reporting: No Open Access
Publication channel's open availability : No Open Access publication channel
Web address : https://doi.org/10.1007/978-3-032-22827-7_45
This research presents the development of an autonomous mobile robot (AMR) system using the Robot Operating System (ROS) for hospital logistics. The robot platform, Ceterio C-100, integrates a lifting mechanism and dual LiDAR sensors for efficient indoor navigation, obstacle detection, and collision avoidance. The system combines SLAM (Simultaneous Localization and Mapping) and autonomous navigation within a simulation environment, with motion synchronized to the real hardware. The implementation utilizes ROS and Docker containers for scalable and efficient deployment. The integration of 5G connectivity ensures low-latency communication and stable remote control, even in dynamic and demanding environments. During testing, the robot successfully executed navigation tasks, visualized in RViz, and responded in real time using ROS teleoperation tools. The study illustrates the full development cycle of AMRs from model sketching and URDF configuration to LiDAR sensing and real-world synchronization. The results confirm that 5G-connected, ROS-based AMRs reduce manual workloads and support automation in healthcare and smart industry operations.
Keywords:
5G, Autonomous Mobile Robot, autonomous navigation, Docker, ROS, SLAM
