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JRM Vol.37 No.6 pp. 1343-1354
doi: 10.20965/jrm.2025.p1343
(2025)

Paper:

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Research on Cloud-Supported Autonomous Mobile Robots: Self-Position Estimation Under Communication Delay and Verification of Applicability of Kalman Filter

Kazuteru Tobita ORCID Icon, Seiya Nakamura, Mao Nabeta, and Kazuhiro Mima

Shizuoka Institute of Science and Technology
2200-2 Toyosawa, Fukuroi, Shizuoka 437-8555, Japan

Received:
April 1, 2025
Accepted:
July 2, 2025
Published:
December 20, 2025
Creative Commons License
Keywords:
mobile robot, self-position estimation, Kalman filter, packet loss, communication delay
Abstract

As a safety measure against the communication risk caused by the use of the cloud for self-position estimation by autonomous mobile robots, we propose a method that combines the accurate 3D self-position estimation FAST-LIO on the cloud side and the minimum 2D self-position estimation AMCL on the robot side. In this study, we created an environment in which communication delays and disruptions are added by software. We then examined how these affect the self-location estimation of FAST-LIO in the cloud. In addition, to improve the reliability of the overall system, the advantages of each algorithm were leveraged and their disadvantages complemented by effectively combining the results of different self-position estimations (FAST-LIO in the cloud and AMCL on the robot side) using the unscented Kalman filter (UKF). The experimental results showed that stable self-position estimation at 100 ms intervals can be achieved using the UKF to combine AMCL (which is updated at 100 ms intervals on the robot side) with FAST-LIO on the cloud side (where update times are at worst 1 s due to latency and other factors).

Delay and packet loss model for cloud-supported AMR

Delay and packet loss model for cloud-supported AMR

Cite this article as:
K. Tobita, S. Nakamura, M. Nabeta, and K. Mima, “Research on Cloud-Supported Autonomous Mobile Robots: Self-Position Estimation Under Communication Delay and Verification of Applicability of Kalman Filter,” J. Robot. Mechatron., Vol.37 No.6, pp. 1343-1354, 2025.
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Last updated on Dec. 19, 2025