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

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).