5G Network Implementation for Autonomous Mobile Robots in Manufacturing Fields

Abstract

Autonomous Mobile Robots are popular facilities in manufacturing fields today but require a wireless network connection to back-end applications. The low latency and high bandwidth for massive connectivity are crucial issues in intelligent manufacturing since intelligent features require more computing resources with higher operating efficiency. This study compares the WiF6,  5G with multi-access edge computing (MEC), and 5G without MEC to verify the response delay from the different computation resources. The experiment performed the data throughput measurement of video and audio of a high-definition camera to evaluate the latency. Camera (CCTV) is commonly used in AMR to detect objects in its peripheral environment, and it committed the most bandwidth in the mobile operation of AMR. The measurement results reveal that the best latency of video and audio are 123 ms and 13.85 ms via the WiFi 6 network. The data transmission routes through the 5G with MEC and without 5G resulted in a more significant delay. Consequently, the latency can be unsatisfaction in the manufacturing field when the AMRs tend to reach farther away applications or computation resources with the current network. In summary, the results show that the current 5G network applied in the experiment had constrained by the mobile network operator (MNO). In further works, we propose a network simulation tool, GNS3, and illustrate a topology for combining practical and hypothetical data analysis.