Design and Implementation of Intelligent Construction Automation System Based on <scp>6G</scp> Network

ABSTRACT In modern intelligent construction automation systems, due to the interference of network delay, task synchronization between devices is hindered, resulting in uncoordinated operations between robots and collisions, and task conflicts. This paper builds an intelligent construction automation system based on a 6G network, using the low latency and high bandwidth characteristics of a 6G network to effectively solve the delay problem in task synchronization and collaborative work. Its innovative combination of network slicing technology and edge computing methods customizes specific network resources for different application scenarios to minimize latency. The fusion of convolutional neural network (CNN) and long short‐term memory (LSTM) models can make better predictions, and combined with the deep reinforcement learning model (DRL), a path planning plan can be formulated based on the prediction results to avoid collision problems in the robot's work. The experimental results show that after the 6G network optimization system, the task scheduling rate of the robot can reach 0.95, compared with 5G network optimization, which only reaches 0.90, and the collision problem of the robot can be well avoided. The collision rate after optimization can approach 0, which can ensure the smooth progress of the construction process and the safety and reliability of task execution.