Collaborative Control Method of Intelligent Warehouse Traffic Signal and Multi-AGV Path Planning

Aiming at the problems of multi-AGV (automated guided vehicle) path planning in intelligent warehouse, such as poor real-time performance, weak obstacle recognition ability, multi-AGV collision, deadlock and congestion, a collaborative control method for intelligent warehouse traffic signals control and multi-AGV path planning is proposed. Traffic signals and multi-AGV path planning are regarded as a whole in this method. A collaborative control framework for traffic signals and multi-AGV path planning is designed. The LS-A3C (long short-asynchronous advantage actor-critic) algorithm and Bi-LSTM-CBAM (bi-long short-term memory-convolutional block attention module) algorithm are proposed as the core algorithm of the framework. The long-term and short-term information of traffic signals are encoded in LS-A3C algorithm, which uses a long short-term encoder and attention mechanism. They are represented by learning cell features. The A3C framework is used to calculate the Q value of the cell and the control strategy. The traffic signals time adapting to AGV flow is adjusted to solve the problems of multi-AGV collision, deadlock and congestion. The output result is spliced by calculating the state characteristics of the present moment and the leading moment in Bi-LSTM-CBAM algorithm to solve the problem of gradient disappearance and explosion in neural network effectively and improve real-time AGV path planning. The attention mechanism module CBAM is introduced. Weights are assigned based on how important the input is in order to strengthen the AGV’s ability to identify obstacles. Finally, simulation experiments are carried out on Sumo and Gazebo joint simulation platform. The experimental results show that the collaborative control method significantly reduces the AGV collision, deadlock and congestion, significantly improves the obstacle recognition ability, and greatly enhances the real-time path planning. The purpose of improving the AGV operation efficiency is achieved. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.