ZHENG T T, WANG D, LIU W J, et al. Study on software self-adaptive mechanism for intelligent AGV scheduling system [J]. Journal of Henan Polytechnic University (Natural Science) , 2025, 44(4): 74-82.

doi: 10.16186/j.cnki.1673-9787.2024070019

Received: 2024/07/03

Revised: 2024/10/01

Published: 2025/06/19

Study on software self-adaptive mechanism for intelligent AGV scheduling system

Zheng Tingting1, Wang Dong1,2, Liu Weijing1,2, Xu Wenli1, Dang Xing1,2, Wang Junde1

1. Tianjin Institute of Aerospace Mechanical and Electrical Equipment， Tianjin  300458， China； 2. Tianjin Key Laboratory of Aerospace Intelligent Equipment Technology， Tianjin  300458， China

Abstract: Objectives Traditional AGV scheduling systems were found to struggle with adapting to dynamic changes in user needs and environments in practical applications， exhibiting issues such as slow demand response and rigid scheduling strategies. Methods Integrating a multi-level design structure of “user + software + hardware”， a software adaptive mechanism for intelligent AGV scheduling systems was proposed，. Based on the adaptive theory of “data perception， design decision-making， and execution feedback”， multiple aspects， including task scheduling， path planning， traffic control， battery management， and communication management， were comprehensively considered within the software adaptive system. The system’s response speed and scheduling efficiency were improved from a global perspective to enable real-time perception and dynamic adjustment to changes in AGV task demands and environments. Firstly， rapid import technology was used to capture user needs， which were then converted into recognizable raw data for the system based on rule mapping. Secondly， by matching with a preset scheduling strategy pool， relevant parameters of key influencing factors were identified to select the most suitable working mode for executing AGV tasks in the current scenario. Additionally， the adaptive mechanism continuously optimized the scheduling strategy pool based on real-time feedback from execution results， adapting to new demands and environmental changes. Results The experimental results indicated that the speed of collecting and applying user needs was significantly improved. The response time was no longer than 300 ms. After using the adaptive mechanism design framework， the overall operational efficiency of AGVs was increased by 36%. Conclusions The software adaptive mechanism significantly enhanced the response speed， scheduling efficiency， and data accuracy of AGVs. It effectively adapted to complex work demands in specific working scenarios. The system flexibility and adaptability were improved. 

Key words: adaptive mechanism; intelligent AGV; dispatching system; environmental perception; dynamic tuning