An AGV Path Planning Method for Discrete Manufacturing Smart Factory

Guo Xin-de; Chris Hong-qiang Ding

doi:10.12052/gdutxb.210136

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An AGV Path Planning Method for Discrete Manufacturing Smart Factory

更新时间：2026-05-27

- An AGV Path Planning Method for Discrete Manufacturing Smart Factory
- Vol. 38, Issue 6, Pages: 70-76(2021)
- 作者机构：
  
  1. 广东工业大学自动化学院,广东,广州,510006
  2. 物联网智能信息处理与系统集成教育部重点实验室,广东,广州,510006
  3. 香港中文大学,广东,深圳,518172
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.210136
  CLC： TP242.2
- Online First：10 November 2021，
  
  Published：2021
- 稿件说明：
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郭心德, 丁宏强. 离散制造智能工厂场景的AGV路径规划方法[J]. 广东工业大学学报, 2021, 38(6): 70-76.

Guo Xin-de, Chris Hong-qiang Ding. An AGV Path Planning Method for Discrete Manufacturing Smart Factory[J]. 2021, 38(6): 70-76.
郭心德, 丁宏强. 离散制造智能工厂场景的AGV路径规划方法[J]. 广东工业大学学报, 2021, 38(6): 70-76. DOI： 10.12052/gdutxb.210136.

Guo Xin-de, Chris Hong-qiang Ding. An AGV Path Planning Method for Discrete Manufacturing Smart Factory[J]. 2021, 38(6): 70-76. DOI： 10.12052/gdutxb.210136.

摘要

自动导引车(Automated Guided Vehicle

AGV)的自主路径规划是离散制造智能工厂中物流系统的重要组成部分

AGV可以大大提高离散智能制造的智能化和自动化能力

而传统的AGV导航方式自由度较低。本文研究面向离散制造智能工厂场景下的AGV自主路径规划问题

应用深度强化学习方法提高自主路径规划的自由度。设计了一种多模态环境信息感知的神经网络结构

并将AGV在全局障碍下的路径规划预训练策略引入到复杂的离散制造智能工厂场景下的路径规划

实现了AGV从环境感知到动作决策的端到端路径规划。实验结果表明

采用本文提出算法的AGV能够在复杂的离散制造智能工厂环境进行自主规划路径

并具有较高的成功率和避障能力。

Abstract

Automated guided vehicle (AGV) autonomous path planning is an important part of the logistics system in discrete manufacturing smart factories. AGV can greatly improve the intelligence and automation capabilities of discrete smart manufacturing. The traditional AGV navigation method has a low degree of freedom. The autonomous path planning of AGV is studied under the scenario of discrete manufacturing smart factories

and deep reinforcement learning methods applied to improve the freedom of autonomous path planning. A neural network structure for multi-modal environmental information perception is designed

and the path planning policy of AGV under global obstacles introduced to the path planning in the complex discrete manufacturing smart factory scenario

thereby realizing the AGVs end-to-end path planning from environmental perception to action for decision making. The experimental results show that AGV can independently plan paths in the complex and unknown intelligent logistics system environment of discrete manufacturing smart factories

and has a high success rate and obstacle avoidance ability.

关键词

Keywords

references

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