广东工业大学学报 ›› 2021, Vol. 38 ›› Issue (06): 70-76.doi: 10.12052/gdutxb.210136

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离散制造智能工厂场景的AGV路径规划方法

郭心德1, 丁宏强2,3   

  1. 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
  • 收稿日期:2021-09-06 出版日期:2021-11-10 发布日期:2021-11-09
  • 通信作者: 丁宏强(1954–)，男，教授，博士，主要研究方向为机器学习、数据挖掘、生物信息学、信息检索、网络链接分析和高性能计算，E-mail：chrisding@cuhk.edu.cn E-mail:chrisding@cuhk.edu.cn
  • 作者简介:郭心德(1996–)，男，硕士研究生，主要研究方向为深度强化学习和路径规划，E-mail：ward@mail2.gdut.edu.cn
  • 基金资助:
    国家重点研发计划项目(2020AAA0108304)；国家自然科学基金资助项目(62073088，U1911401，U1701261)；广东省基础与应用基础研究基金资助项目(2019A1515011606)

An AGV Path Planning Method for Discrete Manufacturing Smart Factory

Guo Xin-de1, Chris Hong-qiang Ding2,3   

  1. 1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China;
    2. Key Laboratory of Intelligent Information Processing and System Integration of IoT, Ministry of Education, Guangzhou 510006, China;
    3. The Chinese University of Hong Kong, Shenzhen 518172, China
  • Received:2021-09-06 Online:2021-11-10 Published:2021-11-09

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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。

关键词: 自动导引车(AGV), 路径规划, 深度强化学习, 神经网络结构

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.

Key words: automated guided vehicle (AGV), path planning, deep reinforcement learning, neural network structure

中图分类号: 

  • TP242.2
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