Journal of Guangdong University of Technology ›› 2021, Vol. 38 ›› Issue (06): 70-76.doi: 10.12052/gdutxb.210136

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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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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

CLC Number: 

  • TP242.2
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