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System Development & Application
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 5
1501-1504,1509

Design of AGV intelligent navigation system based on deep reinforcement learning

He Xuemei1
Kuang Yin1
Yang Zhipeng2
Yang Yaqiao3
1. College of Art & Design, Shaanxi University of Science & Technology, Xi'an 710021, China
2. System Design Institute of Hubei Aerospace Technology Academy, Wuhan 430040, China
3. State Grid Wuhan Dongxihu District Power Supply Company, Wuhan 430040, China
DOI: 10.19734/j.issn.1001-3695.2021.10.0472

Abstract

Aiming at autonomous navigation and delivery of AGV in large-scale complicated and unknown environment, this paper put forward an autonomous online decision-making algorithm based on deep reinforcement learning. Specifically, combining with sensors to detect and perceive surrounding obstacles, the method used DDPG algorithm to realize the input of environmental perception and action direct output control, which helped the AGV complete autonomous navigation and autonomous obstacle avoidance tasks. To solve the problem, it disturbed the training samples easily by the environment, the algorithm preprocessed the relevant data with Gaussian noise in the learning sample, which helped the agent adapt to the training environment under noise and improve its robustness in real environment. Simulation results show that the improved DL-DDPG algorithm can provide more efficient online decision-making ability for the control system and enable the competency of autonomous navigation and intelligent control of AGV.

Foundation Support

陕西省科技厅资助项目(2019GY-077)
教育部人文社会科学研究规划基金资助项目(17YJAZH100)
陕西省教育厅人文社科一般专项项目(20JK0070)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.10.0472
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 5
Section: System Development & Application
Pages: 1501-1504,1509
Serial Number: 1001-3695(2022)05-036-1501-04

Publish History

[2021-12-31] Accepted Paper
[2022-05-05] Printed Article

Cite This Article

贺雪梅, 匡胤, 杨志鹏, 等. 基于深度强化学习的AGV智能导航系统设计 [J]. 计算机应用研究, 2022, 39 (5): 1501-1504,1509. (He Xuemei, Kuang Yin, Yang Zhipeng, et al. Design of AGV intelligent navigation system based on deep reinforcement learning [J]. Application Research of Computers, 2022, 39 (5): 1501-1504,1509. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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