Section: ENGINEERING AND TECHNOLOGY Open Access Logo

Roadmap, routing and obstacle avoidance of AGV robot in the static environment of the flexible manufacturing system with matrix devices layout

Nguyen Hong Thai 1
Ly Thi Khanh Trinh 2, *
Le Quoc Dzung 2
  1. School of mechanical engineering, Hanoi University of Science and Technology, Vietnam
  2. Faculty of Automation Technology, Electric Power University, Ha noi, Vietnam
Correspondence to: Ly Thi Khanh Trinh, Faculty of Automation Technology, Electric Power University, Ha noi, Vietnam. Email: lyttk@epu.edu.vn.
Volume & Issue: Vol. 24 No. 3 (2021) | Page No.: 2091-2099 | DOI: 10.32508/stdj.v24i3.2536
Published: 2021-09-06

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Copyright The Author(s) 2023. This article is published with open access by Vietnam National University, Ho Chi Minh city, Vietnam. This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0) which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited. 

Abstract

In Flexible Manufacturing Systems (FMSs), Automated Guided Vehicles (AGVs) are considered an important element of the manufacturing system. The positioning system of the AGV robot is increasingly intelligent to integrate with manufacturing systems and through an IoT connectivity to find its path and flexibly avoid obstacles without the need for a fixed traditional navigation system. In order for the AGV robot to find its own path and avoid obstacles when required, robots are often equipped with a navigation system to know its current position in the system and compare with the roadmap installed on the robot's controller. To deal with this problem, this paper presents the method to build a robot's roadmap in a flexible manufacturing system where devices are arranged in a matrix style. On that basis, we have developed an algorithm for optimal routing and static obstacle avoidance based on the Dijkstra algorithm. A Matlab computation and simulation program has been written according to the algorithms of this study to explore different scenarios in the production line.

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