scholarly journals Automated guided vehicle systems and their Petri-net properties

1992 ◽  
Vol 3 (6) ◽  
pp. 379-390 ◽  
Author(s):  
Suhua Hsieh ◽  
Ying-Jer Shih
Keyword(s):  
Petri Net ◽  
Automated Guided Vehicle ◽  
Vehicle Systems

Optimal Petri-Net Controller for Avoiding Collisions in a Class of Automated Guided Vehicle Systems

2020 ◽  
Vol 21 (11) ◽  
pp. 4526-4537 ◽  
Author(s):  
Jiliang Luo ◽  
Yaxin Wan ◽  
Weimin Wu ◽  
Zhiwu Li
Keyword(s):  
Petri Net ◽  
Automated Guided Vehicle ◽  
Vehicle Systems

A review of automated guided-vehicle systems design and scheduling

1991 ◽  
Vol 2 (1) ◽  
pp. 44-51 ◽  
Author(s):  
RUSSELL E. KING ◽  
CARL WILSON
Keyword(s):  
Systems Design ◽  
Automated Guided Vehicle ◽  
Vehicle Systems

scholarly journals SOLVING THE FAILING TRACK MARKER PROBLEM IN AUTOMATED GUIDED VEHICLE SYSTEMS – A CASE STUDY

2020 ◽  
Vol 10 (3) ◽  
pp. 36-43
Author(s):  
Tomasz Lewowski
Keyword(s):  
Data Acquisition ◽  
Automated Guided Vehicle ◽  
Technical Process ◽  
Paper Briefly ◽  
Internal Defects ◽  
Algorithm Development ◽  
Verification Algorithm ◽  
Vehicle Systems ◽  
Self Tuning

This paper is a case study of the development of a localization and positioning subsystem of an Automated Guided Vehicle-based transportation system. The described system uses primarily RFID markers for localization. In some deployments, those markers occasionally fail, mostly due to being crushed by cargo platforms operated by a human or due to internal defects. Those failures are not common enough to warrant switching from marker-based localization to a more sophisticated technique, but they require additional effort from maintenance staff. In this case study, we present our solution to this problem – a self-tuning algorithm that is able to detect marker failures and, in most cases, keep the system operational. The paper briefly discusses business circumstances under which such a solution is reasonable and then describes in detail the entire technical process, including data acquisition, verification, algorithm development and finally, the result of deploying the system in production.

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