The History of Automated Guided Vehicle Systems

Optimal location of workstations in tandem automated-guided vehicle systems

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-014-5678-x ◽

2014 ◽

Vol 72 (9-12) ◽

pp. 1429-1438 ◽

Cited By ~ 3

Author(s):

Amir Salehipour ◽

Mohammad Mehdi Sepehri

Keyword(s):

Optimal Location ◽

Automated Guided Vehicle ◽

Vehicle Systems

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A review of automated guided-vehicle systems design and scheduling

Production Planning & Control ◽

10.1080/09537289108919329 ◽

1991 ◽

Vol 2 (1) ◽

pp. 44-51 ◽

Cited By ~ 24

Author(s):

RUSSELL E. KING ◽

CARL WILSON

Keyword(s):

Systems Design ◽

Automated Guided Vehicle ◽

Vehicle Systems

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Automated guided vehicle systems and their Petri-net properties

Journal of Intelligent Manufacturing ◽

10.1007/bf01473533 ◽

1992 ◽

Vol 3 (6) ◽

pp. 379-390 ◽

Cited By ~ 5

Author(s):

Suhua Hsieh ◽

Ying-Jer Shih

Keyword(s):

Petri Net ◽

Automated Guided Vehicle ◽

Vehicle Systems

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

Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska ◽

10.35784/iapgos.1512 ◽

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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