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Erschienen in:
2021 | OriginalPaper | Buchkapitel
Multi AGV Industrial Supervisory System
verfasst von : Ana Cruz, Diogo Matos, José Lima, Paulo Costa, Pedro Costa
Erschienen in: Optimization, Learning Algorithms and Applications
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Abstract
Automated guided vehicles (AGV) represent a key element in industries’ intralogistics and the use of AGV fleets bring multiple advantages. Nevertheless, coordinating a fleet of AGV is already a complex task but when exposed to delays in the trajectory and communication faults it can represent a threat, compromising the safety, productivity and efficiency of these systems. Concerning this matter, trajectory planning algorithms allied with supervisory systems have been studied and developed. This article aims to, based on work developed previously, implement and test a Multi AGV Supervisory System on real robots and analyse how the system responds to the dynamic of a real environment, analysing its intervention, what influences it and how the execution time is affected.