This paper presents an optimization strategy to coordinate multiple Autonomous Guided Vehicles (AGVs) on ad-hoc pre-defined roadmaps used in logistic operations in industrial applications. Specifically, the objective is to maximize traffic throughput of AGVs navigating in an automated warehouse by minimizing the time AGVs spend negotiating complex traffic patterns to avoid collisions with other AGVs. In this work, the coordination problem is posed as a Quadratic Programming (QP) problem where the optimization is performed in a centralized manner. The optimality of the coordination strategy is established and the feasibility of the strategy is validated in simulation for different scenarios and for real industrial environments. The performance of the proposed strategy is then compared with a decentralized coordination strategy which relies on local negotiations for shared resources. The results show that the proposed coordination strategy successfully maximizes vehicle throughout and significantly minimizes the time vehicles spend negotiating traffic under different scenarios.

A Quadratic Programming approach for coordinating multi-AGV systems / Digani, Valerio; Hsieh, M. Ani; Sabattini, Lorenzo; Secchi, Cristian. - (2015), pp. 600-605. ((Intervento presentato al convegno 11th IEEE International Conference on Automation Science and Engineering, CASE 2015 tenutosi a Gothenburg, Sweden nel 24-28 August 2015.

I documenti presenti in Iris Unimore sono rilasciati con licenza Creative Commons Attribuzione - Non commerciale - Non opere derivate 3.0 Italia, salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Powered by IRIS-about IRIS-Utilizzo dei cookie

 Copyright © 2020