期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 16, 期 9, 页码 6133-6142出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2020.2963910
关键词
Smart manufacturing; Task analysis; Conferences; Performance evaluation; Reliability; Production facilities; Collaboration; Mobile intelligence; multiagent system; smart factories; trust; team formation
类别
资金
- COGITO - Dynamic and Cognitive Systems to allow Buildings to Learn and Adapt project - Italian Government [ARS01_00836]
- Piano della Ricerca 2016/2018 - Linea di intervento 2 (Universita degli Studi di Catania)
- NeCS Laboratory (DICEAM, University Mediterranea of Reggio Calabria)
- PRIN project Fluidware - MIUR - Italian Government [2017KRC7KT]
In Smart Factories, automated guided vehicles (AGVs) accomplish heterogeneous tasks as moving objects, restoring connectivity, or performing different manufacturing activities into production lines. These kinds of devices combine several capabilities, as artificial intelligence (visual and speech recognition, automatic fault detecting, proactive behavior) and mobility, into the so-called mobile intelligence. A typical scenario is represented by a workshop with a large number of mobile intelligent devices with associated agents, mutually interacting on their behalf. Here, to reach a given target by contemporary satisfying some basic requirements like effectiveness and efficiency, it is often necessary to organize ad hoc teams of free-moving vehicles, sensors, and smart devices. Therefore, a specific issue is the adequate representation of the reciprocal agent/device trustworthiness for advantaging such team formation processes within a smart factory environment. To this end, in this article, first, we define a trust measure based on reliability and reputation of AGVs, which are computed based on the feedbacks released for the AGVs activities in the factory; second, we design a trust framework exploiting the defined measures to support the formation of virtual, temporary, and trust-based teams of mobile intelligent devices; and third, we present a set of experimental results highlighting that the proposed trust framework can improve the workshop performance in terms of effectiveness and efficiency.
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