A Route Planner Using a Delegate Multi-Agent System for a Modular Manufacturing Line: Proof of Concept

Abstract

Featured Application: Reconfigurable logistic system for new generation of Factory of the Future manufacturing systems and modular systems.Abstract: Route planning in a multi-agent system (MAS) is still a complex task, especially if there is need for a continuous, decentralized planner of the routes for physical agents in a dynamic environment. It is a planner of this kind that is required in the application the article considers: the transportation of parts at a modular manufacturing line. Such a planner has to meet several difficult requirements, regarding the physical, time-constrained dynamic environment, live-locks and deadlocks, delayed agents, and last needs to minimize the travel time and the total distance traveled. The article proposes an approach using a delegate multi-agent system (D-MAS) in order to meet these requirements. The approach was verified using virtual reality so as to provide a better understanding of the planner's issues. Several coordination rules were proposed and implemented. As a further verification, the proof-of-concept solution was compared to a non-reservation planner. It was shown, that as the number of agents increases, the approach, including the reservations, outperformed its competitor. Various recommendations for the implementation of the planner were formulated. It was concluded that the performance of the planner is sufficient for its future use. The main objective of article was proof-of-concept and determining the functionality of a prototype based on MAS that was in compliance with a modular manufacturing line developed by us. Appl. Sci. 2019, 9, 4515 2 of 20 these requirements and they offer additional features. These include autonomy, decentralization, scalability, and flexibility [4]. Inclusion of these features can be found in [3,5]. Yet, MASs have not been widely adopted in the industrial domain. Several reasons for this were identified by Karnouskos and Leitao [6], particularly, the demand of the industry for mature technologies, initial investments, missing compliance with existing standards, the lack of development methodologies, insufficient interoperability, and integration with physical systems. There are also several areas in MASs-such as continuous, decentralized route planning for physical agents in a dynamic environment-that can still be challenging [7]. Several works were done in this field [8,9]. This article uses delegates in the context of D-MASs (delegate multi-agent systems). Delegate or delegate agents are simple, reactive agents that are created, sent out, and collected by task and resource agents. Next, the features of delegate agents are that agents are virtual entities, not directly connected with anything physical, and that they communicate with other agents through the environment [8]. Primary agents or delegates use behavioral modules called D-MASs that reduce the agents' internal structural complexities; a definition can be found in [9].The idea of our project was to create an intra-logistic system that can transport parts (i.e., manufa...

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“…For the application considered in the study ofĎurica et al [109], a continuous and decentralized route planner is necessary for a modular manufacturing line, therefore they proposed an approach in which a delegate multi-agent system (D-MAS) is used and this approach verified using a simulation with VR.…”

“…In the Discussion (Section 4), a description of the different applications found while conducting this study was made. These applications are related to different purposes that are described and in which that section has been divided: videogames development [30][31][32][33][34][35][36][37][38][39], robot simulation [40,41], interactive virtual environments development [42][43][44][45], human behavior simulation , dissemination of cultural heritage [100,101], urban m development of autonomous vehicles [107,108], and machinery simulation [109][110][111] (Figure 5). Also, simulation of human behavior can be divided into subcategories: crowds simulation [57][58][59][60][61][62][63][64][65][66][67][68], emergency plans [69][70][71][72], work environment [73][74][75][76][77]...…”

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