Bio-inspired multi-agent systems for reconfigurable manufacturing systems

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Abstract

The current market's demand for customization and responsiveness is a major challenge for producing intelligent, adaptive manufacturing systems. The Multi-Agent System (MAS) paradigm offers an alternative way to design this kind of system based on decentralized control using distributed, autonomous agents, thus replacing the traditional centralized control approach. The MAS solutions provide modularity, flexibility and robustness, thus addressing the responsiveness property, but usually do not consider true adaptation and re-configuration. Understanding how, in nature, complex things are performed in a simple and effective way allows us to mimic nature's insights and develop powerful adaptive systems that able to evolve, thus dealing with the current challenges imposed on manufacturing systems. The paper provides an overview of some of the principles found in nature and biology and analyses the effectiveness of bio-inspired methods, which are used to enhance multi-agent systems to solve complex engineering problems, especially in the manufacturing field. An industrial automation case study is used to illustrate a bio-inspired method based on potential fields to dynamically route pallets.

Introduction

The current global economy imposes new challenges for manufacturing companies, with cost, quality and responsiveness being the three critical foundations on which every manufacturing company stands to remain competitive (ElMaraghy, 2006). Under these circumstances, manufacturing systems are required to be more flexible, robust and reconfigurable, supporting the agile response to the changing conditions through their dynamic re-configuration on the fly (i.e., without stopping, re-programming or re-starting the processes or the other system components). Since the systems are more complex, distributed and reconfigurable, the probability of the system malfunction also increases (Trentesaux, 2009).

Since the traditional approaches, based on centralized, rigid structures, do not have enough flexibility to cope with modularity, flexibility, robustness and re-configuration, several paradigms have been introduced over the last few years: Multi-Agent Systems (MAS) (Wooldridge, 2002), Holonic Manufacturing Systems (HMS) (Deen, 2003, Leitão and Restivo, 2006) and Bionic Manufacturing Systems (BMS) (Okino, 1993). In spite of their natural differences, these paradigms propose distributed, autonomous and adaptive manufacturing systems, which can respond promptly and correctly to external changes. These paradigms differ from the conventional approaches due to their inherent ability to adapt to changes without external interventions. In addition, the HMS and BMS paradigms indicate that hierarchy is needed to guarantee the inter-entity conflict resolution and to maintain overall system coherence and objectivity in the face of the individual, autonomous attitude of the entities (Sousa et al., 1999).

The work on MAS, HMS and BMS provides a good framework to rise to the challenge of developing a new class of adaptive and reconfigurable manufacturing systems that will support robustness and re-configurability quite naturally (see the surveys of Leitão, 2009a, Pechoucek and Marik, 2008). However, the current application of such paradigms usually does not consider self-adaptation and self-organization, which results in the systems becoming increasingly reconfigurable, adaptive, organized and efficient.

In biology and nature, complex systems behave simply because of the cooperation of individuals, who are very simple, with very limited cognitive skills (e.g., the colonies of ants and bees). Biological insights have been the source of inspiration for the development of several techniques and methods to solve complex engineering problems, such as logistics and traffic optimization, telecommunications networks, economic markets and production systems (Leitão, 2009b). Multi-Agent Systems have already inherited certain ideas derived from biology and nature, but they can be enhanced with other biological insights, notably the swarm intelligence and self-organization, to obtain more responsive, adaptive systems that address the current requirements imposed on manufacturing systems. In particular, bio-inspired techniques can contribute to obtaining manufacturing systems with the desired characteristics of flexibility, robustness, re-configuration and responsiveness.

The motivation of this paper is to understand how bio-inspired techniques can be used to solve complex engineering problems. Thus, some biological phenomena are studied, some of the existing bio-inspired applications are analyzed, and then the real benefits of bio-inspired MAS for solving the current manufacturing control problems are examined. A real implementation of a bio-inspired solution for routing pallets in a flexible manufacturing system is described to illustrate its suitability.

The remainder of this paper is organized as follows. Section 2 provides an overview of biological phenomena, and Section 3 introduces some bio-inspired techniques and methods used to solve complex problems, especially manufacturing problems. Section 4 discusses the suitability of bio-inspired multi-agent systems solutions for the manufacturing field, and Section 5 describes a bio-inspired solution based on potential fields for an industrial automation case study. Finally, Section 6 presents our conclusions and our prospects for future research.

Section snippets

Basic concepts found in biology

Nature offers plenty of powerful mechanisms, refined by millions of years of evolution, to handle emergent and evolvable environments (Leitão, 2009b). This section tries to show how complex things behave simply in nature and biology, introducing the concepts of swarm intelligence (Section 2.1) and evolution and self-organization (Section 2.2).

Survey of bio-inspired applications for solving complex problems

Several researchers used biological behavior (e.g., colonies of insects) to solve complex mathematical engineering problems. In this section, bio-inspired techniques and methods in engineering are briefly reviewed, with special attention to their applicability in manufacturing.

Applicability of bio-inspired systems in manufacturing

The analysis in the previous section shows the tremendous potential of using of bio-inspired systems to solve complex engineering problems. This section discusses the applicability and benefits of combining bio-inspired techniques with multi-agent systems in the manufacturing domain in order to address the current challenges.

The MAS paradigm has already inherited biological insights (Barbosa and Leitão, 2010):

  • Distributed nature—multi-agent systems are based on a set of distributed, autonomous

An automation case study: a bio-inspired approach

An experimental case study was used to demonstrate the applicability of bio-inspired multi-agent systems in manufacturing. For this purpose, the full-size flexible manufacturing system (FMS), located at the AIP-PRIMECA production center (Université de Valenciennes et Hainaut-Cambrésis, France), was used. The FMS is composed of seven work stations (i.e., assembly robots, quality control units and load/unload units), interconnected by a flexible conveyor system that uses shuttles to move pallets

Conclusions

This paper analyzed some mechanisms found in biology and nature, especially swarm intelligence and self-organization, and tried to understand their potential benefits to solve complex engineering problems. Special attention was devoted to the existing bio-inspired applications, particularly in manufacturing. This paper also discussed the application of bio-inspired techniques to enhance multi-agent systems in the different manufacturing areas and considered how to achieve a greater adoption in

Acknowledgments

The experimental work was performed by a project team, including Nadine Zbib, Cyrille Pach, Yves Sallez, Thierry Berger, with the help and support of the AIP-PRIMECA team. The authors wish to thank all of them for their work.

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    Note: This paper is an extended version of a paper presented at the 10th IFAC Workshop on Intelligent Manufacturing Systems (IMS’10), selected by the organization to be submitted to the EAAI Journal.

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