Service-oriented agents for collaborative industrial automation and production systems

Service-oriented Multi-Agent Systems (SoMAS) is an approach to combine the fundamental characteristics of service-oriented and multi-agent methods into a new platform for industrial automation. Several research works already targeted the connection of these technologies, presenting different perspectives in how and why to join them. This research focuses on available efforts and solutions in the area of SoMAS and explains the idea behind the service-oriented agents in industrial automation. A SoMAS system is mainly composed by shared resources in form of services and their providing/requesting agents. The paper also discusses the required engineering aspects of these systems, from the internal anatomy to the interaction patterns. Parameters of flexibility, reconfiguration, autonomy and reduced development efforts were considered and they should be the trademark of SoMAS. Aiming to illustrate the proposed approach, an example of service-oriented automation agents is given.The authors would like to thank the European Commission and the partners of the EU IST FP6 project “Service-Oriented Cross-layer infrastructure for Distributed smart Embedded devices” (SOCRADES), the EU FP6 "Network of Excellence for Innovative Production Machines and Systems” (I*PROMS), and the EC ICT FP7 project “Cooperating Objects Network of Excellence” (CONET) for their support

Enhancing service-oriented holonic multi-agent systems with self-organization

Multi-agents systems and holonic manufacturing systems are suitable approaches to design a new and alternative class of production control systems, based on the decentralization of control functions over distributed autonomous and cooperative entities. However, in spite of their enormous potential they lack some aspects related to interoperability, migration, optimisation in decentralised structures and truly self-adaptation. This paper discusses the advantages of combining these paradigms with complementary paradigms, such as service-oriented architectures, and enhancing them with biologically inspired algorithms and techniques, such as emergent behaviour and self-organization, to reach a truly robust, agile and adaptive control system. An example of applying a stigmergy-based algorithm to dynamically route pallets in a production system is also provided

A framework of integrating knowledge of human factors to facilitate HMI and collaboration in intelligent manufacturing

Recent developments in the field of intelligent manufacturing have led to increased levels of automation and robotic operators becoming commonplace within manufacturing processes. However, the human component of such systems remains prevalent, resulting in significant disturbance and uncertainty. Consequently, semi-automated processes are difficult to optimise. This paper studies the relationships between robotic and human operators to develop the understanding of how the human influence affects these production processes, and proposes a framework to integrate and implement knowledge of such factors, with the aim of improving Human-Machine-Interaction, facilitating bi-directional collaboration, and increasing productivity and quality, supported by an example case-study

An agent-based approach for the dynamic and decentralized service reconfiguration in collaborative production scenarios

Future industrial systems endorse the implementation of innovative paradigms addressing the continuous flexibility, reconfiguration, and evolution to face the volatility of dynamic markets demanding complex and customized products. Smart manufacturing relies on the capability to adapt and evolve to face changes, particularly by identifying, on-the-fly, opportunities to reconfigure its behavior and functionalities and offer new and more adapted services. This paper introduces an agent-based approach for service reconfiguration that allows the identification of the opportunities for reconfiguration in a pro-active and dynamic manner, and the implementation on-the-fly of the best strategies for the service reconfiguration that will lead to a better production efficiency. The developed prototype for a flexible manufacturing system case study allowed to verify the feasibility of greedy local service reconfiguration for competitive and collaborative industrial automation situations.info:eu-repo/semantics/publishedVersio

Industrial agents in the era of service-oriented architectures and cloudbased industrial infrastructures

The umbrella paradigm underpinning novel collaborative industrial systems is to consider the set of intelligent system units as a conglomerate of distributed, autonomous, intelligent, proactive, fault-tolerant, and reusable units, which operate as a set of cooperating entities (Colombo and Karnouskos, 2009). These entities are forming an evolvable infrastructure, entering and/or going out (plug-in/plugout) in an asynchronous manner. Moreover, these entities, having each of them their own functionalities, data, and associated information are now connected and able to interact. They are capable of working in a proactive manner, initiating collaborative actions and dynamically interacting with each other in order to achieve both local and global objectives.info:eu-repo/semantics/publishedVersio

Towards robustness and self-organization of ESB-based solutions using service life-cycle management

Enterprise Service Bus (ESB) is a middleware infra-structure that provides a way to integrate loosely-coupled heterogeneous software applications based on the Service Oriented Architecture principles. The life-cycle management of services in such highly changing environments is a critical issue for the component’s reuse, maintenance and operation. This work introduces a service life-cycle management module that extends the traditional functionalities with advanced monitoring and data analytics to contribute for the robustness and self-organization of networks of clusters based on ESB platforms. The realization of this module was embedded in the JBoss ESB, considering a sniffer mechanism to collect relevant details of the service messages crossing the bus. A Liferay portal was created to display information related to the services’ health.Um Barramento de Serviços (BS) é um middleware que oferece uma infraestrutura que permite a integração de aplicações heterogéneas, com base nos princípios das Arquiteturas Orientadas aos Serviços. A gestão do ciclo de vida dos serviços em tais ambientes altamente dinâmicos é um problema crítico com impacto na operação e manutenção do sistema, bem como na capacidade de reutilização de componentes. Este trabalho apresenta um módulo de gestão do ciclo de vida de serviços, que acrescenta às funcionalidades tradicionais a monitorização avançada e a análise de dados, no sentido de contribuir para a robustez e auto-organização de redes de agregados computacionais baseados em plataformas BS. O módulo foi integrado no JBoss ESB e utilizou-se um sniffer para recolher os detalhes das mensagens que se encontram a ser trocadas no barramento. Foi criado um portal Liferay para apresentar a informação relativa à saúde dos serviços.This work was supported by the European Union FP7 Programme under the ARUM project No. 31405

Improving human-robot-interaction utilizing learning and intelligence: a human factors-based approach

Several decades of development in the fields of robotics and automation have resulted in human-robot interaction is commonplace, and the subject of intense study. These interactions are particularly prevalent in manufacturing, where human operators (HOs) have been employed in numerous robotics and automation tasks. The presence of HOs continues to be a source of uncertainty in such systems, despite the study of human factors, in an attempt to better understand these variations in performance. Concurrent developments in intelligent manufacturing present opportunities for adaptability within robotic control. This article examines relevant human factors and develops a framework for integrating the necessary elements of intelligent control and data processing to provide appropriate adaptability to robotic elements, consequently improving collaborative interaction with human colleagues. A neural network-based learning approach is used to predict the influence on human task performance and use these predictions to make informed changes to programed behavior, and a methodology developed to explore the application of learning techniques to this area further. This article is supported by an example case study, in which a simulation model is used to explore the application of the developed system, and its performance in a real-world production scenario. The simulation results reveal that adaptability can be realized with some relatively simple techniques and models if applied in the right manner and that such adaptability is helpful to tackle the issue of performance disparity in manufacturing operations

An agent-based reinforcement learning approach to improve human-robot-interaction in manufacturing

This work is aimed at the understanding and application of several emerging technologies as they relate to improving the interactions which occur between robotic operators and their human colleagues across a range of manufacturing processes. These interactions are problematic, as variation in performance of human beings remains one of the largest sources of disturbances within such systems, with potentially significant implications for productivity if it continues unmitigated. The problem remains for the most part unaddressed, despite these interactions becoming increasingly prevalent as the rate of adoption of automation technologies increases. By reconciling multiple areas encompassed by the wider domain of intelligent manufacturing, the presented work identifies a methodology and a set of software tools which leverage the strengths of neural-network-based reinforcement learning to develop intelligent software agents capable of adaptable behaviour in response to observed environmental changes. The methodology further focuses on developing representative simulation models for these interactions following a pattern of generalisation, to effectively represent both human and robotic elements, and facilitate implementation. By learning through their interaction with the simulated manufacturing environment, these agents can determine an appropriate policy, by which to autonomously adjust their operating parameters, as a response to changes in their human colleagues. This adaptability is demonstrated to enable the intelligent agents to determine an action policy which results in less observed idle time, along with improved leanness and overall productivity, over multiple scenarios. The findings of the work suggest that software agents that make use of a reinforcement based learning approach are well suited to the task of enabling robotic adaptability in such a way, and the developed methodology provides a platform for further development and exploration, along with numerous insights into the effective development of these agents