Context-aware system applied in industrial assembly environment

The objective of this paper is to present an ongoing development of a context-aware system used within industrial environments. The core of the system is so-called Cognitive Model for Robot Group Control. This model is based on well-known concepts of Ubiquitous Computing, and is used to control robot behaviours in specially designed industrial environments. By using sensors integrated within the environment, the system is able to track and analyse changes, and update its informational buffer appropriately. Based on freshly collected information, the Model is able to provide a transformation of high-level contextual information to lower-level information that is much more suitable and understandable for technical systems. The Model uses semantically defined knowledge to define domain of interest, and Bayesian Network reasoning to deal with the uncertain events and ambiguity scenarios that characterize our naturally unstructured world

Modelado semántico de procesos independiente de las infraestructuras de fabricación

En este trabajo se propone un modelo de gestión de procesos de negocio que permita diseñar y desplegar procesos de fabricación con independencia de las características físicas del nivel de planta. Para componer los procesos completos de manera automática, la propuesta se basa en el uso de ontologías como fuente de conocimiento, a partir de las cuales se realizan los razonamientos que permiten adaptar los procesos abstractos a las características especificas de una planta sin intervención humana. Para ello se presenta un modelo conceptual y una arquitectura basada en servicios, en la que la maquinaria industrial actúa como proveedor de servicios y el sistema de gestión de procesos de negocio actúa como consumidor de los mismos. A partir de la propuesta se ha realizado su implementación y se ha diseñado un caso de uso junto con un conjunto de experimentos que han demostrado que se obtiene un alto grado de automatismo en la composición automática de procesos de fabricación, validando la propuesta

Ubiquitous Robotics System for Knowledge-based Auto-configuration System for Service Delivery within Smart Home Environments

The future smart home will be enhanced and driven by the recent advance of the Internet of Things (IoT), which advocates the integration of computational devices within an Internet architecture on a global scale [1, 2]. In the IoT paradigm, the smart home will be developed by interconnecting a plethora of smart objects both inside and outside the home environment [3-5]. The recent take-up of these connected devices within home environments is slowly and surely transforming traditional home living environments. Such connected and integrated home environments lead to the concept of the smart home, which has attracted significant research efforts to enhance the functionality of home environments with a wide range of novel services. The wide availability of services and devices within contemporary smart home environments make their management a challenging and rewarding task. The trend whereby the development of smart home services is decoupled from that of smart home devices increases the complexity of this task. As such, it is desirable that smart home services are developed and deployed independently, rather than pre-bundled with specific devices, although it must be recognised that this is not always practical. Moreover, systems need to facilitate the deployment process and cope with any changes in the target environment after deployment. Maintaining complex smart home systems throughout their lifecycle entails considerable resources and effort. These challenges have stimulated the need for dynamic auto-configurable services amongst such distributed systems. Although significant research has been directed towards achieving auto-configuration, none of the existing solutions is sufficient to achieve auto-configuration within smart home environments. All such solutions are considered incomplete, as they lack the ability to meet all smart home requirements efficiently. These requirements include the ability to adapt flexibly to new and dynamic home environments without direct user intervention. Fulfilling these requirements would enhance the performance of smart home systems and help to address cost-effectiveness, considering the financial implications of the manual configuration of smart home environments. Current configuration approaches fail to meet one or more of the requirements of smart homes. If one of these approaches meets the flexibility criterion, the configuration is either not executed online without affecting the system or requires direct user intervention. In other words, there is no adequate solution to allow smart home systems to adapt dynamically to changing circumstances, hence to enable the correct interconnections among its components without direct user intervention and the interruption of the whole system. Therefore, it is necessary to develop an efficient, adaptive, agile and flexible system that adapts dynamically to each new requirement of the smart home environment. This research aims to devise methods to automate the activities associated with customised service delivery for dynamic home environments by exploiting recent advances in the field of ubiquitous robotics and Semantic Web technologies. It introduces a novel approach called the Knowledge-based Auto-configuration Software Robot (Sobot) for Smart Home Environments, which utilises the Sobot to achieve auto-configuration of the system. The research work was conducted under the Distributed Integrated Care Services and Systems (iCARE) project, which was designed to accomplish and deliver integrated distributed ecosystems with a homecare focus. The auto-configuration Sobot which is the focus of this thesis is a key component of the iCARE project. It will become one of the key enabling technologies for generic smart home environments. It has a profound impact on designing and implementing a high quality system. Its main role is to generate a feasible configuration that meets the given requirements using the knowledgebase of the smart home environment as a core component. The knowledgebase plays a pivotal role in helping the Sobot to automatically select the most appropriate resources in a given context-aware system via semantic searching and matching. Ontology as a technique of knowledgebase representation generally helps to design and develop a specific domain. It is also a key technology for the Semantic Web, which enables a common understanding amongst software agents and people, clarifies the domain assumptions and facilitates the reuse and analysis of its knowledge. The main advantages of the Sobot over traditional applications is its awareness of the changing digital and physical environments and its ability to interpret these changes, extract the relevant contextual data and merge any new information or knowledge. The Sobot is capable of creating new or alternative feasible configurations to meet the system’s goal by utilising inferred facts based on the smart home ontological model, so that the system can adapt to the changed environment. Furthermore, the Sobot has the capability to execute the generated reconfiguration plan without interrupting the running of the system. A proof-of-concept testbed has been designed and implemented. The case studies carried out have shown the potential of the proposed approach to achieve flexible and reliable auto-configuration of the smart home system, with promising directions for future research

Capability-based adaptation of production systems in a changing environment

Today’s production systems have to cope with volatile production environments characterized by frequently changing customer requirements, an increasing number of product variants, small batch sizes, short product life-cycles, the rapid emergence of new technical solutions and increasing regulatory requirements aimed at sustainable manufacturing. These constantly changing requirements call for adaptive and rapidly responding production systems that can adjust to the required changes in processing functions, production capacity and the distribution of the orders. This adaptation is required on the physical, logical and parametric levels. Such adaptivity cannot be achieved without intelligent methodologies, information models and tools to facilitate the adaptation planning and reactive adaptation of the systems. In industry it has been recognized that, because of the often expensive and inefficient adaptation process, companies rarely decide to adapt their production lines. This is mainly due to a lack of sufficient information and documentation about the capabilities of the current system and its lifecycle, as well as a lack of detailed methods for planning the adaptation, which makes it impossible to accurately estimate its scale and cost. Currently, the adaptation of production systems is in practice a human driven process, which relies strongly on the expertise and tacit knowledge of the system integrators or the end-user of the system. This thesis develops a capability-based, computer-aided adaptation methodology, which supports both the human-controlled adaptation planning and the dynamic reactive adaptation of production systems. The methodology consists of three main elements. The first element is the adaptation schema, which illustrates the activities and information flows involved in the overall adaptation planning process and the resources used to support the planning. The adaptation schema forms the backbone of the methodology, guiding the use of other developed elements during both the adaptation planning and reactive adaptation. The second element, which is actually the core of the developed methodology, is the formal ontological resource description used to describe the resources based on their capabilities. The overall resource description utilizes a capability model, which divides the capabilities into simple and combined capabilities. The resources are assigned the simple capabilities they possess. When multiple resources are co-operating, their combined capability can be reasoned out based on the associations defined in the capability model. The adaptation methodology is based on the capability-based matching of product requirements and available system capabilities in the context of the adaptation process. Thus, the third main element developed in this thesis is the framework and rules for performing this capability matching. The approach allows automatic information filtering and the generation of system configuration scenarios for the given requirements, thus facilitating the rapid allocation of resources and the adaptation of systems. Human intelligence is used to validate the automatically-generated scenarios and to select the best one, based on the desired criteria. Based on these results, an approach to evaluating the compatibility of an existing production system with different product requirements has been formulated. This approach evaluates the impact any changes in these requirements may have on the production system. The impact of the changes is illustrated in the form of compatibility graphs, which enable comparison between different product scenarios in terms of the effort required to implement the system adaptation, and the extent to which the current system can be utilized to meet the new requirements. It thus aids in making decisions regarding product and production strategies and adaptation