Towards Python-based Domain-specific Languages for Self-reconfigurable Modular Robotics Research

This paper explores the role of operating system and high-level languages in the development of software and domain-specific languages (DSLs) for self-reconfigurable robotics. We review some of the current trends in self-reconfigurable robotics and describe the development of a software system for ATRON II which utilizes Linux and Python to significantly improve software abstraction and portability while providing some basic features which could prove useful when using Python, either stand-alone or via a DSL, on a self-reconfigurable robot system. These features include transparent socket communication, module identification, easy software transfer and reliable module-to-module communication. The end result is a software platform for modular robots that where appropriate builds on existing work in operating systems, virtual machines, middleware and high-level languages.Comment: Presented at DSLRob 2011 (arXiv:1212.3308

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Evaluating a Prototype Approach to Validating a DDS-based System Architecture for Automated Manufacturing Environments

Data Distribution Services (DDS) are emerging as communication systems in manufacturing environments. One of the key features of a DDS based system is the ability to regain performance levels after the introduction or removal of a DDS participant. In implementing a DDS participant to an existing system, message transport speed and message latency is often sacrificed due to protection problems in OEM software. Validity and suitability for integration of OpenDDS specifically, a manufacturing system is evaluated by defining two implementation scenarios; a flexible approach with a dedicated DDS participant application, and a high speed approach integrating the OpenDDS API directly in the target application. The system is validated by monitoring performance, efficiency and robustness in use and implementation. This result is part of a system architecture, developed for project Smart Industrial Robotics (SInBot), that focuses on maximizing the efficient use of mobile industrial robots during medium sized production runs. This modular system architecture is based on distributed intelligence and decentralized control to enable online reconfiguration of industrial robots in manufacturing facilities

BRAHMS: Novel middleware for integrated systems computation

Biological computational modellers are becoming increasingly interested in building large, eclectic models, including components on many different computational substrates, both biological and non-biological. At the same time, the rise of the philosophy of embodied modelling is generating a need to deploy biological models as controllers for robots in real-world environments. Finally, robotics engineers are beginning to find value in seconding biomimetic control strategies for use on practical robots. Together with the ubiquitous desire to make good on past software development effort, these trends are throwing up new challenges of intellectual and technological integration (for example across scales, across disciplines, and even across time) - challenges that are unmet by existing software frameworks. Here, we outline these challenges in detail, and go on to describe a newly developed software framework, BRAHMS. that meets them. BRAHMS is a tool for integrating computational process modules into a viable, computable system: its generality and flexibility facilitate integration across barriers, such as those described above, in a coherent and effective way. We go on to describe several cases where BRAHMS has been successfully deployed in practical situations. We also show excellent performance in comparison with a monolithic development approach. Additional benefits of developing in the framework include source code self-documentation, automatic coarse-grained parallelisation, cross-language integration, data logging, performance monitoring, and will include dynamic load-balancing and 'pause and continue' execution. BRAHMS is built on the nascent, and similarly general purpose, model markup language, SystemML. This will, in future, also facilitate repeatability and accountability (same answers ten years from now), transparent automatic software distribution, and interfacing with other SystemML tools. (C) 2009 Elsevier Ltd. All rights reserved

Reconfiguration model using knowledge based engineering systems

Globalization has forced enterprises to adapt their products and services to remain competitive in the free market. Manufacture plays an important role in the competitive aspect; it is where an innovation in the production system could lead to business advantage. These innovations usually involve the key elements in manufacturing systems: machines, tools and resources administration. A reconfigurable manufacturing system (RMS) is one designed for rapid change in its structure and components, to quickly adjust its production capacity and functionality in response to sudden market or intrinsic system changes. However, reconfiguration alone is not enough since it will provide information to produce a certain item but it won t provide the components that will automate the machine tool for mass production. The process of automation of machine tools is known as retrofit, process being developed and researched in emergent economies. The current retrofit kits are expensive and are not tailor made, thus, they are not attractive for small and medium enterprises. This article describes a solution for fast reconfiguration of machine tools using the Knowledge Based-Engineering System methodology (KBES) that allows to obtain, structure and manage the knowledge generated in a determined engineering process, in this case, the reconfiguration processHincapié Montoya, M.; Güemes-Castorena, D.; Contero, M.; Ramírez-Cadena, M.; Diaz, C. (2015). Reconfiguration model using knowledge based engineering systems. Journal of Manufacturing Technology Research. 6(1):63-81. http://hdl.handle.net/10251/77893S63816

The contribution of industry 4.0 technologies to increase internal and external operational flexibility of production systems

Manufacturing flexibility is recognized as an essential competitive factor in the company's operational strategy as a response to market uncertainties and turbulence. Industry 4.0 emerges as a new industrial paradigm that allows meeting these types of needs of manufacturing companies, focusing on the creation of an intelligent system along the entire value chain that allows the achievement of flexible and adaptive processes. However, the academic literature has not yet presented empirical evidence on how each specific Industry 4.0 technology can contribute to operational flexibility requirements. Although Industry 4.0 is treated as a solution to this need, it is known that there are different types of implementations of Industry 4.0 depending on the operational objectives pursued and the characteristics of the companies. Therefore, the technological sets of Industry 4.0 can have different forms of contribution to achieve greater flexibility in production processes. The aim of this thesis is to create a framework to help companies implement flexible operations in the context of Industry 4.0. The study followed a mixed approach, combining qualitative and quantitative methods. In quantitative terms, the thesis presents two survey research. The first was conducted with 94 companies in the machinery and equipment sector, through which the effect that different operational objectives – including flexibility – have on the definition of technological arrangements in Industry 4.0, is analyzed. The second was conducted with 379 companies, with the objective of analyzing how the smart supply chain concept contributes to the flexibility of the supply chain, especially in the context of uncertainties.. On the other hand, in qualitative terms, the thesis presents a multi-case study in 11 leading manufacturing companies in the implementation of 4.0 technologies, aiming to understand how these technologies are implemented to achieve different operational flexibility requirements. The present thesis demonstrates that, in fact, 4.0 technologies contribute to operational flexibility, but also explores the limitations and nuances of these contributions in different situations. The main contribution of this study is to provide empirical evidence of the effectiveness of different technologies used in a combined way to increase operational flexibility at its different levels.A flexibilidade da manufatura é reconhecida como um fator competitivo essencial na estratégia operacional das empresas, como resposta a necessidades do mercado, especialmente diante de incertezas e turbulências. A Industria 4.0 surge como um novo paradigma industrial que permite atender esse tipo de necessidades das empresas manufatureiras, sendo seu foco a criação de um sistema inteligente ao longo de toda a cadeia de valor que possibilita a obtenção de processos flexíveis e adaptativos. Contudo, a literatura acadêmica ainda não tem apresentado evidências empíricas sobre a forma como cada tecnologia específica da Indústria 4.0 pode contribuir para os requisitos de flexibilidade operacional. Embora Industria 4.0 seja apresentada como uma solução para essa necessidade, é sabido que existem diferentes tipos de implementação da Indústria 4.0 que dependem dos objetivos operacionais almejados e das características das empresas. Portanto, os conjuntos tecnológicos da Indústria 4.0 podem ter diferentes formas de contribuição para alcançar uma maior flexibilidade dos processos de produção. O objetivo desta tese é criar um framework para auxiliar as empresas na implementação de operações flexíveis no contexto da Indústria 4.0. O estudo seguiu uma abordagem mista, combinando métodos qualitativos e quantitativo. Em termos quantitativos, a tese apresenta duas pesquisas survey. A primeira foi conduzida com 94 empresas do setor de máquinas e equipamentos, através da qual se analisa o efeito que diferentes objetivos operacionais dentre eles a flexibilidade possuem sobre a definição de arranjos tecnológicos da Indústria 4.0. A segunda foi conduzida com 379 empresas, com objetivo de analisar como o conceito de smart supply chain contribui para a flexibilidade da cadeia de suprimento, principalmente no contexto de incertezas. Por outro lado, em termos qualitativos, a tese apresenta um estudo multicasos em 11 empresas de manufatura líderes na implantação de tecnologias 4.0, visando entender a forma como essas tecnologias são implementadas para alcançar diferentes requisitos de flexibilidade operacional. A presente tese demonstra que, de fato, as tecnologias 4.0 contribuem para a flexibilidade operacional, mas também explora as limitações e nuances dessas contribuições em diferentes situações. A principal contribuição deste estudo é fornecer evidências empíricas da efetividade de diferentes tecnologias utilizadas de forma combinada para incrementar a flexibilidade operacional nos seus diferentes níveis

Stimulus Pulse-Based Distributed Control for the Locomotion of a UBot Modular Robot

A distributed control algorithm based on a stimulus pulse signal is proposed in this paper for the locomotion of a Modular Self-reconfigurable Robot (MSRR). This approach can adapt effectively to the dynamic changes in the MSRR's topological configuration: the functional role of the configuration can be recognized through local topology detection, dynamic ID address allocation and local topology matching, such that the features of the entire configuration can be identified and thereby the corresponding stimulus signals can be chosen to control the whole system for coordinated locomotion. This approach has advantages over centralized control in terms of flexibility and robustness, and communication efficiency is not limited by the module number, which can realize coordinated locomotion control conveniently (especially for configurations made up of massive modules and characterized by a chain style or a quadruped style)