Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

Use of bio-inspired techniques to solve complex engineering problems: industrial automation case study

Nowadays local markets have disappeared and the world lives in a global economy. Due to this reality, every company virtually competes with all others companies in the world. In addition to this, markets constantly search products with higher quality at lower costs, with high customization. Also, products tend to have a shorter period of life, making the demanding more intense. With this scenario, companies, to remain competitive, must constantly adapt themselves to the market changes, i.e., companies must exhibit a great degree of self-organization and self-adaptation. Biology with the millions of years of evolution may offer inspiration to develop new algorithms, methods and techniques to solve real complex problems. As an example, the behaviour of ants and bees, have inspired researchers in the pursuit of solutions to solve complex and evolvable engineering problems. This dissertation has the goal of explore the world of bio-inspired engineering. This is done by studying some of the bio-inspired solutions and searching for bio-inspired solutions to solve the daily problems. A more deep focus will be made to the engineering problems and particularly to the manufacturing domain. Multi-agent systems is a concept aligned with the bio-inspired principles offering a new approach to develop solutions that exhibit robustness, flexibility, responsiveness and re-configurability. In such distributed bio-inspired systems, the behaviour of each entity follows simple few rules, but the overall emergent behaviour is very complex to understand and to demonstrate. Therefore, the design and simulation of distributed agent-based solutions, and particularly those exhibiting self-organizing, are usually a hard task. Agent Based Modelling (ABM) tools simplifies this task by providing an environment for programming, modelling and simulating agent-based solutions, aiming to test and compare alternative model configurations. A deeply analysis of the existing ABM tools was also performed aiming to select the platform to be used in this work. Aiming to demonstrate the benefits of bio-inspired techniques for the industrial automation domain, a production system was used as case study for the development of a self-organizing agent-based system developed using the NetLogo tool. Hoje em dia os mercados locais desapareceram e o mundo vive numa economia global. Devido a esta realidade, cada companhia compete, virtualmente, com todas as outras companhias do mundo. A acrescentar a isto, os mercados estão constantemente à procura de produtos com maior qualidade a preços mais baixos e com um grande nível de customização Também, os produtos tendem a ter um tempo curto de vida, fazendo com que a procura seja mais intensa. Com este cenário, as companhias, para permanecer competitivas, têm que se adaptar constantemente de acordo com as mudanças de mercado, i.e., as companhias têm que exibir um alto grau de auto-organização e auto-adaptação. A biologia com os milhões de anos de evolução, pode oferecer inspiração para desenvolver novos algoritmos, métodos e técnicas para resolver problemas complexos reais. Como por exemplo, o comportamento das formigas e das abelhas inspiraram investigadores na descoberta de soluções para resolver problemas complexos e evolutivos de engenharia. Esta dissertação tem como objectivo explorar o mundo da engenharia bio-inspirada. Isto é feito através do estudo de algumas das soluções bio-inspiradas existentes e da procura de soluções bio-inspiradas para resolver os problemas do dia-a-dia. Uma atenção especial vai ser dada aos problemas de engenharia e particularmente aos problemas do domínio da manufactura. Os sistemas multi-agentes são um conceito que estão em linha com os princípios bio-inspirados oferecendo uma abordagem nova para desenvolver soluções que exibam robustez, flexibilidade, rapidez de resposta e reconfiguração. Nestes sistemas distribuídos bio-inspirados, o comportamento de cada entidade segue um pequeno conjunto de regras simples, mas o comportamento emergente global é muito complexo de perceber e de demonstrar. Por isso, o desenho e simulação de soluções distribuídas de agentes, e particularmente aqueles que exibem auto-organização, são normalmente uma tarefa árdua. As ferramentas de Modelação Baseada de Agentes (MBA) simplificam esta tarefa providenciando um ambiente para programar, modelar e simular, com o objectivo de testar e comparar diferentes configurações do modelo. Uma análise mais aprofundada das ferramentas MBA foi também efectuada tendo como objectivo seleccionar a plataforma a usar neste trabalho

Engineering complex systems with multigroup agents

Doctor of PhilosophyComputing and Information SciencesScott A. DeLoachAs sensor prices drop and computing devices continue to become more compact and powerful, computing capabilities are being embedded throughout our physical environment. Connecting these devices in cyber-physical systems (CPS) enables applications with significant societal impact and economic benefit. However, engineering CPS poses modeling, architecture, and engineering challenges and, to fully realize the desired benefits, many outstanding challenges must be addressed. For the cyber parts of CPS, two decades of work in the design of autonomous agents and multiagent systems (MAS) offers design principles for distributed intelligent systems and formalizations for agent-oriented software engineering (AOSE). MAS foundations offer a natural fit for enabling distributed interacting devices. In some cases, complex control structures such as holarchies can be advantageous. These can motivate complex organizational strategies when implementing such systems with a MAS, and some designs may require agents to act in multiple groups simultaneously. Such agents must be able to manage their multiple associations and assignments in a consistent and unambiguous way. This thesis shows how designing agents as systems of intelligent subagents offers a reusable and practical approach to designing complex systems. It presents a set of flexible, reusable components developed for OBAA++, an organization-based architecture for single-group MAS, and shows how these components were used to develop the Adaptive Architecture for Systems of Intelligent Systems (AASIS) to enable multigroup agents suitable for complex, multigroup MAS. This work illustrates the reusability and flexibility of the approach by using AASIS to simulate a CPS for an intelligent power distribution system (IPDS) operating two multigroup MAS concurrently: one providing continuous voltage control and a second conducting discrete power auctions near sources of distributed generation

DECISION SUPPORT MODEL IN FAILURE-BASED COMPUTERIZED MAINTENANCE MANAGEMENT SYSTEM FOR SMALL AND MEDIUM INDUSTRIES

Maintenance decision support system is crucial to ensure maintainability and reliability of equipments in production lines. This thesis investigates a few decision support models to aid maintenance management activities in small and medium industries. In order to improve the reliability of resources in production lines, this study introduces a conceptual framework to be used in failure-based maintenance. Maintenance strategies are identified using the Decision-Making Grid model, based on two important factors, including the machines’ downtimes and their frequency of failures. The machines are categorized into three downtime criterions and frequency of failures, which are high, medium and low. This research derived a formula based on maintenance cost, to re-position the machines prior to Decision-Making Grid analysis. Subsequently, the formula on clustering analysis in the Decision-Making Grid model is improved to solve multiple-criteria problem. This research work also introduced a formula to estimate contractor’s response and repair time. The estimates are used as input parameters in the Analytical Hierarchy Process model. The decisions were synthesized using models based on the contractors’ technical skills such as experience in maintenance, skill to diagnose machines and ability to take prompt action during troubleshooting activities. Another important criteria considered in the Analytical Hierarchy Process is the business principles of the contractors, which includes the maintenance quality, tools, equipments and enthusiasm in problem-solving. The raw data collected through observation, interviews and surveys in the case studies to understand some risk factors in small and medium food processing industries. The risk factors are analysed with the Ishikawa Fishbone diagram to reveal delay time in machinery maintenance. The experimental studies are conducted using maintenance records in food processing industries. The Decision Making Grid model can detect the top ten worst production machines on the production lines. The Analytical Hierarchy Process model is used to rank the contractors and their best maintenance practice. This research recommends displaying the results on the production’s indicator boards and implements the strategies on the production shop floor. The proposed models can be used by decision makers to identify maintenance strategies and enhance competitiveness among contractors in failure-based maintenance. The models can be programmed as decision support sub-procedures in computerized maintenance management systems

Best matching processes in distributed systems

The growing complexity and dynamic behavior of modern manufacturing and service industries along with competitive and globalized markets have gradually transformed traditional centralized systems into distributed networks of e- (electronic) Systems. Emerging examples include e-Factories, virtual enterprises, smart farms, automated warehouses, and intelligent transportation systems. These (and similar) distributed systems, regardless of context and application, have a property in common: They all involve certain types of interactions (collaborative, competitive, or both) among their distributed individuals—from clusters of passive sensors and machines to complex networks of computers, intelligent robots, humans, and enterprises. Having this common property, such systems may encounter common challenges in terms of suboptimal interactions and thus poor performance, caused by potential mismatch between individuals. For example, mismatched subassembly parts, vehicles—routes, suppliers—retailers, employees—departments, and products—automated guided vehicles—storage locations may lead to low-quality products, congested roads, unstable supply networks, conflicts, and low service level, respectively. This research refers to this problem as best matching, and investigates it as a major design principle of CCT, the Collaborative Control Theory. The original contribution of this research is to elaborate on the fundamentals of best matching in distributed and collaborative systems, by providing general frameworks for (1) Systematic analysis, inclusive taxonomy, analogical and structural comparison between different matching processes; (2) Specification and formulation of problems, and development of algorithms and protocols for best matching; (3) Validation of the models, algorithms, and protocols through extensive numerical experiments and case studies. The first goal is addressed by investigating matching problems in distributed production, manufacturing, supply, and service systems based on a recently developed reference model, the PRISM Taxonomy of Best Matching. Following the second goal, the identified problems are then formulated as mixed-integer programs. Due to the computational complexity of matching problems, various optimization algorithms are developed for solving different problem instances, including modified genetic algorithms, tabu search, and neighbourhood search heuristics. The dynamic and collaborative/competitive behaviors of matching processes in distributed settings are also formulated and examined through various collaboration, best matching, and task administration protocols. In line with the third goal, four case studies are conducted on various manufacturing, supply, and service systems to highlight the impact of best matching on their operational performance, including service level, utilization, stability, and cost-effectiveness, and validate the computational merits of the developed solution methodologies

A decentralized metaheuristic approach applied to the FMS scheduling problem

La programación de FMS ha sido uno de los temas más populares para los investigadores. Se han entregado varios enfoques para programar los FMS, incluidas las técnicas de simulación y los métodos analíticos. Las metaheurísticas descentralizadas pueden verse como una forma en que la población se divide en varias subpoblaciones, con el objetivo de reducir el tiempo de ejecución y el número de evaluaciones, debido a la separación del espacio de búsqueda. La descentralización es una ruta de investigación prominente en la programación, por lo que el costo de la computación se puede reducir y las soluciones se pueden encontrar más rápido, sin penalizar la función objetivo. En este proyecto, se propone una metaheurística descentralizada en el contexto de un problema de programación flexible del sistema de fabricación. La principal contribución de este proyecto es analizar otros tipos de división del espacio de búsqueda, particularmente aquellos asociados con el diseño físico del FMS. El desempeño del enfoque descentralizado se validará con los puntos de referencia de programación de FMS.FMS scheduling has been one of the most popular topics for researchers. A number of approaches have been delivered to schedule FMSs including simulation techniques and analytical methods. Decentralized metaheuristics can be seen as a way where the population is divided into several subpopulations, aiming to reduce the run time and the numbers of evaluation, due to the separation of the search space. Decentralization is a prominent research path in scheduling so the computing cost can be reduced and solutions can be found faster, without penalizing the objective function. In this project, a decentralized metaheuristic is proposed in the context of a flexible manufacturing system scheduling problem. The main contribution of this project is to analyze other types of search space division, particularly those associated with the physical layout of the FMS. The performance of the decentralized approach will be validated with FMS scheduling benchmarks.Ingeniero (a) IndustrialPregrad

Advances in Intelligent Robotics and Collaborative Automation

This book provides an overview of a series of advanced research lines in robotics as well as of design and development methodologies for intelligent robots and their intelligent components. It represents a selection of extended versions of the best papers presented at the Seventh IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IDAACS 2013 that were related to these topics. Its contents integrate state of the art computational intelligence based techniques for automatic robot control to novel distributed sensing and data integration methodologies that can be applied to intelligent robotics and automation systems. The objective of the text was to provide an overview of some of the problems in the field of robotic systems and intelligent automation and the approaches and techniques that relevant research groups within this area are employing to try to solve them.The contributions of the different authors have been grouped into four main sections:• Robots• Control and Intelligence• Sensing• Collaborative automationThe chapters have been structured to provide an easy to follow introduction to the topics that are addressed, including the most relevant references, so that anyone interested in this field can get started in the area

Advances in Intelligent Robotics and Collaborative Automation

This book provides an overview of a series of advanced research lines in robotics as well as of design and development methodologies for intelligent robots and their intelligent components. It represents a selection of extended versions of the best papers presented at the Seventh IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IDAACS 2013 that were related to these topics. Its contents integrate state of the art computational intelligence based techniques for automatic robot control to novel distributed sensing and data integration methodologies that can be applied to intelligent robotics and automation systems. The objective of the text was to provide an overview of some of the problems in the field of robotic systems and intelligent automation and the approaches and techniques that relevant research groups within this area are employing to try to solve them.The contributions of the different authors have been grouped into four main sections:• Robots• Control and Intelligence• Sensing• Collaborative automationThe chapters have been structured to provide an easy to follow introduction to the topics that are addressed, including the most relevant references, so that anyone interested in this field can get started in the area

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments