1,718 research outputs found
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
Ontologies for Industry 4.0
The current fourth industrial revolution, or ‘Industry 4.0’ (I4.0), is driven by digital data, connectivity, and cyber systems, and it has the potential to create impressive/new business opportunities. With the arrival of I4.0, the scenario of various intelligent systems interacting reliably and securely with each other becomes a reality which technical systems need to address. One major aspect of I4.0 is to adopt a coherent approach for the semantic communication in between multiple intelligent systems, which include human and artificial (software or hardware) agents. For this purpose, ontologies can provide the solution by formalizing the smart manufacturing knowledge in an interoperable way. Hence, this paper presents the few existing ontologies for I4.0, along with the current state of the standardization effort in the factory 4.0 domain and examples of real-world scenarios for I4.0.Peer ReviewedPostprint (published version
Towards edge robotics: the progress from cloud-based robotic systems to intelligent and context-aware robotic services
Current robotic systems handle a different range of applications such as video surveillance, delivery
of goods, cleaning, material handling, assembly, painting, or pick and place services. These systems
have been embraced not only by the general population but also by the vertical industries to
help them in performing daily activities. Traditionally, the robotic systems have been deployed in
standalone robots that were exclusively dedicated to performing a specific task such as cleaning the
floor in indoor environments. In recent years, cloud providers started to offer their infrastructures
to robotic systems for offloading some of the robot’s functions. This ultimate form of the distributed
robotic system was first introduced 10 years ago as cloud robotics and nowadays a lot of robotic solutions
are appearing in this form. As a result, standalone robots became software-enhanced objects
with increased reconfigurability as well as decreased complexity and cost. Moreover, by offloading
the heavy processing from the robot to the cloud, it is easier to share services and information from
various robots or agents to achieve better cooperation and coordination.
Cloud robotics is suitable for human-scale responsive and delay-tolerant robotic functionalities
(e.g., monitoring, predictive maintenance). However, there is a whole set of real-time robotic applications
(e.g., remote control, motion planning, autonomous navigation) that can not be executed with
cloud robotics solutions, mainly because cloud facilities traditionally reside far away from the robots.
While the cloud providers can ensure certain performance in their infrastructure, very little can be
ensured in the network between the robots and the cloud, especially in the last hop where wireless
radio access networks are involved. Over the last years advances in edge computing, fog computing,
5G NR, network slicing, Network Function Virtualization (NFV), and network orchestration are stimulating
the interest of the industrial sector to satisfy the stringent and real-time requirements of their
applications. Robotic systems are a key piece in the industrial digital transformation and their benefits
are very well studied in the literature. However, designing and implementing a robotic system
that integrates all the emerging technologies and meets the connectivity requirements (e.g., latency,
reliability) is an ambitious task.
This thesis studies the integration of modern Information andCommunication Technologies (ICTs)
in robotic systems and proposes some robotic enhancements that tackle the real-time constraints of
robotic services. To evaluate the performance of the proposed enhancements, this thesis departs
from the design and prototype implementation of an edge native robotic system that embodies the concepts of edge computing, fog computing, orchestration, and virtualization. The proposed edge
robotics system serves to represent two exemplary robotic applications. In particular, autonomous
navigation of mobile robots and remote-control of robot manipulator where the end-to-end robotic
system is distributed between the robots and the edge server. The open-source prototype implementation
of the designed edge native robotic system resulted in the creation of two real-world testbeds
that are used in this thesis as a baseline scenario for the evaluation of new innovative solutions in
robotic systems.
After detailing the design and prototype implementation of the end-to-end edge native robotic
system, this thesis proposes several enhancements that can be offered to robotic systems by adapting
the concept of edge computing via the Multi-Access Edge Computing (MEC) framework. First, it
proposes exemplary network context-aware enhancements in which the real-time information about
robot connectivity and location can be used to dynamically adapt the end-to-end system behavior to
the actual status of the communication (e.g., radio channel). Three different exemplary context-aware
enhancements are proposed that aim to optimize the end-to-end edge native robotic system. Later,
the thesis studies the capability of the edge native robotic system to offer potential savings by means of
computation offloading for robot manipulators in different deployment configurations. Further, the
impact of different wireless channels (e.g., 5G, 4G andWi-Fi) to support the data exchange between a
robot manipulator and its remote controller are assessed.
In the following part of the thesis, the focus is set on how orchestration solutions can support
mobile robot systems to make high quality decisions. The application of OKpi as an orchestration algorithm
and DLT-based federation are studied to meet the KPIs that autonomously controlledmobile
robots have in order to provide uninterrupted connectivity over the radio access network. The elaborated
solutions present high compatibility with the designed edge robotics system where the robot
driving range is extended without any interruption of the end-to-end edge robotics service. While the
DLT-based federation extends the robot driving range by deploying access point extension on top of
external domain infrastructure, OKpi selects the most suitable access point and computing resource
in the cloud-to-thing continuum in order to fulfill the latency requirements of autonomously controlled
mobile robots.
To conclude the thesis the focus is set on how robotic systems can improve their performance by
leveraging Artificial Intelligence (AI) and Machine Learning (ML) algorithms to generate smart decisions.
To do so, the edge native robotic system is presented as a true embodiment of a Cyber-Physical
System (CPS) in Industry 4.0, showing the mission of AI in such concept. It presents the key enabling
technologies of the edge robotic system such as edge, fog, and 5G, where the physical processes are
integrated with computing and network domains. The role of AI in each technology domain is identified
by analyzing a set of AI agents at the application and infrastructure level. In the last part of the
thesis, the movement prediction is selected to study the feasibility of applying a forecast-based recovery
mechanism for real-time remote control of robotic manipulators (FoReCo) that uses ML to infer
lost commands caused by interference in the wireless channel. The obtained results are showcasing
the its potential in simulation and real-world experimentation.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Karl Holger.- Secretario: Joerg Widmer.- Vocal: Claudio Cicconett
Life Cycle Engineering 4.0: A Proposal to Conceive Manufacturing Systems for Industry 4.0 Centred on the Human Factor (DfHFinI4.0)
Engineering 4.0 environments are characterised by the digitisation, virtualisation, and connectivity of products, processes, and facilities composed of reconfigurable and adaptive socio-technical cyber-physical manufacturing systems (SCMS), in which Operator 4.0 works in real time in VUCA (volatile, uncertain, complex and ambiguous) contexts and markets. This situation gives rise to the interest in developing a framework for the conception of SCMS that allows the integration of the human factor, management, training, and development of the competencies of Operator 4.0 as fundamental aspects of the aforementioned system. The present paper is focused on answering how to conceive the adaptive manufacturing systems of Industry 4.0 through the operation, growth, and development of human talent in VUCA contexts. With this objective, exploratory research is carried, out whose contribution is specified in a framework called Design for the Human Factor in Industry 4.0 (DfHFinI4.0). From among the conceptual frameworks employed therein, the connectivist paradigm, Ashby's law of requisite variety and Vigotsky's activity theory are taken into consideration, in order to enable the affective-cognitive and timeless integration of the human factor within the SCMS. DfHFinI4.0 can be integrated into the life cycle engineering of the enterprise reference architectures, thereby obtaining manufacturing systems for Industry 4.0 focused on the human factor. The suggested framework is illustrated as a case study for the Purdue Enterprise Reference Architecture (PERA) methodology, which transforms it into PERA 4.0
The concept of collaborative engineering: a systematic literature review
Collaborative engineering is not a new subject but it assumes a new importance in the Industry 4.0 (I4.0). There are other concepts frequently mismatched with collaboration. Thus, the main objective of this paper is to put forward a collaborative engineering concept, along its sub concepts, supported by an extensive systematic literature review. A critical analysis and discussion about the fundamental importance of learning, and the central human role in collaboration, in the I4.0, is presented, based on the main insights brought through the literature review. This study also enables to realize about the importance of collaboration in the current digitalization era, along with the importance of recent approaches and technology for enabling or promoting collaboration. Main current practices of human centered and autonomous machine-machine approaches and applications of collaboration in engineering, namely in manufacturing and management, are presented, along
with main difficulties and further open research opportunities on collaboration.This work was supported by the Fundação para a Ciência e a Tecnologia [UIDB/00319/2020,
UIDB/50014/2020, and EXPL/EME-SIS/1224/2021]
Technology enablers for the implementation of Industry 4.0 to traditional manufacturing sectors: A review
The traditional manufacturing sectors (footwear, textiles and clothing, furniture and toys, among others) are based on small and medium enterprises with limited capacity on investing in modern production technologies. Although these sectors rely heavily on product customization and short manufacturing cycles, they are still not able to take full advantage of the fourth industrial revolution. Industry 4.0 surfaced to address the current challenges of shorter product life-cycles, highly customized products and stiff global competition. The new manufacturing paradigm supports the development of modular factory structures within a computerized Internet of Things environment. With Industry 4.0, rigid planning and production processes can be revolutionized. However, the computerization of manufacturing has a high degree of complexity and its implementation tends to be expensive, which goes against the reality of SMEs that power the traditional sectors. This paper reviews the main scientific-technological advances that have been developed in recent years in traditional sectors with the aim of facilitating the transition to the new industry standard.This research was supported by the Spanish Research Agency (AEI) and the European Regional Development Fund (ERDF) under the project CloudDriver4Industry TIN2017-89266-R
Developing cognitive advisor agents for operators in industry 4.0
Human cyber-physical systems (CPS) are an important component in the development of Industry 4.0. The paradigm shift of doing to thinking has allowed the emergence of cognition as a new perspective for intelligent systems. Currently, different platforms offer several cognitive solutions. Within this space, user assistance systems become increasingly necessary not as a tool but as a function that amplifies the capabilities of the operator in the work environment. There exist different perspectives of cognition. In this study cognition is introduced from the point of view of joint cognitive systems (JCSs); the synergistic combination of different technologies such as artificial intelligence (AI), the Internet of Things (IoT) and multi-agent systems (MAS) allows the operator and the process to provide the necessary conditions to do their work effectively and efficientlyPostprint (published version
Opportunities and Threats Analysis of Industry 4.0 under the Concept: Neo-Ludism
Industrial revolutions have played very important roles on the business world, the public and non-governmental organizations, which are defined as the third sector today, and have caused differences. The first industrial revolution and the fourth revolution at the beginning of the twenty-first century, in terms of leaving a structural and lasting impact, leave more radical traces than the others. In terms of both revolutions, "technological unemployment" and "technological unemployment anxiety", which are the most important macro variables, are common aspects. Every industrial revolution has social, economic, political, technological, legal and environmental effects. In this study, by making a literature review on technological unemployment, which is one of the most important parameters of the social and economic factors created by Industry 4.0, which is known as the fourth industrial revolution and still continues its process in the period of this study, strengths and weaknesses are discussed, opportunities and threats are evaluated and a SWOT analysis was made. By working on new business models, the concept of neo-Ludimz was introduced with a different perspective
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