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

Synthesis about a collaborative project on “Technology Assessment of Autonomous Systems”

The project started in 2009 with the support of DAAD in Germany and CRUP in Portugal under the “Collaborative German-Portuguese University Actions” programme. One central goal is the further development of a theory of technology assessment applied to robotics and autonomous systems in general that reflects in its methodology the changing conditions of knowledge production in modern societies and the emergence of new robotic technologies and of associated disruptive changes. Relevant topics here are handling broadened future horizons and new clusters of science and technology (medicine, engineering, interfaces, industrial automation, micro-devices, security and safety), as well as new governance structures in policy decision making concerning research and development (R&D).Robotic systems, Autonomous systems, Technology assessment, Germany, Portugal

Roadmap for Real World Internet applications

This paper emphasises the socioeconomic background required to design the Future Internet in order that its services will be accepted by its users and that the economic value latent in the technology is realised. It contains an innovative outlook on sensing aspects of the Future Internet and describes a scenario-based design approach that is feasible to roadmap the dynamic deployment of Real World Internet applications. A multifaceted socioeconomic assessment leads to recommendations for the technology deployment and key features of the Future Internet that will globally integrate technologies like Wireless Sensor and Actuator Networks and Networked Embedded Devices.Real World Internet ; Future Internet ; Scenario-based Design ; Socioeconomics ; Business Models ; Requirements

Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work

Opportunistic software composition: benefits and requirements

International audienceTraditional software development relies on building and assembling pieces of software in order to satisfy explicit requirements. Component-based software engineering simplifies composition and reuse, but software adaptation to the environment remains a challenge. Opportunistic composition is a new approach for building and re-building software in open and dynamic contexts. It is based on the ability to compose software components in a bottom-up manner, merely because they are available at a point and not because the construction of a specific software has been demanded. In this way, software emerges from the environment. This paper analyzes the advantages of such an approach in terms of flexibility and reuse, along with the requirements that an infrastructure supporting opportunistic composition should satisfy: it should be decentralized, autonomic, and dynamically adaptive. The state of the art of automatic software composition shows that few solutions are actually bottom-up, and that none of them fully satisfies the requirements of opportunistic composition