On the Integration of Adaptive and Interactive Robotic Smart Spaces

© 2015 Mauro Dragone et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)Enabling robots to seamlessly operate as part of smart spaces is an important and extended challenge for robotics R&D and a key enabler for a range of advanced robotic applications, such as AmbientAssisted Living (AAL) and home automation. The integration of these technologies is currently being pursued from two largely distinct view-points: On the one hand, people-centred initiatives focus on improving the user’s acceptance by tackling human-robot interaction (HRI) issues, often adopting a social robotic approach, and by giving to the designer and - in a limited degree – to the final user(s), control on personalization and product customisation features. On the other hand, technologically-driven initiatives are building impersonal but intelligent systems that are able to pro-actively and autonomously adapt their operations to fit changing requirements and evolving users’ needs,but which largely ignore and do not leverage human-robot interaction and may thus lead to poor user experience and user acceptance. In order to inform the development of a new generation of smart robotic spaces, this paper analyses and compares different research strands with a view to proposing possible integrated solutions with both advanced HRI and online adaptation capabilities.Peer reviewe

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

A cognitive robotic ecology approach to self-configuring and evolving AAL systems

Robotic ecologies are systems made out of several robotic devices, including mobile robots, wireless sensors and effectors embedded in everyday environments, where they cooperate to achieve complex tasks. This paper demonstrates how endowing robotic ecologies with information processing algorithms such as perception, learning, planning, and novelty detection can make these systems able to deliver modular, flexible, manageable and dependable Ambient Assisted Living (AAL) solutions. Specifically, we show how the integrated and self-organising cognitive solutions implemented within the EU project RUBICON (Robotic UBIquitous Cognitive Network) can reduce the need of costly pre-programming and maintenance of robotic ecologies. We illustrate how these solutions can be harnessed to (i) deliver a range of assistive services by coordinating the sensing & acting capabilities of heterogeneous devices, (ii) adapt and tune the overall behaviour of the ecology to the preferences and behaviour of its inhabitants, and also (iii) deal with novel events, due to the occurrence of new user's activities and changing user's habits

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

PEIS stol: autonomni robotski stol za kućanstva

There are two main trends in the area of home and service robotics. The classical one aims at the development of a single skilled servant robot, able to perform complex tasks in a passive environment. The second, more recent trend aims at the achievement of complex tasks through the cooperation of a network of simpler robotic devices pervasively embedded in the domestic environment. This paper contributes to the latter trend by describing the PEIS Table, an autonomous robotic table that can be embedded in a smart environment. The robotic table can operate alone, performing simple point-to-point navigation, or it can collaborate with other devices in the environment to perform more complex tasks. Collaboration follows the PEIS Ecology model. The hardware and software design of the PEIS Table are guided by a set of requirements for robotic domestic furniture that differ, to some extent, from the requirements usually considered for service robots.U uslužnoj robotici i robotici za kućanstva postoje dva glavna trenda. Klasičan pristup teži razvoju jednog složenog uslužnog robota koji je sposoban izvršavati složene zadatke u pasivnom okruženju. Dok drugi, nešto noviji pristup, teži rješavanju složenih zadataka kroz suradnju umreženih nešto jednostavnijih robota prožetih kroz cijelo kućanstvo. Ovaj članak svoj doprinos daje drugom pristupu opisujući PEIS stol, autonomni robotski stol koji se može postaviti u inteligentnom okruženju. Robotski stol može djelovati samostalno, navigirajući od točke do točke ili može surađivati s ostalim uređajima u okruženju radi izvršavanja složenijih zadataka. Ta suradnja prati PEIS ekološki model. Dizajn sklopovlja i programske podrške PEIS stola prati zahtjeve za robotsko pokućstvo koji se donekle razlikuju od zahtjeva koji se inače postavljaju za uslužne robote

The Internet of Robotic Things:A review of concept, added value and applications

The Internet of Robotic Things is an emerging vision that brings together pervasive sensors and objects with robotic and autonomous systems. This survey examines how the merger of robotic and Internet of Things technologies will advance the abilities of both the current Internet of Things and the current robotic systems, thus enabling the creation of new, potentially disruptive services. We discuss some of the new technological challenges created by this merger and conclude that a truly holistic view is needed but currently lacking.Funding Agency:imec ACTHINGS High Impact initiative</p

Semantics-based platform for context-aware and personalized robot interaction in the internet of robotic things

Robots are moving from well-controlled lab environments to the real world, where an increasing number of environments has been transformed into smart sensorized IoT spaces. Users will expect these robots to adapt to their preferences and needs, and even more so for social robots that engage in personal interactions. In this paper, we present declarative ontological models and a middleware platform for building services that generate interaction tasks for social robots in smart IoT environments. The platform implements a modular, data-driven workflow that allows developers of interaction services to determine the appropriate time, content and style of human-robot interaction tasks by reasoning on semantically enriched loT sensor data. The platform also abstracts the complexities of scheduling, planning and execution of these tasks, and can automatically adjust parameters to the personal profile and current context. We present motivational scenarios in three environments: a smart home, a smart office and a smart nursing home, detail the interfaces and executional paths in our platform and present a proof-of-concept implementation. (C) 2018 Elsevier Inc. All rights reserved

PEIS stol: autonomni robotski stol za kućanstva

There are two main trends in the area of home and service robotics. The classical one aims at the development of a single skilled servant robot, able to perform complex tasks in a passive environment. The second, more recent trend aims at the achievement of complex tasks through the cooperation of a network of simpler robotic devices pervasively embedded in the domestic environment. This paper contributes to the latter trend by describing the PEIS Table, an autonomous robotic table that can be embedded in a smart environment. The robotic table can operate alone, performing simple point-to-point navigation, or it can collaborate with other devices in the environment to perform more complex tasks. Collaboration follows the PEIS Ecology model. The hardware and software design of the PEIS Table are guided by a set of requirements for robotic domestic furniture that differ, to some extent, from the requirements usually considered for service robots.U uslužnoj robotici i robotici za kućanstva postoje dva glavna trenda. Klasičan pristup teži razvoju jednog složenog uslužnog robota koji je sposoban izvršavati složene zadatke u pasivnom okruženju. Dok drugi, nešto noviji pristup, teži rješavanju složenih zadataka kroz suradnju umreženih nešto jednostavnijih robota prožetih kroz cijelo kućanstvo. Ovaj članak svoj doprinos daje drugom pristupu opisujući PEIS stol, autonomni robotski stol koji se može postaviti u inteligentnom okruženju. Robotski stol može djelovati samostalno, navigirajući od točke do točke ili može surađivati s ostalim uređajima u okruženju radi izvršavanja složenijih zadataka. Ta suradnja prati PEIS ekološki model. Dizajn sklopovlja i programske podrške PEIS stola prati zahtjeve za robotsko pokućstvo koji se donekle razlikuju od zahtjeva koji se inače postavljaju za uslužne robote

Seven HCI Grand Challenges

This article aims to investigate the Grand Challenges which arise in the current and emerging landscape of rapid technological evolution towards more intelligent interactive technologies, coupled with increased and widened societal needs, as well as individual and collective expectations that HCI, as a discipline, is called upon to address. A perspective oriented to humane and social values is adopted, formulating the challenges in terms of the impact of emerging intelligent interactive technologies on human life both at the individual and societal levels. Seven Grand Challenges are identified and presented in this article: Human-Technology Symbiosis; Human-Environment Interactions; Ethics, Privacy and Security; Well-being, Health and Eudaimonia; Accessibility and Universal Access; Learning and Creativity; and Social Organization and Democracy. Although not exhaustive, they summarize the views and research priorities of an international interdisciplinary group of experts, reflecting different scientific perspectives, methodological approaches and application domains. Each identified Grand Challenge is analyzed in terms of: concept and problem definition; main research issues involved and state of the art; and associated emerging requirements

Artificial Intelligence in the Path Planning Optimization of Mobile Agent Navigation

AbstractMany difficult problem solving require computational intelligence. One of the major directions in artificial intelligence consists in the development of efficient computational intelligence algorithms, like: evolutionary algorithms, and neural networks. Systems, that operate in isolation or cooperate with each other, like mobile robots could use computational intelligence algorithms for different problems/tasks solving, however in their behavior could emerge an intelligence called system's intelligence, intelligence of a system. The traveling salesman problem TSP has a large application area. It is a well-known business problem. Maximum benefits TSP, price collecting TSP have a large number of economic applications. TSP is also used in the transport logic Raja, 2012. It also has a wide range of applicability in the mobile robotic agent path planning optimization. In this paper a mobile robotic agent's path planning will be discussed, using unsupervised neural networks for the TSP solving, and from the TSP results the finding of a closely optimal path between two points in the agent's working area. In the paper a modification of the criteria function of the winner neuron selection will also be presented. At the end of the paper measurement results will be presented