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  • 1.
    Amato, G.
    et al.
    ISTI-CNR, Pisa, Italy.
    Bacciu, D.
    Università di Pisa, Pisa, Italy.
    Broxvall, Mathias
    Örebro Universitet, Örebro, Sweden.
    Chessa, S.
    Università di Pisa, Pisa, Italy.
    Coleman, S.
    University of Ulster, Ulster, UK.
    Di Rocco, Maurizio
    Örebro Universitet, Örebro, Sweden.
    Dragone, M.
    Trinity College Dublin, Dublin, Ireland.
    Gallicchio, C.
    Università di Pisa, Pisa, Italy.
    Gennaro, C.
    ISTI-CNR, Pisa, Italy.
    Lozano, H.
    Tecnalia, Madrid, Spain.
    McGinnity, T. M.
    University of Ulster, Ulster, UK.
    Micheli, A.
    Università di Pisa, Pisa, Italy.
    Ray, A. K.
    University of Ulster, Ulster, UK.
    Renteria, A.
    Tecnalia, Madrid, Spain.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Swords, D.
    University College Dublin, Dublin, Ireland.
    Vairo, C.
    ISTI-CNR, Pisa, Italy.
    Vance, P.
    University of Ulster, Ulster, UK.
    Robotic Ubiquitous Cognitive Ecology for Smart Homes2015In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 80, S57-S81 p.Article in journal (Refereed)
    Abstract [en]

    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 proof of concept 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 feedback 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.

  • 2.
    Bacciu, Davide
    et al.
    Dipartimento di Informatica, Università di Pisa, Italy.
    Gallicchio, Claudio
    Dipartimento di Informatica, Università di Pisa, Italy.
    Micheli, Alessio
    Dipartimento di Informatica, Università di Pisa, Italy.
    Di Rocco, Maurizio
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Learning context-aware mobile robot navigation in home environments2014In: IISA 2014: The 5th International Conference on Information, Intelligence, Systems and Applications, New York: Institute of Electrical and Electronics Engineers (IEEE), 2014, 57-62 p., 6878733Conference paper (Refereed)
    Abstract [en]

    We present an approach to make planning adaptive in order to enable context-aware mobile robot navigation. We integrate a model-based planner with a distributed learning system based on reservoir computing, to yield personalized planning and resource allocations that account for user preferences and environmental changes. We demonstrate our approach in a real robot ecology, and show that the learning system can effectively exploit historical data about navigation performance to modify the models in the planner, without any prior information oncerning the phenomenon being modeled. The plans produced by the adapted CL fail more rarely than the ones generated by a non-adaptive planner. The distributed learning system handles the new learning task autonomously, and is able to automatically identify the sensorial information most relevant for the task, thus reducing the communication and computational overhead of the predictive task.

  • 3.
    Carletti, Cristina
    et al.
    University of Ancona.
    Di Rocco, Maurizio
    Roma Tre University.
    Gasparri, Andrea
    Roma Tre University.
    Ulivi, Giovanni
    Roma Tre University.
    A distributed transferable belief model for collaborative topological map-building in multi-robot systems2010In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2010, 554-560 p.Conference paper (Refereed)
    Abstract [en]

    In this paper the problem of multi-robot collaborative topological map-building is addressed. In this framework, a team of robots is supposed to move in an indoor office-like environment. Each robot, after building a local map by using infrared range-finders, achieves a topological representation of the environment by extracting the most significant features via the Hough transform and comparing them with a set of predefined environmental patterns. The local view of each robot which is significantly constrained by its limited sensing capabilities is then strengthened by a collaborative aggregation schema based on the Transferable Belief Model (TBM). In this way, a better representation of the environment is achieved by each robot with a minimal exchange of information. A preliminary experimental validation carried out by exploiting data collected from a self-made team of robots is proposed.

  • 4.
    Cavallo, Filippo
    et al.
    BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy.
    Limosani, Raffaele
    BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy.
    Manzi, Alessandro
    BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy.
    Bonaccorsi, Manuele
    BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy.
    Esposito, Raffaele
    BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy.
    Di Rocco, Maurizio
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Teti, Giancarlo
    Robotech Srl, Peccioli, Italy.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Dario, Paolo
    BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy.
    Development of a socially believable multi-robot solution from town to home2014In: Cognitive Computation, ISSN 1866-9956, E-ISSN 1866-9964, Vol. 6, no 4, 954-967 p.Article in journal (Refereed)
    Abstract [en]

    Technological advances in the robotic and ICT fields represent an effective solution to address specific societal problems to support ageing and independent life. One of the key factors for these technologies is that they have to be socially acceptable and believable to the end-users. This paper aimed to present some technological aspects that have been faced to develop the Robot-Era system, a multi-robotic system that is able to act in a socially believable way in the environments daily inhabited by humans, such as urban areas, buildings and homes. In particular, this paper focuses on two services-shopping delivery and garbage collection-showing preliminary results on experiments conducted with 35 elderly people. The analysis adopts an end-user-oriented perspective, considering some of the main attributes of acceptability: usability, attitude, anxiety, trust and quality of life.

  • 5.
    Di Rocco, Maurizio
    Roma Tre University.
    Formation control through environment pattern recognition for a multi-robotarchitecture2009Conference paper (Refereed)
  • 6.
    Di Rocco, Maurizio
    et al.
    Örebro University, School of Science and Technology. Roma Tre University, Rome, Italy.
    La Gala, Francesco
    INSEAN, Rome, Italy.
    Ulivi, Giovanni
    Roma Tre University, Rome, Italy.
    Testing Multirobot Algorithms: SAETTA: A Small and Cheap Mobile Unit2013In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 20, no 2, 52-62 p.Article in journal (Other academic)
  • 7.
    Di Rocco, Maurizio
    et al.
    Roma Tre University.
    Pascucci, Federica
    Probabilistic localization in sensor networks using distributed Kalman filter2010In: 7th IFAC Symposium on Intelligent Autonomous Vehicles 2010, Lecce, Italy / [ed] Giovanni Indiveri, Antonio M. Pascoal, 2010Conference paper (Refereed)
    Abstract [en]

    In recent years sensor networks have interested fields such as environment monitoring, surveillance and other distributed applications for data elaboration. This interest has been based on the decentralized approach in treating the information. However it is still a challenge to manipulate such streams of data when the dimension of the net becomes large despite computational capabilities and consumption constraints. In most of applications, location awareness is fundamental to accomplish common tasks. In this paper a probabilistic approach to solve localization problem in wireless sensor networks is presented. The algorithm, based on the Kalman Filter, estimates the sensors' location by an adaptive behavior. The technique proposed allows a reduction of the computation burden respect to the traditional Kalman Filter showing, as explained in simulations and real world experiments, good performances.

  • 8.
    Di Rocco, Maurizio
    et al.
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    When Robots are Late: Configuration Planning for Multiple Robots with Dynamic Goals2013Conference paper (Refereed)
    Abstract [en]

    Unexpected contingencies in robot execution may induce a cascade of effects, especially when multiple robots are involved. In order to effectively adapt to this, robots need the ability to reason along multiple dimensions at execution time. We propose an approach to closed-loop planning capable of generating configuration plans, i.e., action plans for multirobot systems which specify the causal, temporal, resource and information dependencies between individual sensing, computation, and actuation components. The key feature which enables closed loop performance is that configuration plans are represented as constraint networks, which are shared between the planner and the executor and are continuously updated during execution.We report experiments run both in simulation and on real robots, in which a fault in one robot is compensated through different types of planmodifications at run time.

  • 9.
    Di Rocco, Maurizio
    et al.
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Sivakumar, Prasanna Kumar
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Configuration Planning with Multiple Dynamic Goals2013In: Designing intelligent robots: reintegrating AI II. Papers from the AAAI Spring Symposium, AAAI Press, 2013, 12-17 p.Conference paper (Refereed)
    Abstract [en]

    We propose an approach to configuration planning for robotic systems in which plans are represented as constraint networks and planning is defined as search in the space of such networks. The approach supports reasoning about time, resources, and information dependencies between actions. In addition, the system can leverage the flexibility of such networks at execution time to support dynamic goal posting and re-planning.

  • 10.
    Di Rocco, Maurizio
    et al.
    Örebro University, School of Science and Technology.
    Reggente, Matteo
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Gas source localization in indoor environments using multiple inexpensive robots and stigmergy2011In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2011, 5007-5014 p.Conference paper (Refereed)
    Abstract [en]

    Environmental monitoring is a rather new field in robotics. One of the main appealing tasks is gas mapping, i.e., the characterization of the chemical properties (concentration, dispersion, etc.) of the air within an environment. Current approaches rely on a robot using standard localization and mapping techniques to fuse gas measures with spatial features. These approaches require sophisticated sensors and/or high computational resources. We propose a minimalistic approach, in which one or multiple low-cost robots exploit the ability to store information in the environment, or “stigmergy”, to effectively compute an artificial potential leading toward the likely location of the gas source, as indicated by a highest gas concentration or fluctuation. The potential is computed and stored directly on an array of RFID tags buried under the floor. Our approach has been validated in extensive experiments performed on real robots in a domestic environment.

  • 11.
    Di Rocco, Maurizio
    et al.
    Örebro University, School of Science and Technology.
    Sathyakeerthy, Subhash
    Örebro University, School of Science and Technology.
    Grosinger, Jasmin
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Bonaccorsi, Manuele
    The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy.
    Cavallo, Filippo
    The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy.
    Limosani, Raffaele
    The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy.
    Manzi, Alessandro
    The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy.
    Teti, Giancarlo
    Robotech srl, Italy. Association for the Advancement of Artificial Intelligence.
    Dario, Paolo
    The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy.
    A Planner for Ambient Assisted Living:From High-Level Reasoning to Low-Level Robot Execution and Back2014In: Papers from the AAAI Spring Symposium, AAAI Press, 2014Conference paper (Refereed)
    Abstract [en]

    Robot ecologies are a growing paradigm in which oneor several robotic systems are integrated into a smartenvironment. Robotic ecologies hold great promises forelderly assistance. Planning the activities of these systems,however, is not trivial, and requires considerationof issues like temporal and information dependenciesamong different parts of the ecology, exogenous actions,and multiple, dynamic goals. We describe a plannerable to cope with the above challenges. We showin particular how this planner has been incorporatedin closed-loop into a full robotic system that performsdaily tasks in support of elderly people. The full robotecology is deployed in a test apartment inside a real residentialbuilding, and it is currently undergoing an extensiveuser evaluation.

  • 12.
    Di Rocco, Maurizio
    et al.
    Roma Tre University.
    Ulivi, Giavanni
    An efficient implementation of a particle filter for localization using compass data2010In: 7th IFAC Symposium on Intelligent Autonomous Vehicles 2010, Lecce, Italy / [ed] Giovanni Indiveri, Antonio M. Pascoal, 2010Conference paper (Refereed)
    Abstract [en]

    This paper presents a localization algorithm for a small mobile platform. Taking advantage from modern technology, Saetta, a low cost mobile robot, has been built from scratch. Due to the limited processing capabilities, some ad hoc solutions have been used: the lack of processing resources has been compensated by an efficient implementation of the estimator and by the use of compass measures which ease the computational load. The results show how a careful design allows the implementation of sophisticated algorithms also on small platforms.

  • 13.
    Dragone, Mauro
    et al.
    Trinity College Dublin, Dublin, Ireland.
    Amato, Giuseppe
    ISTI-CNR, Pisa, Italy.
    Bacciu, Davide
    Università di Pisa, Pisa, Italy.
    Chessa, Stefano
    Università di Pisa, Pisa, Italy.
    Coleman, Sonya
    University of Ulster, Derry, United Kingdom.
    Di Rocco, Maurizio
    Örebro University, School of Science and Technology.
    Gallicchio, Claudio
    Università di Pisa, Pisa, Italy.
    Gennaro, Claudio
    ISTI-CNR, Pisa, Italy.
    Lozano, Hector
    Tecnalia, Bilbao, Spain.
    Maguire, Liam
    University of Ulster, Derry, United Kingdom.
    McGinnity, Martin
    University of Ulster, Derry, United Kingdom.
    Micheli, Alessio
    Università di Pisa, Pisa, Italy.
    O'Hare, Gregory M. P.
    University College Dublin, Ireland.
    Renteria, Arantxa
    Tecnalia, Bilbao, Spain.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Vairo, Claudio
    ISTI-CNR, Pisa, Italy.
    Vance, Philip
    University of Ulster, Derry, United Kingdom.
    A cognitive robotic ecology approach to self-configuring and evolving AAL systems2015In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 45, 269-280 p.Article in journal (Refereed)
    Abstract [en]

    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.

  • 14.
    Fiorini, Flavio
    et al.
    Roma Tre University.
    Gasparri, Andrea
    Roma Tre University.
    Di Rocco, Maurizio
    Roma Tre University.
    Panzieri, Stefano
    Roma Tre University.
    A networked transferable belief model approach for distributed data aggregation: dynamic version2010In: 49th IEEE Conference on Decision and Control (CDC), 2010, IEEE conference proceedings, 2010, 1237-1242 p.Conference paper (Refereed)
    Abstract [en]

    This paper investigates the data aggregation problem for a multi-agent system. In this framework, agents are assumed to be independent reliable sources which collect data and collaborate to reach a common knowledge. In particular, agents are assumed to dynamically gather data over time, i.e., a dynamic scenario. A protocol for distributed data aggregation which is proved to converge to the basic belief assignment (BBA) given by a centralized aggregation based on the Transferable Belief Model (TBM) is provided.

  • 15.
    Fiorini, Flavio
    et al.
    Roma Tre University.
    Gasparri, Andrea
    Roma Tre University.
    Di Rocco, Maurizio
    Roma Tre University.
    Panzieri, Stefano
    Roma Tre University.
    A networked transferable belief model approach for distributed data aggregation: static version2010In: 49th IEEE Conference on Decision and Control (CDC), 2010, IEEE conference proceedings, 2010, 1229-1236 p.Conference paper (Refereed)
    Abstract [en]

    In this paper the data aggregation problem for a multi-agent system is investigated. In this framework, agents are assumed to be independent reliable sources which collect data and collaborate to reach a common knowledge. In particular, each agent is supposed to provide an observation which does not change over time, i.e., static scenario. A protocol for distributed data aggregation which is proved to converge to the basic belief assignment (BBA) given by a centralized aggregation based on the Transferable Belief Model (TBM) is provided.

  • 16.
    Franchi, Antonio
    et al.
    La Sapienza University, Rome.
    Stegagno, Paolo
    La Sapienza University, Rome.
    Di Rocco, Maurizio
    Roma Tre University.
    Oriolo, Giuseppe
    La Sapienza University, Rome.
    Distributed target localization and encirclement with a multi robot system2010In: 7th IFAC Symposium on Intelligent Autonomous Vehicles 2010, Lecce, Italy / [ed] Giovanni Indiveri, Antonio M. Pascoal, 2010Conference paper (Refereed)
    Abstract [en]

    This paper presents a control scheme for localizing and encircling a target using a multi-robot system. The task is achieved in a distributed way, in that each robot only uses local information gathered by on-board relative-position sensors assumed to be noisy, anisotropic, and unable to detect the identity of the measured object. Communication between the robots is provided by limited-range transceivers. Experimental results with stationary and moving targets support the theoretical analysis.

  • 17.
    Gasparri, Andrea
    et al.
    Roma Tre University, Rome, Italy.
    Fiorini, Flavio
    Logofive Srl, Turin, Italy.
    Di Rocco, Maurizio
    Roma Tre University, Rome, Italy.
    Panzieri, Stefano
    Roma Tre University, Rome, Italy.
    A networked transferable belief model approach for distributed data aggregation2012In: IEEE transactions on systems, man and cybernetics. Part B. Cybernetics, ISSN 1083-4419, E-ISSN 1941-0492, Vol. 42, no 2, 391-405 p.Article in journal (Refereed)
    Abstract [en]

    This paper focuses on the extension of the transferable belief model (TBM) to a multiagent-distributed context where no central aggregation unit is available and the information can be exchanged only locally among agents. In this framework, agents are assumed to be independent reliable sources which collect data and collaborate to reach a common knowledge about an event of interest. Two different scenarios are considered: In the first one, agents are supposed to provide observations which do not change over time (static scenario), while in the second one agents are assumed to dynamically gather data over time (dynamic scenario). A protocol for distributed data aggregation, which is proved to converge to the basic belief assignment given by an equivalent centralized aggregation schema based on the TBM, is provided. Since multiagent systems represent an ideal abstraction of actual networks of mobile robots or sensor nodes, which are envisioned to perform the most various kind of tasks, we believe that the proposed protocol paves the way to the application of the TBM in important engineering fields such as multirobot systems or sensor networks, where the distributed collaboration among players is a critical and yet crucial aspect.

  • 18.
    Khaliq, Ali Abdul
    et al.
    Örebro University, School of Science and Technology.
    Di Rocco, Maurizio
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Stigmergic algorithms for multiple minimalistic robots on an RFID floor2014In: Swarm Intelligence, ISSN 1935-3820, Vol. 8, no 3, 199-225 p.Article in journal (Refereed)
    Abstract [en]

    Stigmergy is a powerful principle in nature, which has been shown to have interesting applications to robotic systems. By leveraging the ability to store information in the environment, robots with minimal sensing, memory, and computational capabilities can solve complex problems like global path planning. In this paper, we discuss the use of stigmergy in minimalist multi-robot systems, in which robots do not need to use any internal model, long-range sensing, or position awareness. We illustrate our discussion with three case studies: building a globally optimal navigation map, building a gradient map of a sensed feature, and updating the above maps dynamically. All case studies have been implemented in a real environment with multiple ePuck robots, using a floor with 1,500 embedded radio frequency identification tags as the stigmergic medium. Results collected from tens of hours of real experiments and thousands of simulated runs demonstrate the effectiveness of our approach.

  • 19.
    Khaliq, Ali Abdul
    et al.
    Örebro University, School of Science and Technology.
    Di Rocco, Maurizio
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Stigmergic Algorithms for Simple Robotic Devices (Extended abstract)2013In: Workshop on Unconventional Approaches to Robotics, Automation and Control Inspired by Nature (ICRA 2013), 2013Conference paper (Refereed)
    Abstract [en]

    This position paper is meant to discuss the use of stigmergy in minimalist robotic systems, and to argue for a methodological approach based on the combination of formal analysis and empirical evaluation. In the full paper we will illustrate this approach in three case studies: building a globally optimal navigation map, building a gas concentration gradient map, and updating the above maps dynamically. All case studies have been implemented in a real environment with inexpensive robots, using an RFID floor as the stigmergic medium.

  • 20.
    Sathyakeerthy, Subhash
    et al.
    Örebro University, School of Science and Technology.
    Di Rocco, Maurizio
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Scaling up ubiquitous robotic systems from home to town (and beyond)2013In: UbiComp '13 Adjunct Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication, ACM Press, 2013, 107-110 p.Conference paper (Refereed)
1 - 20 of 20
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