oru.sePublikationer
Change search
Refine search result
1234 1 - 50 of 180
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the 'Create feeds' function.
  • 1.
    Abdul Khaliq, Ali
    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.
    Point-to-point safe navigation of a mobile robot using stigmergy and RFID technology2016In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2016, 1497-1504 p., 7759243Conference paper (Refereed)
    Abstract [en]

    Reliable autonomous navigation is still a challenging problem for robots with simple and inexpensive hardware. A key difficulty is the need to maintain an internal map of the environment and an accurate estimate of the robot’s position in this map. Recently, a stigmergic approach has been proposed in which a navigation map is stored into the environment, on a grid of RFID tags, and robots use it to optimally reach predefined goal points without the need for internal maps. While effective,this approach is limited to a predefined set of goal points. In this paper, we extend this approach to enable robots to travel to any point on the RFID floor, even if it was not previously identified as a goal location, as well as to keep a safe distance from any given critical location. Our approach produces safe, repeatable and quasi-optimal trajectories without the use of internal maps, self localization, or path planning. We report experiments run in a real apartment equipped with an RFID floor, in which a service robot either reaches or avoids a user who wears slippers equipped with an RFID tag reader.

  • 2.
    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.

  • 3. Bacciu, D.
    et al.
    Broxvall, Mathias
    Örebro University, School of Science and Technology.
    Coleman, S.
    Dragone, M.
    Gallicchio, C.
    Gennaro, C.
    Guzmán, R.
    Lopez, R.
    Lozano-Peiteado, H.
    Ray, A.
    Renteria, A.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Vairo, C.
    Self-sustaining learning for robotic ecologies2012Conference paper (Refereed)
    Abstract [en]

    The most common use of wireless sensor networks (WSNs) is to collect environmental data from a specificarea, and to channel it to a central processing node for on-line or off-line analysis. The WSN technology,however, can be used for much more ambitious goals. We claim that merging the concepts and technology ofWSN with the concepts and technology of distributed robotics and multi-agent systems can open new waysto design systems able to provide intelligent services in our homes and working places. We also claim thatendowing these systems with learning capabilities can greatly increase their viability and acceptability, bysimplifying design, customization and adaptation to changing user needs. To support these claims, we illus-trate our architecture for an adaptive robotic ecology, named RUBICON, consisting of a network of sensors,effectors and mobile robots.

  • 4.
    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.

  • 5.
    Benferhat, Salem
    et al.
    Univ. Paul Sabatier, Toulouse, France.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Smets, Philippe
    Univ. Libre de Bruxelles, Brussels, Belgium.
    Belief functions and default reasoning2000In: Artificial Intelligence, ISSN 0004-3702, E-ISSN 0374-2539, Vol. 122, no 1-2, 1-69 p.Article in journal (Refereed)
    Abstract [en]

    We present a new approach to deal with default information based on the theory of belief functions. Our semantic structures, inspired by Adams' epsilon semantics, are epsilon-belief assignments, where mass values are either close to 0 or close to 1. In the first part of this paper, we show that these structures can be used to give a uniform semantics to several popular non-monotonic systems, including Kraus, Lehmann and Magidor's system P, Pearl's system Z, Brewka's preferred sub-theories, Geffner's conditional entailment, Pinkas' penalty logic, possibilistic logic, and the lexicographic approach. In the second part, we use epsilon-belief assignments to build a new system, called LCD, and we show that this system correctly addresses the well-known problems of specificity, irrelevance, blocking of inheritance, ambiguity, and redundancy

  • 6.
    Besold, Tarek R.
    et al.
    Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany.
    Kuehnberger, Kai-Uwe
    Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany.
    Garcez, Artur d'Avila
    City University London, London, UK.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Fischer, Martin H.
    University of Potsdam, Potsdam, Germany.
    Bundy, Alan
    University of Edinburgh, Edinburgh, Scotland.
    Anchoring Knowledge in Interaction: Towards a Harmonic Subsymbolic/Symbolic Framework and Architecture of Computational Cognition2015In: Artificial General Intelligence (AGI 2015), Springer, 2015, 35-45 p.Conference paper (Refereed)
    Abstract [en]

    We outline a proposal for a research program leading to a new paradigm, architectural framework, and prototypical implementation, for the cognitively inspired anchoring of an agent's learning, knowledge formation, and higher reasoning abilities in real-world interactions: Learning through interaction in real-time in a real environment triggers the incremental accumulation and repair of knowledge that leads to the formation of theories at a higher level of abstraction. The transformations at this higher level filter down and inform the learning process as part of a permanent cycle of learning through experience, higher-order deliberation, theory formation and revision.

    The envisioned framework will provide a precise computational theory, algorithmic descriptions, and an implementation in cyber-physical systems, addressing the lifting of action patterns from the subsymbolic to the symbolic knowledge level, effective methods for theory formation, adaptation, and evolution, the anchoring of knowledge-level objects, realworld interactions and manipulations, and the realization and evaluation of such a system in different scenarios. The expected results can provide new foundations for future agent architectures, multi-agent systems, robotics, and cognitive systems, and can facilitate a deeper understanding of the development and interaction in human-technological settings.

  • 7. Bidot, Julien
    et al.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Lagriffoul, Fabien
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Geometric backtracking for combined task and motion planning in robotic systems2017In: Artificial Intelligence, ISSN 0004-3702, E-ISSN 1872-7921, Vol. 247, 229-265 p.Article in journal (Refereed)
    Abstract [en]

    Planners for real robotic systems should not only reason about abstract actions, but also about aspects related to physical execution such as kinematics and geometry. We present an approach to hybrid task and motion planning, in which state-based forward-chaining task planning is tightly coupled with motion planning and other forms of geometric reasoning. Our approach is centered around the problem of geometric backtracking that arises in hybrid task and motion planning: in order to satisfy the geometric preconditions of the current action, a planner may need to reconsider geometric choices, such as grasps and poses, that were made for previous actions. Geometric backtracking is a necessary condition for completeness, but it may lead to a dramatic computational explosion due to the large size of the space of geometric states. We explore two avenues to deal with this issue: the use of heuristics based on different geometric conditions to guide the search, and the use of geometric constraints to prune the search space. We empirically evaluate these different approaches, and demonstrate that they improve the performance of hybrid task and motion planning. We demonstrate our hybrid planning approach in two domains: a real, humanoid robotic platform, the DLR Justin robot, performing object manipulation tasks; and a simulated autonomous forklift operating in a warehouse.

  • 8.
    Bidot, Julien
    et al.
    Örebro University, School of Science and Technology.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Lagriffoul, Fabien
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Geometric backtracking for combined task and path planning in robotic systemsManuscript (preprint) (Other academic)
    Abstract [en]

    Planners for real, possibly complex, robotic systems should not only reason about abstract actions, but also about aspects related to physical execution such as kinematics and geometry. We present an approach in which state-based forward-chaining task planning is tightly coupled with sampling-based motion planning and other forms of geometric reasoning. We focus on the problem of geometric backtracking which arises when a planner needs to reconsider geometric choices, like grasps and poses, that were made for previous actions, in order to satisfy geometric preconditions of the current action. Geometric backtracking is a necessary condition for completeness, but it may lead to a dramatic computational explosion due to the systematic exploration of the space of geometric states. In order to deal with that, we introduce heuristics based on the collisions between the robot and movable objects detected during geometric backtracking and on kinematic relations between actions. We also present a complementary approach based on propagating explicit constraints which are automatically generated from the symbolic actions to be evaluated and from the kinematic model of the robot. We empirically evaluate these dierent approaches. We demonstrate our planner on a real advanced robot, the DLR Justin robot, and on a simulated autonomous forklift. 

  • 9. Bloch, I.
    et al.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    On the representation of fuzzy spatial relations in robot maps2003In: Intelligent systems for information processing: from representation to applications / [ed] Bernadette Bouchon-Meunier, Laurent Foulloy, Ronald R. Yager, Amsterdam: Elsevier, 2003, 47-57 p.Conference paper (Refereed)
    Abstract [en]

    Spatial directional relations, like "north of," play an important role in the modeling of the environment by an autonomous robot. We propose an approach to represent spatial relations grounded in fuzzy set theory and fuzzy mathematical morphology. We show how this approach can be applied to robot maps, and suggest that these relations can be used for self-localization and for reasoning about the environment. We illustrate our approach on real data collected by a mobile robot in an office environment

  • 10. Bloch, Isabelle
    et al.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Why robots should use fuzzy mathematical morphology2002Conference paper (Refereed)
    Abstract [en]

    Mobile robots must represent and reason about spatial knowledge acquired from sensor data which are inherently approximate and uncertain. While techniques based on fuzzy sets are increasingly used in this domain, the use of these techniques often rests on intuitive grounds. In this paper, we show that fuzzy mathematical morphology, a theory often used in image processing but mostly ignored in the robotic tradition, can provide a well grounded approach to the treatment of imprecise spatial knowledge in robotics

  • 11.
    Boldrin, Luca
    et al.
    University of Pauda.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    A modal logic for fusing partial belief of multiple reasoners1999In: Journal of logic and computation (Print), ISSN 0955-792X, E-ISSN 1465-363X, Vol. 9, no 1, 81-103 p.Article in journal (Refereed)
    Abstract [en]

    We present PLn, a multi-agent epistemic logic in which each agent can perform uncertain (possibilistic) reasoning. The original feature of this logic is the presence of a distributed belief operator, with the purpose of merging the belief of different agents. Unlike the corresponding operator in the categorical (non-uncertain) case, our distributed belief operator accumulates support for the same fact coming from different agents. This means that opinions shared by different agents can be combined into a stronger distributed belief. This feature is useful in problems like pooling expert opinions and combining information from multiple unreliable sources. We provide a possible worlds semantics and an axiomatic calculus for our logic, and prove soundness, completeness and decidability results. We hint at some possible applications of PLn in the conclusions

  • 12.
    Bonaccorsi, Manuele
    et al.
    The BioRobotics Institute, Scuola Superiore Sant’Anna, Viale R. Piaggio, Pontedera, Italy.
    Fiorini, L
    The BioRobotics Institute, Scuola Superiore Sant’Anna, Viale R. Piaggio, Pontedera, Italy.
    Sathyakeerthy, Subhash
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Cavallo, Filippo
    The BioRobotics Institute, Scuola Superiore Sant’Anna, Viale R. Piaggio, Pontedera, Italy.
    Dario, Paolo
    The BioRobotics Institute, Scuola Superiore Sant’Anna, Viale R. Piaggio, Pontedera, Italy.
    Design of cloud robotic services for senior citizens to improve independent living in multiple environments2015In: Intelligenza Artificiale, ISSN 1724-8035, Vol. 9, no 1, 63-72 p.Article in journal (Refereed)
    Abstract [en]

    The paper proposed a cloud robotic solution for the healthcare management of senior citizens, to demonstrate the opportunity to remotely provide continuous assistive robotic services to a number of seniors regardless to their position in the monitored environment. In particular, a medication reminding, a remote home monitoring and an user indoor localization service were outsourced in the cloud and provided to the robots, users and caregivers on request. The proposed system was composed of a number of robotic agents distributed over two smart environments: a flat at the Domocasa Lab (Peccioli, IT) and a condominium at the Angen site of the Orebro science park (Orebro, SE). The cloud acquired data from remote smart environments and enabled the local robots to provide advanced assistive services to a number of users. The proposed smart environments were able to collect raw data for the environmental monitoring and the localization of the users by means of wireless sensors, and provide such data to the cloud. On the cloud, specific algorithms improved the local robots, by providing event scheduling to accomplish assistive services and situation awareness on the users position and environments’ status. The indoor user localization service, was provided by means of commercial and ad-hoc sensors distributed over the environments and a sensor fusion algorithm on the cloud. The entire cloud solution was evaluated in terms of Quality of Service (QoS) to estimate the effectiveness of the architecture.

  • 13.
    Bonaccorsi, Manuele
    et al.
    The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy.
    Fiorini, Laura
    The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy.
    Cavallo, Filippo
    The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Dario, Paolo
    The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy.
    A cloud robotics solution to improve social assistive robots for active and healthy aging2016In: International Journal of Social Robotics, ISSN 1875-4791, E-ISSN 1875-4805, Vol. 8, no 3, 393-408 p.Article in journal (Refereed)
    Abstract [en]

    Technological innovation in robotics and ICT represents an effective solution to tackle the challenge of providing social sustainable care services for the ageing population. The recent introduction of cloud technologies is opening new opportunities for the provisioning of advanced robotic services based on the cooperation of a number of connected robots, smart environments and devices improved by the huge cloud computational and storage capability. In this context, this paper aims to investigate and assess the potentialities of a cloud robotic system for the provisioning of assistive services for the promotion of active and healthy ageing. The system comprised two different smart environments, located in Italy and Sweden, where a service robot is connected to a cloud platform for the provisioning of localization based services to the users. The cloud robotic services were tested in the two realistic environments to assess the general feasibility of the solution and demonstrate the ability to provide assistive location based services in a multiple environment framework. The results confirmed the validity of the solution but also suggested a deeper investigation on the dependability of the communication technologies adopted in such kind of systems.

  • 14.
    Bordignon, Mirko
    et al.
    Dept. of Information Engineering, University of Padova, Italy.
    Pagello, Enrico
    Dept. of Information Engineering, University of Padova, Italy.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    An inexpensive, off-the-shelf platform for networked embedded robotics2007In: Proceedings of the 1st international conference on Robot communication and coordination, RoboComm '07, Piscataway: IEEE press , 2007, Art no: 45- p.Conference paper (Refereed)
    Abstract [en]

    Recent years have witnessed the proliferation of a new class of devices, commonly referred to as Networked Embedded Devices. Their increasingly low cost and small size make them suited for large scale sensing applications. Likewise, they could be appealing as a means to embed intelligent actuation capabilities into the environment, turning simple artifacts into networked robotic appliances. The currently existing devices, however, are not suited for this development. In this paper, we present the PEIS-Mote: an open, general, small-size and inexpensive sensor-actuator node especially suited for networked robotics, and built from commonly available off-the-shelf components. This platform can run a popular operating system for sensor networks, TinyOS, which makes it interoperable with most commercially available sensor nodes.

  • 15. Bordignon, Mirko
    et al.
    Rashid, Jayedur
    Örebro University, Department of Technology.
    Broxvall, Mathias
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Seamless integration of robots and tiny embedded devices in a PEIS-ecology2007In: IEEE/RSJ international  conference on intelligent robots and systems, IROS 2007, New York: IEEE , 2007, 3101-3106 p.Conference paper (Refereed)
    Abstract [en]

    The fields of autonomous robotics and ambient intelligence are converging toward the vision of smart robotic environments, in which tasks are performed via the cooperation of many networked robotic devices. To enable this vision, we need a common communication and cooperation model that can be shared between robotic devices at different scales, ranging from standard mobile robots to tiny embedded devices. Unfortunately, today's robot middlewares are too heavy to run on tiny devices, and middlewares for embedded devices are too simple to support the cooperation models needed by an autonomous smart environment. In this paper, we propose a middleware model which allows the seamless integration of standard robots and simple off-the-shelf embedded devices. Our middleware is suitable for building truly ubiquitous robotics applications, in which devices of very different scales and capabilities can cooperate in a uniform way. We discuss the principles and implementation of our middleware, and show an experiment in which a mobile robot, a commercial mote, and a custom-built mote cooperate in a home service scenario.

  • 16.
    Borissov, Alexei
    et al.
    Örebro University, School of Science and Technology.
    Janecek, Jakob
    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.
    Towards a network robot system for object identification and localization in RoboCup@Home2008Conference paper (Refereed)
    Abstract [en]

    This paper describes a realization of a network robot system for autonomous object localization and identification. Developing a ``Lost \& Found'' capability, the use of which can be envisaged in a wide range of applicative domains including domestic assistive scenarios, is a challenging task for current AI and robotic technology. Indeed, this task is currently one of the core challenges within the RoboCup@Home competition. A number of approaches for implementing a robust and general Lost \& Found functionality are feasible. In this paper we present a solution which integrates state-of-the-art intelligent software, robotic and sensory components in a distributed network of cooperating modules. This article describes the design and implementation of the system, provides a preliminary experimental evaluation and discusses the applicability of our approach to the RoboCup@Home challenge.

  • 17.
    Bouguerra, Abdelbaki
    et al.
    Örebro University, Department of Technology.
    Karlsson, Lars
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Active execution monitoring using planning and semantic knowledge2007In: Proc. of the ICAPS Workshop on Planning and Plan Execution for Real-World Systems, Providence, RI, 2007, 2007, 9-15 p.Conference paper (Other academic)
    Abstract [en]

    To cope with the dynamics and uncertainty inherent in real world environments, autonomous mobile robots need to perform execution monitoring for verifying that their plans are executed as expected. Domain semantic knowledge has lately been proposed as a source of information to derive and monitor implicit expectations of executing actions. For instance, when the robot moves into an office, it would expect to see a desk and a chair. Such expectations are checked using the immediately available perceptual information. We propose to extend the semantic knowledge-based execution monitoring to handle situations where some of the required information is missing. To this end, we use AI sensor-based planning to actively search for such information. We show how verifying execution expectations can be formulated and solved as a planning problem involving sensing actions. Our approach is illustrated by showing test scenarios run in an indoor environment using a mobile robot.

  • 18.
    Bouguerra, Abdelbaki
    et al.
    Örebro University, School of Science and Technology.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Handling uncertainty in semantic-knowledge based execution monitoring2007In: Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International Conference on Oct. 29 2007-Nov. 2 2007, NEW YORK: IEEE , 2007, 443-449 p.Chapter in book (Other academic)
    Abstract [en]

    Executing plans by mobile robots, in real world environments, faces the challenging issues of uncertainty and environment dynamics. Thus, execution monitoring is needed to verify that plan actions are executed as expected. Semantic domain-knowledge has lately been proposed as a source of information to derive and monitor implicit expectations of executing actions. For instance, when a robot moves into a room asserted to be an office, it would expect to see a desk and a chair. We propose to extend the semantic knowledge-based execution monitoring to take uncertainty in actions and sensing into account when verifying the expectations derived from semantic knowledge. We consider symbolic probabilistic action models, and show how semantic knowledge is used together with a probabilistic sensing model in the monitoring process of such actions. Our approach is illustrated by showing test scenarios ran in an indoor environment using a mobile robot

  • 19.
    Bouguerra, Abdelbaki
    et al.
    Örebro University, Department of Technology.
    Karlsson, Lars
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Handling uncertainty in semantic-knowledge based execution monitoring2007In: IEEE/RSJ international conference on intelligent robots and systems, IROS 2007 San Diego, CA, 2007, New York: IEEE , 2007, 437-443 p.Conference paper (Refereed)
    Abstract [en]

    Executing plans by mobile robots, in real world environments, faces the challenging issues of uncertainty and environment dynamics. Thus, execution monitoring is needed to verify that plan actions are executed as expected. Semantic domain-knowledge has lately been proposed as a source of information to derive and monitor implicit expectations of executing actions. For instance, when a robot moves into a room asserted to be an office, it would expect to see a desk and a chair. We propose to extend the semantic knowledge-based execution monitoring to take uncertainty in actions and sensing into account when verifying the expectations derived from semantic knowledge. We consider symbolic probabilistic action models, and show how semantic knowledge is used together with a probabilistic sensing model in the monitoring process of such actions. Our approach is illustrated by showing test scenarios run in an indoor environment using a mobile robot.

  • 20.
    Bouguerra, Abdelbaki
    et al.
    Örebro University, School of Science and Technology.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Monitoring the execution of robot plans using semantic knowledge2008In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 0921-8830, Vol. 56, no 11, 942-954 p.Article in journal (Refereed)
    Abstract [en]

    Even the best laid plans can fail, and robot plans executed in real world domains tend to do so often. The ability of a robot to reliably monitor the execution of plans and detect failures is essential to its performance and its autonomy. In this paper, we propose a technique to increase the reliability of monitoring symbolic robot plans. We use semantic domain knowledge to derive implicit expectations of the execution of actions in the plan, and then match these expectations against observations. We present two realizations of this approach: a crisp one, which assumes deterministic actions and reliable sensing, and uses a standard knowledge representation system (LOOM); and a probabilistic one, which takes into account uncertainty in action effects, in sensing, and in world states. We perform an extensive validation of these realizations through experiments performed both in simulation and on real robots.

  • 21.
    Bouguerra, Abdelbaki
    et al.
    Örebro University, Department of Technology.
    Karlsson, Lars
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Semantic knowledge-based execution monitoring for mobile robots2007In: 2007 IEEE international conference on robotics and automation (ICRA), 2007, 3693-3698 p.Conference paper (Refereed)
    Abstract [en]

    We describe a novel intelligent execution monitoring approach for mobile robots acting in indoor environments such as offices and houses. Traditionally, monitoring execution in mobile robotics amounted to looking for discrepancies between the model-based predicted state of executing an action and the real world state as computed from sensing data. We propose to employ semantic knowledge as a source of information to monitor execution. The key idea is to compute implicit expectations, from semantic domain information, that can be observed at run time by the robot to make sure actions are executed correctly. We present the semantic knowledge representation formalism, and how semantic knowledge is used in monitoring. We also describe experiments run in an indoor environment using a real mobile robot

  • 22.
    Bouguerra, Abdelbaki
    et al.
    Örebro University, Department of Technology.
    Karlsson, Lars
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Situation assessment for sensor-based recovery planning2006In: 17th European Conference on Artificial Intelligence (ECAI) / [ed] Gerhard Brewka, Silvia Coradeschi, Anna Perini, Paolo Traverso, IOS Press, 2006, 673-677 p.Conference paper (Refereed)
    Abstract [en]

    We present an approach for recovery from perceptual failures, or more precisely anchoring failures. Anchoring is the problem of connecting symbols representing objects to sensor data corresponding to the same objects. The approach is based on using planning, but our focus is not on the plan generation per se. We focus on the very important aspect of situation assessment and how it is carried out for recovering from anchoring failures. The proposed approach uses background knowledge to create hypotheses about world states and handles uncertainty in terms of probabilistic belief states. This work is relevant both from the perspective of developing the anchoring framework, and as a study in plan-based recovery from epistemic failures in mobile robots. Experiments on a mobile robot are shown to validate the applicability of the proposed approach.

  • 23.
    Broxvall, Mathias
    et al.
    Örebro University, Department of Technology.
    Coradeschi, Silvia
    Örebro University, Department of Technology.
    Karlsson, Lars
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Have another look on failures and recovery planning in perceptual anchoring2004Conference paper (Refereed)
    Abstract [en]

    An important requirement for autonomous systems is the ability to detect and recover from exceptional situations such as failures in observations. In this paper we demonstrate how techniques for planning with sensing under uncertainty can play a major role in solving the problem of recovering from such situations. In this first step we concentrate on failures in perceptual anchoring, that is how to connect a symbol representing an object to the percepts of that object. We provide a classification of failures and present planning-based methods for recovering from them. We illustrate our approach by showing tests run on a mobile robot equipped with a color camera.

  • 24.
    Broxvall, Mathias
    et al.
    Örebro University, Department of Technology.
    Coradeschi, Silvia
    Örebro University, Department of Technology.
    Karlsson, Lars
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Recovery planning for ambiguous cases in perceptual anchoring2005In: Proceedings of the 20th national conference on Artificial intelligence, AAAI-05: volume 3, 2005, 1254-1260 p.Conference paper (Refereed)
    Abstract [en]

    An autonomous robot using symbolic reasoning, sensing and acting in a real environment needs the ability to create and maintain the connection between symbols representing objects in the world and the corresponding perceptual representations given by its sensors. This connection has been named perceptual anchoring. In complex environments, anchoring is not always easy to establish: the situation may often be ambiguous as to which percept actually corresponds to a given symbol. In this paper, we extend perceptual anchoring to deal robustly with ambiguous situations by providing general methods for detecting them and recovering from them. We consider different kinds of ambiguous situations and present planning-based methods to recover from them. We illustrate our approach by showing experiments involving a mobile robot equipped with a color camera and an electronic nose.

  • 25.
    Broxvall, Mathias
    et al.
    Örebro University, Department of Technology.
    Coradeschi, Silvia
    Örebro University, Department of Technology.
    Loutfi, Amy
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    An ecological approach to odour recognition in intelligent environments2006In: 2006 IEEE International Conference on Robotics and automation, ICRA 2006, 2006, 2066-2071 p.Conference paper (Refereed)
    Abstract [en]

    We present a new approach for odour detection and recognition based on a so-called PEIS-Ecology: a network of gas sensors and a mobile robot are integrated in an intelligent environment. The environment can provide information regarding the location of potential odour sources, which is then relayed to a mobile robot equipped with an electronic nose. The robot can then perform a more thorough analysis of the odour character. This is a novel approach which alleviates some the challenges in mobile olfaction techniques by single and embedded mobile robots. The environment also provides contextual information which can be used to constrain the learning of odours, which is shown to improve classification performance.

  • 26.
    Broxvall, Mathias
    et al.
    Örebro University, Department of Technology.
    Gritti, Marco
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Seo, Beom-Su
    Cho, Young-Jo
    PEIS ecology: integrating robots into smart environments2006In: 2006 IEEE International Conference on Robotics and automation, ICRA 2006, 2006, 212-218 p.Conference paper (Refereed)
    Abstract [en]

    We introduce the concept of Ecology of Physically Embedded Intelligent Systems, or PEIS-Ecology. This is a network of heterogeneous robotic devices (PEIS) pervasively embedded in the environment. A PEIS can be as simple as a toaster and as complex as a humanoid robot. PEIS can exchange information at different levels of abstraction, and share both physical and virtual functionalities to perform complex tasks. By putting together insights from the fields of autonomous robotics and of ambient intelligence, the PEIS-Ecology approach explores a new road to building assistive, personal, and service robots. In this paper, we discuss this concept, describe a first realization of it, and show an implemented use-case scenario.

  • 27.
    Broxvall, Mathias
    et al.
    Örebro University, Department of Technology.
    Karlsson, Lars
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Steps toward detecting and recovering from perceptual failures2004In: Proceedings of the 8th international conference on intelligent autonomous systems, 2004, 793-800 p.Conference paper (Refereed)
    Abstract [en]

    An important requirement for autonomous systems is the ability to detect and recover from exceptional situations such as failures in observations. In this paper we investigate how traditional AI planning techniques can be used to reason about observations and to recover from these situations. In this first step we concentrate on failures in perceptual anchoring. We illustrate our approach by showing experiments run on a mobile robot equipped with a color camera.

  • 28.
    Broxvall, Mathias
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Interacting with a robot ecology using task templates2007In: 2007 RO-MAN: 16TH IEEE INTERNATIONAL SYMPOSIUM ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION, VOLS 1-3, NEW YORK: IEEE , 2007, 486-491 p.Chapter in book (Other academic)
    Abstract [en]

    Robot ecologies provide a new paradigm for assistive, service, industrial, and entertainment robotics which is quickly gaining popularity. These ecologies contain a large number of robotic components pervasively embedded in the environment and interacting with each other. Human users of such systems need to be able to interface with both the system as a whole and, if desired, which each individual component. The humans should be able to transmit, in a natural way, commands that range from basic ones, such as ''turn on the lights in the bedroom'', to abstract ones, such as ''bring me a cup of coffee''. Human users may also need to interact with task execution especially at decision points. In this paper, we introduce an approach to interface a human user to a specific type of robot ecology, called an ecology of Physically Embedded Intelligent Systems, or PEIS-Ecology. The ecology includes simple sensors and actuators and more complicated devices such as mobile robots. The proposed interface satisfies two requirements: 1) to easily and automatically generate component interfaces, and 2) to provide a simple mechanism by which to request and monitor the execution of tasks in the ecology.

  • 29.
    Broxvall, Mathias
    et al.
    Örebro University, Department of Technology.
    Loutfi, Amy
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Interacting with a robot ecology using task templates2007In: 16th IEEE international symposium on robot and human interactive communication, RO-MAN 2007, New York: IEEE , 2007, 487-492 p.Conference paper (Refereed)
    Abstract [en]

    Robot ecologies provide a new paradigm for assistive, service, industrial, and entertainment robotics which is quickly gaining popularity. These ecologies contain a large number of robotic components pervasively embedded in the environment and interacting with each other. Human users of such systems need to be able to interface with both the system as a w hole and, if desired, which each individual component. The humans should be able to transmit, in a natural way, commands that range from basic ones, such as "turn on the lights in the bedroom", to abstract ones, such as "bring me a cup of coffee". Human users may also need to interact with task execution, especially at decision points. In this paper, we introduce an approach to interface a human user to a specific type of robot ecology, called an ecology of Physically Embedded Intelligent Systems, or PEIS-Ecology. The ecology includes simple sensors and actuators and more complicated devices such as mobile robots. The proposed interface satisfies two requirements: 1) to easily and automatically generate component interfaces, and 2) to provide a simple mechanism by which to request and monitor the execution of tasks in the ecology.

  • 30.
    Bruno, Barbara
    et al.
    Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, Genova, Italy.
    Grosinger, Jasmin
    Örebro University, School of Science and Technology.
    Mastrogiovanni, Fulvio
    Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, Genova, Italy.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Sathyakeerthy, Subhash
    Örebro University, School of Science and Technology.
    Sgorbissa, Antonio
    Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, Genova, Italy.
    Multi-modal sensing for human activity recognition2015In: Proceedings of the 24th IEEE International Symposium on Robot and Human Interactive Communication, Kobe, Japan, Aug 31 - Sept 4, 2015, New York: IEEE conference proceedings , 2015, 594-600 p.Conference paper (Refereed)
    Abstract [en]

    Robots for the elderly are a particular category of home assistive robots, aiming at assisting the elderly inthe execution of daily life tasks to extend their independent life. To this aim, such robots should be able to determine the level of independence of the user and track its evolution over time, to adapt the assistance to the person capabilities and needs. Human Activity Recognition systems employ various sensing strategies, relying on environmental or wearable sensors,to recognize various daily life activities which provide insights on the health status of a person. The main contribution of the article is the design of an heterogeneous information management framework, allowing for the description of a wide variety of human activities in terms of multi-modal environmental and wearable sensing data and providing accurate knowledge about the user activity to any assistive robot.

  • 31.
    Bruno, Barbara
    et al.
    Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, Genova, Italy.
    Mastrogiovanni, Fulvio
    Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, Genova, Italy.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Sgorbissa, Antonio
    Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, Genova, Italy.
    Using fuzzy logic to enhance classification of human motion primitives2014In: Information Processing and Management of Uncertainty in Knowledge-Based Systems: 15th International Conference, IPMU 2014, Montpellier, France, July 15-19, 2014, Proceedings, Part II / [ed] Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R., Springer, 2014, 596-605 p.Chapter in book (Refereed)
    Abstract [en]

    The design of automated systems for the recognition of specific human activities is among the most promising research activities in Ambient Intelligence. The literature suggests the adoption of wearable devices, relying on acceleration information to model the activities of interest and distance metrics for the comparison of such models with the run-time data. Most current solutions do not explicitly model the uncertainty associated with the recognition, but rely on crisp thresholds and comparisons which introduce brittleness and inaccuracy in the system. We propose a framework for the recognition of simple activities in which recognition uncertainty is modelled using possibility distributions. We show that reasoning about this explicitly modelled uncertainty leads to a system with enhanced recognition accuracy and precision.

  • 32.
    Buschka, Pär
    et al.
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    A virtual sensor for room detection2002In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems: IROS - Lausanne, CH, 2002, 2002, 637-642 p.Conference paper (Refereed)
    Abstract [en]

    Indoor environments typically consist of sets of connected room-like spaces. We present a local technique that uses range data to detect these spaces during navigation. Our technique includes two parts: segmentation, which isolates room-like spaces and detects when the robot has entered a new one; and feature extraction, which associates each space with a set of geometric features useful for navigation or recognition. Many such features can be considered: here we propose a new method to compute width and length of a rectangular room in a way which is largely invariant with respect to the configuration of the furniture. We report experimental results that show the performance of our technique, and hint at a possible use of this technique for coarse localization on a topological map

  • 33.
    Buschka, Pär
    et al.
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Room detection for topology-based map building2002In: Proceedings of the 2nd Swedish workshop on autonomous robots, 2002, 39-44 p.Conference paper (Other (popular science, discussion, etc.))
    Abstract [en]

    Indoor environments typically consist of sets of connected room-like spaces. We present a local technique that uses range data to detect these spaces during navigation. This is done by a segmentation which isolates room-like spaces and detects when the robot has entered a new one. These spaces can be seen as nodes in a topological map and we show how to incrementally build such a map. We also report experimental results that show the performance of our technique

  • 34.
    Buschka, Pär
    et al.
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Some notes on the use of hybrid maps for mobile robots2004In: Proceedings of the 8th international conference on intelligent autonomous systems, 2004, 547-556 p.Conference paper (Refereed)
    Abstract [en]

    Hybrid maps are quickly becoming popular in the field of mobile robotics. There is, however, little understanding of the general principles that can be used to combine different maps into a hybrid one, and to make these maps to cooperate. In this note, we propose a definition and a classification of hybrid maps, and discuss the synergies that can make a hybrid map something more than the sum of its parts. We illustrate these points with experimental results obtained on a metric-topological map.

  • 35.
    Buschka, Pär
    et al.
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Wasik, Zbigniew
    Örebro University, Department of Technology.
    Fuzzy landmark-based localization for a legged robot2000In: Proceedings, 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems: IROS 2000 - Takamatsu, Japan, 2000, 2000, 1205-1210 p.Conference paper (Refereed)
    Abstract [en]

    We describe a new technique for landmark-based self-localization which is suitable for robots with poor odometry. This technique uses fuzzy logic to account for errors and imprecision in visual recognition, and for extreme uncertainty in the estimate of the robot's motion. It only requires an approximate model of the sensor system and a qualitative estimate of the robot's displacement, and it has a moderate computational cost. We show examples of use of our technique on a Sony AIBO legged robot in the RoboCup domain.

  • 36.
    Canovas, Juan-Pedro
    et al.
    University of Murcia, Spain.
    LeBlanc, Kevin
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Cooperative object localization using fuzzy logic2003In: Proceedings of the IEEE international conference on methods and models in automation and robotics: MMAR, 2003, 773-778 p.Conference paper (Refereed)
    Abstract [en]

    Cooperative localization of objects is an important challenge in multi-robot systems. We propose a new approach to cooperative object localization by a group of communicating robots. In our approach we see each robot as an expert which provides unreliable information about the location of objects. The information provided by different robots is combined using fuzzy logic techniques, in order to reach agreement between the robots. This contrasts with current techniques, which average the information provided by different robots, and can incur well-known problems when information is unreliable. We have tested our technique on a team of Sony AIBO robots in the RoboCup domain. We present experimental results obtained by sharing information about the location of the ball

  • 37.
    Canovas, Juan-Pedro
    et al.
    University of Murcia, Spain.
    LeBlanc, Kevin
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Robust multi-robot object localization using fuzzy logic2005In: RoboCup 2004: robot soccer world cup VIII / [ed] Daniele Nardi, Martin Riedmiller, Claude Sammut, José Santos-Victor, Springer Berlin/Heidelberg, 2005, 247-261 p.Conference paper (Refereed)
    Abstract [en]

    Cooperative localization of objects is an important challenge in multi-robot systems. We propose a new approach to this problem where we see each robot as an expert which shares unreliable information about object locations. The information provided by different robots is then combined using fuzzy logic techniques, in order to reach a consensus between the robots. This contrasts with most current probabilistic techniques, which average information from different robots in order to obtain a tradeoff, and can thus incur well-known problems when information is unreliable. In addition, our approach does not assume that the robots have accurate self-localization. Instead, uncertainty in the pose of the sensing robot is propagated to object position estimates. We present experimental results obtained on a team of Sony AIBO robots, where we share information about the location of the ball in the RoboCup domain

  • 38.
    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.

  • 39.
    Chella, Antonio
    et al.
    Università di Palermo.
    Coradeschi, Silvia
    Örebro University, Department of Technology.
    Frixione, Marcello
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Perceptual anchoring via conceptual spaces2004Conference paper (Refereed)
    Abstract [en]

    Perceptual anchoring is the problem of creating and maintaining in time the connection between symbols and sensor data that refer to the same physical objects. This is one of the facets of the general problem of integrating symbolic and non-symbolic processes in an intelligent system. Gärdenfors' conceptual spaces provide a geometric treatment of knowledge which bridges the gap between the symbolic and sub-symbolic approaches. As such, they can be used for the study of the anchoring problem. In this paper, we propose a computational framework for anchoring based on conceptual spaces. Our framework exploits the geometric structure of conceptual spaces for many of the crucial tasks of anchoring, like matching percepts to symbolic descriptions or tracking the evolution of objects over time.

  • 40.
    Cirillo, Marcello
    et al.
    Örebro University, School of Science and Technology.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    A framework for human-aware robot planning2008In: Tenth Scandinavian conference on artificial intelligence / [ed] A. Holst, P. Kreuger, P. Funk, Amsterdam: IOS press , 2008, 52-59 p.Conference paper (Refereed)
    Abstract [en]

    Robots that share their workspace with humans, like household or service robots, need to take into account the presence of humans when planning their actions. In this paper, we present a framework for human-aware planning in which we consider three kinds of human-robot interaction. We focus in particular on the core module of the framework, a human-aware planner that generates a sequence of actions for a robot, taking into account the status of the environment, the goals of the robot and the forecasted plan of the human. We present a first realization of this planner, together with two simple experiments that demonstrate the feasibility of our approach.

  • 41.
    Cirillo, Marcello
    et al.
    Örebro University, School of Science and Technology.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    A human-aware robot task planner2009In: Proceedings of the 19th international conference on automated planning and scheduling, ICAPS 2009 / [ed] Alfonso Gerevini, Adele Howe, Amedeo Cesta, Ioannis Refanidis, Menlo Park: AAAI press , 2009, 58-65 p.Conference paper (Refereed)
    Abstract [en]

    The growing presence of household robots in inhabited environments arises the need for new robot task planning techniques. These techniques should take into consideration not only the actions that the robot can perform or unexpected external events, but also the actions performed by a human sharing the same environment, in order to improve the cohabitation of the two agents, e.g., by avoiding undesired situations for the human. In this paper, we present a human-aware planner able to address this problem. This planner supports alternative hypotheses of the human plan, temporal duration for the actions of both the robot and the human, constraints on the interaction between robot and human, partial goal achievement and, most importantly, the possibility to use observations of human actions in the policy generated for the robot. The planner has been tested as a standalone component and in conjunction with our framework for human-robot interaction in a real environment.

  • 42.
    Cirillo, Marcello
    et al.
    Örebro University, School of Science and Technology.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Human-aware planning for robots embedded in ambient ecologies2012In: Pervasive and Mobile Computing, ISSN 1574-1192, E-ISSN 1873-1589, Vol. 8, no 4, 542-561 p.Article in journal (Refereed)
    Abstract [en]

    We address the issue of human-robot cohabitation in smart environments. In particular, the presence of humans in a robot's work space has a profound influence on how the latter should plan its actions. We propose the use of Human-Aware Planning, an approach in which the robot exploits the capabilities of a sensor-rich environment to obtain information about the (current and future) activities of the people in the environment, and plans its tasks accordingly.

    Here, we formally describe the planning problem behind our approach, we analyze its complexity and we detail the algorithm of our planner. We then show two application scenarios that could benefit from the techniques described. The first scenario illustrates the applicability of human-aware planning in a domestic setting, while the second one illustrates its use for a robotic helper in a hospital. Finally, we present a five hour-long test run in a smart home equipped with real sensors, where a cleaning robot has been deployed and where a human subject is acting. This test run in a real setting is meant to demonstrate the feasibility of our approach to human-robot interaction.

  • 43.
    Cirillo, Marcello
    et al.
    Örebro University, School of Science and Technology.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Human-aware task planning: an application to mobile robots2010In: ACM transactions on intelligent systems and technology, ISSN 2157-6904, Vol. 1, no 2, Article 15- p.Article in journal (Refereed)
    Abstract [en]

    Consider a house cleaning robot planning its activities for the day. Assume that the robot expects the human inhabitant to first dress, then have breakfast, and finally go out. Then, it should plan not to clean the bedroom while the human is dressing, and to clean the kitchen after the human has had breakfast. In general, robots operating in inhabited environments, like households and future factory floors, should plan their behavior taking into account the actions that will be performed by the humans sharing the same environment. This would improve human-robot cohabitation, for example, by avoiding undesired situations for the human. Unfortunately, current task planners only consider the robot's actions and unexpected external events in the planning process, and cannot accommodate expectations about the actions of the humans.

    In this article, we present a human-aware planner able to address this problem. Our planner supports alternative hypotheses of the human plan, temporal duration for the actions of both the robot and the human, constraints on the interaction between robot and human, partial goal achievement and, most importantly, the possibility to use observations of human actions in the policy generated for the robot. Our planner has been tested both as a stand-alone component and within a full framework for human-robot interaction in a real environment.

  • 44.
    Cirillo, Marcello
    et al.
    Örebro University, School of Science and Technology.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Human-aware task planning for mobile robots2009In: Proceedings of the 5th international conference on advanced robotics, ICAR 2009, New York: IEEE conference proceedings, 2009, 172-178 p.Conference paper (Refereed)
    Abstract [en]

    Robots that share their workspace with people, like household or service robots, need to take into account the presence of humans when planning their actions. In this paper, we present a framework for human-aware planning that would make the robots capable of performing their tasks without interfering with the user in his every day life. We focus in particular on the core module of the framework, a humanaware planner that generates a sequence of actions for a robot, taking into account the state of the environment and the goals of the robot, together with a set of forecasted possible plans of the human. We describe the planner and its relations to other system components like a plan recognizer, and present a series of experiments performed with a household robot in a small apartment.

  • 45.
    Cirillo, Marcello
    et al.
    Örebro University, School of Science and Technology.
    Lanzellotto, Federica
    Roma 3 University, Rome, Italy.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Monitoring domestic activities with temporal constraints and components2009In: Intelligent environments 2009 / [ed] V. Callaghan, A. Kameas, A. Reyes, D. Royo, M. Weber, Amsterdam: IOS Press, 2009, 117-124 p.Conference paper (Refereed)
    Abstract [en]

    Intelligent environments are increasingly rich in ubiquitous sensing capabilities that can be leveraged to know which actions a user is engaged in at any given moment in time. The ability of an intelligent environment to recognize a high-level plan of activities performed by the user in a smart home would allow to construct proactive services, such as reminding, forecasting and providing timely physical support. This article proposes an approach to human activity recognition based on temporal planning. The approach leverages on one hand the ubiquitous sensors provided by the PEIS-Home, a sensor-rich intelligent environment, and, on the other hand, the temporal representation and reasoning capabilities of OMPS, a constraint-based temporal planning and scheduling framework.

  • 46.
    Cirillo, Marcello
    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.
    Proactive assistance in ecologies of physically embedded intelligent systems: a constraint-based approach2011In: Handbook of research on ambient intelligence and smart environments: trends and perspectives / [ed] Nak-Young Chong, Fulvio Mastrogiovanni, IGI Global, 2011, 534-557 p.Chapter in book (Refereed)
    Abstract [en]

    The main goal of this Chapter is to introduce SAM, an integrated architecture for concurrent activity recognition, planning and execution. SAM provides a general framework to define how an intelligent environment can assess contextual information from sensory data. The architecture builds upon a temporal reasoning framework operating in closed-loop between physical sensing and actuation components in a smart environments. The capabilities of the system as well as possible examples of its use are discussed in the context of the PEIS-Home, a smart environment integrated with robotic components.

  • 47.
    Coradeschi, Silvia
    et al.
    Örebro University, Department of Technology.
    Driankov, Dimiter
    Örebro University, Department of Technology.
    Karlsson, Lars
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Fuzzy anchoring2001In: The 10th IEEE international conference on fuzzy systems, 2001, 111-114 p.Conference paper (Refereed)
    Abstract [en]

    An intelligent physical agent must incorporate motor and perceptual processes to interface with the physical world, and abstract cognitive processes to reason about the world and the options available. One crucial aspect of incorporating cognitive processes into a physically embedded reasoning system is the integration between the symbols used by the reasoning processes to denote physical objects, and the perceptual data corresponding to these objects. We treat this integration aspect by proposing a fuzzy computational theory of anchoring. Anchoring is the process of creating and maintaining the correspondence between symbols and percepts that refer to the same physical objects. Modeling this process using fuzzy set-theoretic notions enables dealing with perceptual data that can be affected by uncertainty/imprecision and imprecise/vague linguistic descriptions of objects

  • 48.
    Coradeschi, Silvia
    et al.
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    An introduction to the anchoring problem2003In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 0921-8830, Vol. 43, no 2-3, 85-96 p.Article in journal (Refereed)
    Abstract [en]

    Anchoring is the problem of connecting, inside an artificial system, symbols and sensor data that refer to the same physical objects in the external world. This problem needs to be solved in any robotic system that incorporates a symbolic component. However, it is only recently that the anchoring problem has started to be addressed as a problem per se, and a few general solutions have begun to appear in the literature. This paper introduces the special issue on perceptual anchoring of the Robotics and Autonomous Systems journal. Our goal is to provide a general overview of the anchoring problem, and to highlight some of its subtle points

  • 49.
    Coradeschi, Silvia
    et al.
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Anchoring symbols to sensor data: preliminary report2000In: Proceedings of the 17th AAAI conference, 2000, 129-135 p.Conference paper (Refereed)
    Abstract [en]

    Anchoring is the process of creating and maintaining the correspondence between symbols and percepts that refer to the same physical objects. Although this process must necessarily be present in any physically embedded system that includes a symbolic component (e.g., an autonomous robot), no systematic study of anchoring as a problem per se has been reported in the literature on intelligent systems. In this paper, we propose a domain-independent definition of the anchoring problem, and identify its three basic functionalities: find, reacquire, and track. We illustrate our definition on two systems operating in two different domains: an unmanned airborne vehicle for traffic surveillance; and a mobile robot for office navigation.

  • 50.
    Coradeschi, Silvia
    et al.
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Anchoring symbols to vision data by fuzzy logic1999In: Symbolic and quantitative approaches to reasoning and uncertainty: European conference, ECSQARU '99 : proceedings / [ed] Anthony Hunter, Simon Parsons, Berlin/Heidelberg: Springer Berlin/Heidelberg, 1999, 104-115 p.Conference paper (Refereed)
    Abstract [en]

    Intelligent agents embedded in physical environments need the ability to connect, or anchor, the symbols used to perform abstract reasoning to the physical entities which these symbols refer to. Anchoring must rely on perceptual data which is inherently affected by uncertainty. We propose an anchoring technique based on the use of fuzzy sets to represent uncertainty, and of degree of subset-hood to compute the partial match between signatures of objects. We show examples where we use this technique to allow a deliberative system to reason about the objects (cars) observed by a vision system embarked in an unmanned helicopter, in the framework of the WITAS project.

1234 1 - 50 of 180
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf