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  • 1.
    Akbari, Aliakbar
    et al.
    Institute of Industrial and Control Engineering (IOC), Universitat Politècnica de Catalunya (UPC)—Barcelona Tech, Barcelona, Spain.
    Lagriffoul, Fabien
    Örebro University, School of Science and Technology.
    Rosell, Jan
    Institute of Industrial and Control Engineering (IOC), Universitat Politècnica de Catalunya (UPC)—Barcelona Tech, Barcelona, Spain.
    Combined heuristic task and motion planning for bi-manual robots2019In: Autonomous Robots, ISSN 0929-5593, E-ISSN 1573-7527, Vol. 43, no 6, p. 1575-1590Article in journal (Refereed)
    Abstract [en]

    Planning efficiently at task and motion levels allows the setting of new challenges for robotic manipulation problems, like for instance constrained table-top problems for bi-manual robots. In this scope, the appropriate combination of task and motion planning levels plays an important role. Accordingly, a heuristic-based task and motion planning approach is proposed, in which the computation of the heuristic addresses a geometrically relaxed problem, i.e., it only reasons upon objects placements, grasp poses, and inverse kinematics solutions. Motion paths are evaluated lazily, i.e., only after an action has been selected by the heuristic. This reduces the number of calls to the motion planner, while backtracking is reduced because the heuristic captures most of the geometric constraints. The approach has been validated in simulation and on a real robot, with different classes of table-top manipulation problems. Empirical comparison with recent approaches solving similar problems is also reported, showing that the proposed approach results in significant improvement both in terms of planing time and success rate.

  • 2.
    Antanas, Laura
    et al.
    Department of Computer Science, KULeuven, Heverlee, Belgium.
    Moreno, Plinio
    Institute for Systems and Robotics, Lisbon, Portugal.
    Neumann, Marion
    Department of Computer Science and Engineering, Washington University in St Louis, St Louis, USA.
    Pimentel de Figueiredo, Rui
    Institute for Systems and Robotics, Lisbon, Portugal.
    Kersting, Kristian
    Computer Science Department, Technical University of Dortmund, Dortmund, Germany.
    Santos-Victor, José
    Institute for Systems and Robotics, Lisbon, Portugal.
    De Raedt, Luc
    Department of Computer Science, KULeuven, Heverlee, Belgium.
    Semantic and geometric reasoning for robotic grasping: a probabilistic logic approach2019In: Autonomous Robots, ISSN 0929-5593, E-ISSN 1573-7527, Vol. 43, no 6, p. 1393-1418Article in journal (Refereed)
    Abstract [en]

    While any grasp must satisfy the grasping stability criteria, good grasps depend on the specific manipulation scenario: the object, its properties and functionalities, as well as the task and grasp constraints. We propose a probabilistic logic approach for robot grasping, which improves grasping capabilities by leveraging semantic object parts. It provides the robot with semantic reasoning skills about the most likely object part to be grasped, given the task constraints and object properties, while also dealing with the uncertainty of visual perception and grasp planning. The probabilistic logic framework is task-dependent. It semantically reasons about pre-grasp configurations with respect to the intended task and employs object-task affordances and object/task ontologies to encode rules that generalize over similar object parts and object/task categories. The use of probabilistic logic for task-dependent grasping contrasts with current approaches that usually learn direct mappings from visual perceptions to task-dependent grasping points. The logic-based module receives data from a low-level module that extracts semantic objects parts, and sends information to the low-level grasp planner. These three modules define our probabilistic logic framework, which is able to perform robotic grasping in realistic kitchen-related scenarios.

  • 3.
    Duckett, Tom
    et al.
    Örebro University, Department of Technology.
    Marsland, Stephen
    University of Manchester.
    Shapiro, Jonathan
    University of Manchester.
    Fast, on-line learning of globally consistent maps2002In: Autonomous Robots, ISSN 0929-5593, E-ISSN 1573-7527, Vol. 12, no 3, p. 287-300Article in journal (Refereed)
    Abstract [en]

    To navigate in unknown environments, mobile robots require the ability to build their own maps. A major problem for robot map building is that odometry-based dead reckoning cannot be used to assign accurate global position information to a map because of cumulative drift errors. This paper introduces a fast, on-line algorithm for learning geometrically consistent maps using only local metric information. The algorithm works by using a relaxation technique to minimise an energy function over many small steps. The approach differs from previous work in that it is computationally cheap, easy to implement and is proven to converge to a globally optimal solution. Experiments are presented in which large, complex environments were successfully mapped by a real robot.

  • 4.
    Gholami Shahbandi, Saeed
    et al.
    Center for Applied Intelligent Systems Research, Halmstad University, Halmstad, Sweden.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    2D map alignment with region decomposition2019In: Autonomous Robots, ISSN 0929-5593, E-ISSN 1573-7527, Vol. 43, no 5, p. 1117-1136Article in journal (Refereed)
    Abstract [en]

    In many applications of autonomous mobile robots the following problem is encountered. Two maps of the same environment are available, one a prior map and the other a sensor map built by the robot. To benefit from all available information in both maps, the robot must find the correct alignment between the two maps. There exist many approaches to address this challenge, however, most of the previous methods rely on assumptions such as similar modalities of the maps, same scale, or existence of an initial guess for the alignment. In this work we propose a decomposition-based method for 2D spatial map alignment which does not rely on those assumptions. Our proposed method is validated and compared with other approaches, including generic data association approaches and map alignment algorithms. Real world examples of four different environments with thirty six sensor maps and four layout maps are used for this analysis. The maps, along with an implementation of the method, are made publicly available online.

  • 5.
    Loutfi, Amy
    et al.
    Örebro University, Department of Technology.
    Coradeschi, Silvia
    Örebro University, Department of Technology.
    Smell, think and act: a cognitive robot discriminating odours2006In: Autonomous Robots, ISSN 0929-5593, E-ISSN 1573-7527, Vol. 20, no 3, p. 239-249Article in journal (Refereed)
    Abstract [en]

    In this paper, we explore the integration of an electronic nose and its odour discrimination functionalities into a multi-sensing robotic system which works over an extended period of time. The robot patrols an office environment, collecting odour samples of objects and performing user requested tasks. By considering an experimental platforms which operates over an extended period of time, a number of issues related to odour discrimination arise such as the drift in the sensor data, online learning of new odours, and the correct association of odour properties related to objects. In addition to an electronic nose our robotic system consists of other sensing modalities (vision and sonar), behaviour-based control and a high level symbolic planner.

  • 6.
    Moldovan, Bogdan
    et al.
    Department of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium.
    Moreno, Plinio
    Institute for Systems and Robotics (ISR/IST), LARSyS, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal.
    Nitti, Davide
    Department of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium.
    Santos-Victor, José
    Institute for Systems and Robotics (ISR/IST), LARSyS, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal.
    De Raedt, Luc
    Department of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium.
    Relational affordances for multiple-object manipulation2018In: Autonomous Robots, ISSN 0929-5593, E-ISSN 1573-7527, Vol. 42, no 1, p. 19-44Article in journal (Refereed)
    Abstract [en]

    The concept of affordances has been used in robotics to model action opportunities of a robot and as a basis for making decisions involving objects. Affordances capture the interdependencies between the objects and their properties, the executed actions on those objects, and the effects of those respective actions. However, existing affordance models cannot cope with multiple objects that may interact during action execution. Our approach is unique in that possesses the following four characteristics. First, our model employs recent advances in probabilistic programming to learn affordance models that take into account (spatial) relations between different objects, such as relative distances. Two-object interaction models are first learned from the robot interacting with the world in a behavioural exploration stage, and are then employed in worlds with an arbitrary number of objects. The model thus generalizes over both the number of and the particular objects used in the exploration stage, and it also effectively deals with uncertainty. Secondly, rather than using a (discrete) action repertoire, the actions are parametrised according to the motor capabilities of the robot, which allows to model and achieve goals at several levels of complexity. It also supports a two-arm parametrised action. Thirdly, the relational affordance model represents the state of the world using both discrete (action and object features) and continuous (effects) random variables. The effects follow a multivariate Gaussian distribution with the correlated discrete variables (actions and object properties). Fourthly, the learned model can be employed on planning for high-level goals that closely correspond to goals formulated in natural language. The goals are specified by means of (spatial) relations between the objects. The model is evaluated in real experiments using an iCub robot given a series of such planning goals of increasing difficulty.

  • 7.
    Stachniss, Cyrill
    et al.
    University of Freiburg.
    Plagemann, Christian
    Stanford University.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Learning Gas Distribution Models Using Sparse Gaussian Process Mixtures2009In: Autonomous Robots, ISSN 0929-5593, E-ISSN 1573-7527, Vol. 26, no 2-3, p. 187-202Article in journal (Refereed)
    Abstract [en]

    In this paper, we consider the problem of learning two-dimensional spatial models of gas distributions. To build models of gas distributions that can be used to accurately predict the gas concentration at query locations is a challenging task due to the chaotic nature of gas dispersal. We formulate this task as a regression problem. To deal with the specific properties of gas distributions, we propose a sparse Gaussian process mixture model, which allows us to accurately represent the smooth background signal and the areas with patches of high concentrations. We furthermore integrate the sparsification of the training data into an EM procedure that we apply for learning the mixture components and the gating function. Our approach has been implemented and tested using datasets recorded with a real mobile robot equipped with an electronic nose. The experiments demonstrate that our technique is well-suited for predicting gas concentrations at new query locations and that it outperforms alternative and previously proposed methods in robotics.

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