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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.
    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.

  • 3.
    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.

  • 4.
    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.

  • 5.
    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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