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Sökning: swepub > Örebro universitet > Saffiotti Alessandro > Karlsson Lars

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21.
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23.
  • Karlsson, Lars, et al. (författare)
  • To secure an anchor : a recovery planning approach to ambiguity in perceptual anchoring
  • 2008
  • Ingår i: AI Communications. - Amsterdam : IOS Press. - 0921-7126 .- 1875-8452. ; 21:1, s. 1-14
  • Tidskriftsartikel (refereegranskat)abstract
    • 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. We also present methods to recover from these situations based onautomatically formulating them as conditional planning problems that then are solved by a planner. We illustrate our approach by showing experiments involving a mobile robot equipped with a color camera and an electronic nose.
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24.
  • Lagriffoul, Fabien, 1977- (författare)
  • Combining Task and Motion Planning
  • 2016
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • This thesis addresses the problem of automatically computing, given a high-level goal description, a sequence of actions and motion paths for one or several robots to achieve that goal. Also referred to as CTAMP (Combining Task And Motion Planning), this problem may seem trivial at first glance, since efficient solutions have been found for its two underlying problems, namely task planning and motion planning. However, further consideration reveals that combining task and motion planning, in many cases, is not straightforward. We have identified two important issues which are addressed in this thesis.The first issue originates in the fact that symbolic actions can be geometrically instantiated in multiple ways. Choosing a geometric instance for each action is not trivial, because a “wrong” choice may compromise the feasibility of subsequent actions. To address this issue, in the first part of the thesis we propose a mechanism for backtracking over geometric choices in the context of a partial symbolic plan. This process may greatly increase the complexity of CTAMP. Therefore, we also present a constraint-based approach for pruning out geometric configurations which violate a number of geometric constraints imposed by the action sequence, and by the kinematic models of robots. This approach has been tested with success on the real humanoid robotic platform Justin in the context of the GeRT1 project.The second issue results from the necessity to interleave symbolic and geometric computations for taking geometric constraints into account at the symbolic level. Indeed, the symbolic search space forms an abstraction of the physical world, hence geometric constraints such as objects occlusions or kinematic constraints are not represented. However, interleaving both search processes is not a workable approach for large problem instances, because the resulting search space is too large. In the second part of the thesis, we propose a novel approach for decoupling symbolic and geometric search spaces, while keeping the symbolic level aware of geometric constraints. Culprit detection mechanisms are used for computing explanations for geometric failures, and these explanations are leveraged at the symbolic level for pruning the search space through inference mechanisms. This approach has been extensively tested in simulation, on different types of single and multiple robot systems.
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25.
  • Lagriffoul, Fabien, 1977-, et al. (författare)
  • Combining Task and Motion Planning is Not Always a Good Idea
  • 2013
  • Konferensbidrag (refereegranskat)abstract
    • Combining task and motion planning requires tointerleave causal and geometric reasoning, in order to guaranteethe plan to be executable in the real world. The resulting searchspace, which is the cross product of the symbolic search spaceand the geometric search space, is huge. Systematically calling ageometric reasoner while evaluating symbolic actions is costly. Onthe other hand, geometric reasoning can prune out large parts ofthis search space if geometrically infeasible actions are detectedearly. Hence, we hypothesized the existence of a search depthlevel, until which geometric reasoning can be interleaved withsymbolic reasoning with tractable combinatorial explosion, whilekeeping the benefits of this pruning. In this paper, we propose asimple model that proves the existence of such search depth level,and validate it empirically through experiments in simulation
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26.
  • Lagriffoul, Fabien, 1977-, et al. (författare)
  • Constraint propagation on interval bounds for dealing with geometric backtracking
  • 2012
  • Ingår i: Proceedings of  the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781467317368 ; , s. 957-964
  • Konferensbidrag (refereegranskat)abstract
    • The combination of task and motion planning presents us with a new problem that we call geometric backtracking. This problem arises from the fact that a single symbolic state or action can be geometrically instantiated in infinitely many ways. When a symbolic action cannot begeometrically validated, we may need to backtrack in thespace of geometric configurations, which greatly increases thecomplexity of the whole planning process. In this paper, weaddress this problem using intervals to represent geometricconfigurations, and constraint propagation techniques to shrinkthese intervals according to the geometric constraints of the problem. After propagation, either (i) the intervals are shrunk, thus reducing the search space in which geometric backtracking may occur, or (ii) the constraints are inconsistent, indicating then infeasibility of the sequence of actions without further effort. We illustrate our approach on scenarios in which a two-arm robot manipulates a set of objects, and report experiments that show how the search space is reduced.
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28.
  • Lagriffoul, Fabien, 1977-, et al. (författare)
  • Efficiently combining task and motion planning using geometric constraints
  • 2014
  • Ingår i: The international journal of robotics research. - : SAGE Publications. - 0278-3649 .- 1741-3176. ; 33:14, s. 1726-1747
  • Tidskriftsartikel (refereegranskat)abstract
    • We propose a constraint-based approach to address a class of problems encountered in combined task and motion planning (CTAMP), which we call kinematically constrained problems. CTAMP is a hybrid planning process in which task planning and geometric reasoning are interleaved. During this process, symbolic action sequences generated by a task planner are geometrically evaluated. This geometric evaluation is a search problem per se, which we refer to as geometric backtrack search. In kinematically constrained problems, a significant computational effort is spent on geometric backtrack search, which impairs search at the task level. At the basis of our approach to address this problem, is the introduction of an intermediate layer between task planning and geometric reasoning. A set of constraints is automatically generated from the symbolic action sequences to evaluate, and combined with a set of constraints derived from the kinematic model of the robot. The resulting constraint network is then used to prune the search space during geometric backtrack search. We present experimental evidence that our approach significantly reduces the complexity of geometric backtrack search on various types of problem.
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29.
  • Lundh, Robert, et al. (författare)
  • An algorithm for generating configurations of groups of robots
  • 2007
  • Rapport (övrigt vetenskapligt/konstnärligt)abstract
    • This work is about the use of artificial intelligence (AI) planning techniques to automatically configure cooperation among robots. We consider groups of autonomous robots in which robots can help each other by offering information producing resources and functionalities. A configuration in this context, is a way to allocate and connect functionalities among robots. In general, different configurations can be used to solve the same task, depending on the current situation. Configuration generation is the problem of automatically generating a configuration for some specific purpose given a set of robotic devices possessing dfferent functionalities. In this paper, we consider an existing configuration planner both from a theoretical point of view (soundness, completeness, and optimality), and an empirical point of view (scalability). We also present a technique to improve the scalability of the configuration planner.
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30.
  • Lundh, Robert, et al. (författare)
  • Autonomous functional configuration of a network robot system
  • 2008
  • Ingår i: Robotics and Autonomous Systems. - Amsterdam : Elsevier. - 0921-8890 .- 1872-793X. ; 56:10, s. 819-830
  • Tidskriftsartikel (refereegranskat)abstract
    • We consider distributed systems of networked robots in which: (1) each robot includes sensing, acting and/or processing modular functionalities; and (2) robots can help each other by offering those functionalities. A functional configuration is any way to allocate and connect functionalities among the robots. An interesting feature of a system of this type is the possibility to use different functional configurations to make the same set of robots perform different tasks, or to perform the same task under different conditions. In this paper, we propose an approach to automatically generate at run time a functional configuration of a network robot system to perform a given task in a given environment, and to dynamically change this configuration in response to failures. Our approach is based on artificial intelligence planning techniques, and it is provably sound, complete and optimal. Moreover, our configuration planner can be combined with an action planner to deal with tasks that require sequences of configurations. We illustrate our approach on a specific type of network robot system, called Peis-Ecology, and show experiments in which a sequence of configurations is automatically generated and executed on real robots. These experiments demonstrate that our self-configuration approach can help the system to achieve greater autonomy, flexibility and robustness.
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  • Resultat 21-30 av 41

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