This paper presents a randomized motion planner for kinodynamic asteroid avoidance problems, in which a robot must avoid collision with moving obstacles under kinematic, dynamic constraints and reach a specified goal state. Inspired by probabilistic-roadmap (PRM) techniques, the planner samples the state x time space of a robot by picking control inputs at random in order to compute a roadmap that captures the connectivity of the space. However, the planner does not precompute a roadmap as most PRM planners do. Instead, for each planning query, it generates, on the fly, a small roadmap that connects the given initial and goal state. In contrast to PRM planners, the roadmap computed by our algorithm is a directed graph oriented along the time axis of the space. To verify the planner's effectiveness in practice, we tested it both in simulated environments containing many moving obstacles and on a real robot under strict dynamic constraints. The efficiency of the planner makes it possible for a robot to respond to a changing environment without knowing the motion of moving obstacles well in advance.


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    Titel :

    Kinodynamic motion planning amidst moving obstacles


    Beteiligte:
    Kindel, R. (Autor:in) / Hsu, D. (Autor:in) / Latombe, J.C. (Autor:in) / Rock, S. (Autor:in)


    Erscheinungsdatum :

    2000


    Format / Umfang :

    7 Seiten, 20 Quellen




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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