Steven A. Wilmarth

Texas A&M University, College Station, TX, United States

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Publications (4)0 Total impact

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    ABSTRACT: Motion planning in the presence of obstacles is an important problem in robotics with numerous applications in other areas. While complete motion planning algorithms do exist, they are rarely used in practice since they are computationally infeasible in all but the simplest cases. For this reason, many recent efforts have focused on probabilistic methods, which sacrifice completeness in favor of computational feasibility and applicability. In particular, several algorithms, known as probabilistic roadmap planners, have been shown to perform well in a number of practical situations; however, their performance degrades when paths are required to pass through narrow passages in the free space. In this dissertation we p...
    04/2000;
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    Steven A. Wilmarth, Nancy M. Amato, Peter F. Stiller
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    ABSTRACT: Several motion planning methods using networks of randomly generated nodes in the free space have been shown to perform well in a number of cases, however their performance degrades when paths are required to pass through narrow passages in the free space. In [16] we proposed MAPRM, a method of sampling the configuration space in which randomly generated configurations, free or not, are retracted onto the medial axis of the free space without having to first compute the medial axis; this was shown to increase sampling in narrow passages. In this paper we give details of the MAPRM algorithm for the case of a rigid body moving in three dimensions, and show that the retraction may be carried out without explicitly computing the C-obstacles or the medial axis. We give theoretical and experimental results to show this improves performance on problems involving narrow corridors and compare the performance to uniform random sampling from the free space. # Supported in part by NSF Group Infra...
    01/1999;
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    Steven A. Wilmarth, Nancy M. Amato, Peter F. Stiller
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    ABSTRACT: Probabilistic roadmap planning methods have been shown to perform well in a number of practical situations, but their performance degrades when paths are required to pass through narrow passages in the free space. We propose a new method of sampling the configuration space in which randomly generated configurations, free or not, are retracted onto the medial axis of the free space. We give algorithms that perform this retraction while avoiding explicit computation of the medial axis, and we show that sampling and retracting in this manner increases the number of nodes found in small volume corridors in a way that is independent of the volume of the corridor and depends only on the characteristics of the obstacles bounding it. Theoretical and experimental results are given to show that this improves performance on problems requiring traversal of narrow passages. # Supported in part by NSF Group Infrastructure Grant DMS 96-32028. + Supported in part by NSF CAREER Award CCR-9624315 (wit...
    12/1998;
  • Steven Albert. Wilmarth
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    ABSTRACT: Thesis (Ph. D.)--Texas A & M University, 1999. Includes bibliographical references (leaves 102-107). Vita. "Major Subject: Mathematics."