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    High level decomposition for bipedal locomotion planning

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    GREY-DISSERTATION-2017.pdf (6.313Mb)
    Date
    2017-08-02
    Author
    Grey, Michael Xander
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    Abstract
    Legged robotic platforms offer an attractive potential for deployment in hazardous scenarios that would be too dangerous for human workers. Legs provide a robot with the ability to step over obstacles and traverse steep, uneven, or narrow terrain. Such conditions are common in dangerous environments, such as a collapsing building or a nuclear facility during a meltdown. However, identifying the physical motions that a legged robot needs to perform in order to move itself through such an environment is particularly challenging. A human operator may be able to manually design such a motion on a case-by-case basis, but it would be inordinately time-consuming and unsuitable for real-world deployment. This thesis presents a method to decompose challenging large-scale motion planning problems into a high-level planning problem and a set of parallel low-level planning problems. We apply the method to quasi-static bipedal locomotion planning. The method is tested in a series of simulated environments that are designed to reflect some of the challenging geometric features that a robot may face in a disaster scenario. We analyze the improvement in performance that is provided by the high- and low-level decomposition, and we show that completeness is not lost by this decomposition.
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    http://hdl.handle.net/1853/58713
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    • Georgia Tech Theses and Dissertations [23877]
    • School of Aerospace Engineering Theses and Dissertations [1440]

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