Trabajo presentado al IROS celebrado en San Francisco del 25 al 30 de septiembre de 2011. ; Existing sampling-based robot motion planning methods are often inefficient at finding trajectories for kinodynamic systems, especially in the presence of narrow passages between obstacles and uncertainty in control and sensing. To address this, we propose EG-RRT, an Environment-Guided variant of RRT designed for kinodynamic robot systems that combines elements from several prior approaches and may incorporate a cost model based on the LQG-MP framework to estimate the probability of collision under uncertainty in control and sensing. We compare the performance of EG-RRT with several prior approaches on challenging sample problems. Results suggest that EG-RRT offers significant improvements in performance. ; This work has been partially supported by the Spanish Ministry of Science and Innovation under project DPI2010-18449 and by the U.S. National Institute of Health under Award 1R01EB-006435-01A1 and by the U.S. National Science Foundation under Award 0905344. ; Peer Reviewed


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    Title :

    EG-RRT: Environment-guided random trees for kinodynamic motion planning with uncertainty and obstacles


    Contributors:

    Publication date :

    2011-01-01



    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    DDC:    629



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