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    Sampling based motion planning with reachable volumes: Application to manipulators and closed chain systems

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    Type
    Conference Paper
    Authors
    McMahon, Troy
    Thomas, Shawna
    Amato, Nancy M.
    KAUST Grant Number
    KUS-C1-016-04
    Date
    2014-09
    Permanent link to this record
    http://hdl.handle.net/10754/599551
    
    Metadata
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    Abstract
    © 2014 IEEE. Reachable volumes are a geometric representation of the regions the joints of a robot can reach. They can be used to generate constraint satisfying samples for problems including complicated linkage robots (e.g. closed chains and graspers). They can also be used to assist robot operators and to help in robot design.We show that reachable volumes have an O(1) complexity in unconstrained problems as well as in many constrained problems. We also show that reachable volumes can be computed in linear time and that reachable volume samples can be generated in linear time in problems without constraints. We experimentally validate reachable volume sampling, both with and without constraints on end effectors and/or internal joints. We show that reachable volume samples are less likely to be invalid due to self-collisions, making reachable volume sampling significantly more efficient for higher dimensional problems. We also show that these samples are easier to connect than others, resulting in better connected roadmaps. We demonstrate that our method can be applied to 262-dof, multi-loop, and tree-like linkages including combinations of planar, prismatic and spherical joints. In contrast, existing methods either cannot be used for these problems or do not produce good quality solutions.
    Citation
    McMahon T, Thomas S, Amato NM (2014) Sampling based motion planning with reachable volumes: Application to manipulators and closed chain systems. 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. Available: http://dx.doi.org/10.1109/IROS.2014.6943082.
    Sponsors
    This research supported in part by NSF awards CNS-0551685, CCF-0833199, CCF-0830753, IIS-0916053, IIS-0917266, EFRI-1240483, RI-1217991, by NIH NCI R25 CA090301-11, by Chevron, IBM, Intel,Oracle/Sun and by Award KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).Parasol Lab., Dept. of Comp. Sci. and Eng., Texas A&M Univ., College Station, Texas, USA
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2014 IEEE/RSJ International Conference on Intelligent Robots and Systems
    DOI
    10.1109/IROS.2014.6943082
    ae974a485f413a2113503eed53cd6c53
    10.1109/IROS.2014.6943082
    Scopus Count
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