<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Plaku, E.</style></author><author><style face="normal" font="default" size="100%">Kostas E. Bekris</style></author><author><style face="normal" font="default" size="100%">Chen, B. Y.</style></author><author><style face="normal" font="default" size="100%">Ladd, A. M.</style></author><author><style face="normal" font="default" size="100%">Lydia E. Kavraki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sampling-Based Roadmap of Trees for Parallel Motion Planning</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Robotics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">kavrakilab</style></keyword><keyword><style  face="normal" font="default" size="100%">path planning</style></keyword><keyword><style  face="normal" font="default" size="100%">project_SRT</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">597-608</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper shows how to effectively combine a
  sampling-based method primarily designed for multiple-query motion
  planning [probabilistic roadmap method (PRM)] with sampling-based
  tree methods primarily designed for single-query motion planning
  (expansive space trees, rapidly exploring random trees, and others)
  in a novel planning framework that can be efficiently
  parallelized. Our planner not only achieves a smooth spectrum
  between multiple-query and single-query planning, but it combines
  advantages of both. We present experiments which show that our
  planner is capable of solving problems that cannot be addressed
  efficiently with PRM or single-query planners. A key advantage of
  our planner is that it is significantly more decoupled than PRM and
  sampling-based tree planners. Exploiting this property, we designed
  and implemented a parallel version of our planner. Our experiments
  show that our planner distributes well and can easily solve
  high-dimensional problems that exhaust resources available to single
  machines and cannot be addressed with existing planners.</style></abstract><work-type><style face="normal" font="default" size="100%">article</style></work-type></record></records></xml>