<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><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%">Plaku, E.</style></author><author><style face="normal" font="default" size="100%">L. E. Kavraki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multiple Query Probabilistic Roadmap Planning Using Single Query Planning Primitives</style></title><secondary-title><style face="normal" font="default" size="100%">2003 IEEE/RJS International Conference on Intelligent Robots and Systems (IROS)</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%">2003</style></year><pub-dates><date><style  face="normal" font="default" size="100%">October</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Las Vegas, NV</style></pub-location><pages><style face="normal" font="default" size="100%">656-661</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We propose the combination of techniques that solve
  multiple queries for motion planning problems with single query
  planners. Our implementation uses a probabilistic roadmap method PRM
  with bidirectional rapidly exploring random trees BIRRT as the local
  planner. With small modifications to the standard algorithms, we
  obtain a multiple query planner which is significantly faster and
  more reliable than its component parts. Our method provides a smooth
  spectrum between the PRM and BIRRT techniques and obtains the
  advantages of both. We observed that the performance differences are
  most notable in planning instances with several rigid non-convex
  robots in a scene with narrow passages. This planner is in the
  spirit of non-uniform sampling and refinement techniques used in
  earlier work on PRM.</style></abstract><work-type><style face="normal" font="default" size="100%">inproceedings</style></work-type></record></records></xml>