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Description
  • The task of planning trajectories plays an important role in transportation, robotics, even in information systems (sending messages). In robot motion planning the robot should pass around the obstacles, from a given starting position to a given target position, touching none of them, i.e. the goal is to find a collision-free path from the starting to the target position. This task has many specific formulations depending on the shape of obstacles, allowable directions of movements, knowledge of the sccene, etc. Research on path planning has yielded many fundamentally different approaches to its solution, e.g. visibility graph method or the shortest path map method. Assuming movements only in a restricted number of directions (eight directional, horizontal/vertical) the task, with respect to its combinatorial nature, can be solved by heuristic techniques (genetic algorithms, simulated annealing, tabu-search). We present a framework of such approach and show its drawbacks (combinatorial explosion, lim
  • The task of planning trajectories plays an important role in transportation, robotics, even in information systems (sending messages). In robot motion planning the robot should pass around the obstacles, from a given starting position to a given target position, touching none of them, i.e. the goal is to find a collision-free path from the starting to the target position. This task has many specific formulations depending on the shape of obstacles, allowable directions of movements, knowledge of the sccene, etc. Research on path planning has yielded many fundamentally different approaches to its solution, e.g. visibility graph method or the shortest path map method. Assuming movements only in a restricted number of directions (eight directional, horizontal/vertical) the task, with respect to its combinatorial nature, can be solved by heuristic techniques (genetic algorithms, simulated annealing, tabu-search). We present a framework of such approach and show its drawbacks (combinatorial explosion, lim (en)
Title
  • Planning Trajectories in the Plane with Obstacles Using Voronoi Diagrams
  • Planning Trajectories in the Plane with Obstacles Using Voronoi Diagrams (en)
skos:prefLabel
  • Planning Trajectories in the Plane with Obstacles Using Voronoi Diagrams
  • Planning Trajectories in the Plane with Obstacles Using Voronoi Diagrams (en)
skos:notation
  • RIV/00216305:26210/03:PU40441!RIV/2004/MSM/262104/N
http://linked.open.../vavai/riv/strany
  • 541-554
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM 260000013), Z(MSM 261100009), Z(MSM 262100024)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 621005
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26210/03:PU40441
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Motion planning, genetic algorithm, case-based reasoning, Voronoi diagram, rectilinear metric (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [A24439303DBF]
http://linked.open...i/riv/mistoVydani
  • Wien (Austria)
http://linked.open...vEdiceCisloSvazku
  • 2003
http://linked.open...i/riv/nazevZdroje
  • DAAAM International Scientific Book 2003
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...ocetUcastnikuAkce
http://linked.open...nichUcastnikuAkce
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Šeda, Miloš
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • DAAAM International Wien
https://schema.org/isbn
  • 3-901509-36-4
http://localhost/t...ganizacniJednotka
  • 26210
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