Motion Planning with Discrete Abstractions and Physics-Based Game Engines

  • Erion Plaku
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7660)


To increase automation in game design, this paper proposes a sampling-based motion-planning approach that works in conjunction with physics-based game engines. The approach automatically computes a sequence of motions that enables a virtual agent to reach a desired destination while avoiding collisions. The use of physics-based engines as the underlying simulator results in physically-realistic motions that take into account the motion dynamics, friction, gravity, and other forces interacting with the virtual agent. To account for the increased complexity and achieve computational efficiency, the approach expands a motion tree from the initial state to the goal using discrete abstractions as a guide in a best-first search fashion. Parametrized motion controllers are combined with randomized sampling to enable the motion planner to expand the motion tree along different directions. Comparisons to related work show significant computational speedups.


Motion Planning Game Design Motion Controller Virtual Agent Goal Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Erion Plaku
    • 1
  1. 1.Dept. of Electrical Engineering and Computer ScienceCatholic University of AmericaWashington DCUSA

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