Dynamic obstacle representations for robot and virtual agent navigation

  • Authors:
  • Eric Aaron;Juan Pablo Mendoza

  • Affiliations:
  • Department of Mathematics and Computer Science, Wesleyan University, Middletown, CT;Department of Mathematics and Computer Science, Wesleyan University, Middletown, CT

  • Venue:
  • Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
  • Year:
  • 2011

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Abstract

This paper describes a reactive navigation method for autonomous agents such as robots or actors in virtual worlds, based on novel dynamic tangent obstacle representations, resulting in exceptionally successful, geometrically sensitive navigation. The method employs three levels of abstraction, treating each obstacle entity as an obstacle-valued function; this treatment enables extraordinary flexibility without pre-computation or deliberation, applying to all obstacles regardless of shape, including non-convex, polygonal, or arc-shaped obstacles in dynamic environments. The unconventional levels of abstraction and the geometric details of dynamic tangent representations are the primary contributions of this work, supporting smooth navigation even in scenarios with curved shapes, such as circular and figure-eight shaped tracks, or in environments requiring complex, winding paths.