Accelerating Route Planning and Collision Detection for Computer Generated Forces Using GPUs

Abstract

We present algorithms to accelerate route planning and collision detection for computer generated forces. Our algorithms exploit the parallel computing capability of Graphics Processing Units (GPUs) along with their ability to perform geometric culling. We combine the GPU accelerated computations with exact intersection tests on the CPU. Our approach supports dynamic terrains and multiple feature intersections in parallel. Our technique has been integrated into OneSAF block D build 24. Our route planning technique is a 30x - 50x speedup and has demonstrated an overall speedup of 10x. Our collision detection code is a 5x - 10x speedup over existing collision detection techniques.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2006
Accession Number
ADA481888

Entities

People

  • Angel Rodriguez
  • Brian Salomon
  • David Tuft
  • Dinesh Manocha
  • Maria Bauer
  • Michael Macedonia
  • Ming C. Lin
  • Naga Govindaraju
  • Russell Gayle

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Acquisition
  • Algorithms
  • Collision Avoidance
  • Collisions
  • Computational Complexity
  • Computations
  • Computers
  • Detection
  • Graphics
  • Graphics Processing Unit
  • Image Processing
  • Military Operations
  • Motion Planning
  • Simulations
  • Simulators
  • Training

Fields of Study

  • Computer science

Readers

  • Computational Fluid Dynamics (CFD)
  • Neural Network Machine Learning.
  • Systems Analysis and Design