ClearPath: highly parallel collision avoidance for multi-agent simulation

  • Authors:
  • Stephen. J. Guy;Jatin Chhugani;Changkyu Kim;Nadathur Satish;Ming Lin;Dinesh Manocha;Pradeep Dubey

  • Affiliations:
  • University of North Carolina at Chapel Hill;Intel Corporation;Intel Corporation;Intel Corporation;University of North Carolina at Chapel Hill;University of North Carolina at Chapel Hill;Intel Corporation

  • Venue:
  • Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
  • Year:
  • 2009

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Abstract

We present a new local collision avoidance algorithm between multiple agents for real-time simulations. Our approach extends the notion of velocity obstacles from robotics and formulates the conditions for collision free navigation as a quadratic optimization problem. We use a discrete optimization method to efficiently compute the motion of each agent. This resulting algorithm can be parallelized by exploiting data-parallelism and thread-level parallelism. The overall approach, ClearPath, is general and can robustly handle dense scenarios with tens or hundreds of thousands of heterogeneous agents in a few milli-seconds. As compared to prior collision avoidance algorithms, we observe more than an order of magnitude performance improvement.