Hybrid path planning for massive crowd simulation on the GPU

  • Authors:
  • Aljosha Demeulemeester;Charles-Frederik Hollemeersch;Pieter Mees;Bart Pieters;Peter Lambert;Rik Van de Walle

  • Affiliations:
  • Department of Electronics and Information Systems, Multimedia Lab, Ghent University - IBBT, Ledeberg-Ghent, Belgium;Department of Electronics and Information Systems, Multimedia Lab, Ghent University - IBBT, Ledeberg-Ghent, Belgium;Department of Electronics and Information Systems, Multimedia Lab, Ghent University - IBBT, Ledeberg-Ghent, Belgium;Department of Electronics and Information Systems, Multimedia Lab, Ghent University - IBBT, Ledeberg-Ghent, Belgium;Department of Electronics and Information Systems, Multimedia Lab, Ghent University - IBBT, Ledeberg-Ghent, Belgium;Department of Electronics and Information Systems, Multimedia Lab, Ghent University - IBBT, Ledeberg-Ghent, Belgium

  • Venue:
  • MIG'11 Proceedings of the 4th international conference on Motion in Games
  • Year:
  • 2011

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Abstract

In modern day games, it is often desirable to have many agents navigating intelligently through detailed environments. However, intelligent navigation remains a computationally expensive and complicated problem. In the past, the continuum crowds algorithm demonstrated the value of using a dynamic potential field to guide many agents to a common goal location. However this algorithm is prohibitively resource intensive for real time applications using large and detailed virtual worlds. In this paper, we propose a novel hybrid system that first uses a coarse A* path finding step. This helps to eliminate unnecessary work during the potential field generation by excluding areas of the world from the potential field calculation. Additionally, we show how an optimized potential field solver can be implemented on the GPU using the concepts of persistent threads and inter-block communication. Results show that our system achieves considerable speedups compared to existing path planning systems and that up to 100,000 agents can be simulated and rendered in real time on a mainstream GPU.