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Real-time path planning in dynamic virtual environments using multiagent navigation graphs.

Avneesh Sud1, Erik Andersen, Sean Curtis

  • 1Microsoft Corporation, Redmond, WA 98052, USA. avneesh.sud@microsoft.com

IEEE Transactions on Visualization and Computer Graphics
|March 29, 2008
PubMed
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This study introduces the Multi-agent Navigation Graph (MaNG) for efficient real-time path planning and navigation of multiple virtual agents in dynamic environments. The MaNG enables complex simulations with hundreds of agents, enhancing computational speed and accuracy.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Robotics

Background:

  • Efficient path planning for multiple agents in dynamic environments is computationally challenging.
  • Existing methods struggle with real-time performance and scalability for large numbers of agents.
  • Dynamic scenes require adaptive navigation strategies that consider agent interactions.

Purpose of the Study:

  • To develop a novel, efficient approach for real-time path planning and navigation of multiple virtual agents.
  • To introduce a new data structure, the Multi-agent Navigation Graph (MaNG), for improved agent coordination.
  • To enhance the accuracy and computational speed of multi-agent navigation algorithms.

Main Methods:

  • Introduced the Multi-agent Navigation Graph (MaNG) data structure, built using first- and second-order Voronoi diagrams.

Related Experiment Videos

  • Utilized MaNG for real-time route planning and proximity computations.
  • Extended a social force model for local dynamics computation, incorporating path and proximity information.
  • Employed graphics hardware for MaNG computation and implemented culling techniques for acceleration.
  • Developed techniques to address undersampling issues and improve algorithmic accuracy.

    • Main Results:

    • Demonstrated real-time multi-agent planning capabilities in complex dynamic scenes.
    • Successfully simulated scenarios with hundreds of agents, each pursuing distinct goals.
    • Achieved efficient route planning and proximity computations through the MaNG data structure.
    • Improved computational speed via graphics hardware acceleration and culling techniques.
    • Showcased enhanced accuracy by addressing undersampling issues.

    Conclusions:

    • The Multi-agent Navigation Graph (MaNG) provides an efficient and scalable solution for real-time multi-agent path planning and navigation.
    • The proposed approach effectively handles complex dynamic scenes and large numbers of agents with distinct goals.
    • The integration of MaNG with an extended social force model offers robust local dynamics computation for realistic agent behavior.
    • The use of graphics hardware and culling techniques significantly accelerates MaNG computation, enabling real-time performance.