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Related Experiment Video

Updated: Jun 28, 2026

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

Interactive navigation of heterogeneous agents using adaptive roadmaps.

Russell Gayle¹, Avneesh Sud, Erik Andersen

¹Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599-3175, USA. rgayle@cs.unc.edu

IEEE Transactions on Visualization and Computer Graphics

|November 15, 2008

Summary

This summary is machine-generated.

This study introduces a new algorithm for collision-free navigation, enabling hundreds of agents to move safely in complex environments. It uses adaptive roadmaps and dynamic simulations for real-time, multi-agent path planning.

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Area of Science:

Robotics
Artificial Intelligence
Computer Science

Background:

Navigating large numbers of independent agents in complex, dynamic environments presents significant challenges.
Existing algorithms often struggle with real-time adaptation to dynamic obstacles and inter-agent interactions.

Purpose of the Study:

To develop a novel algorithm for collision-free navigation of numerous independent agents.
To enable simultaneous global path planning for each agent while considering environmental dynamics and agent interactions.

Main Methods:

Introduction of adaptive roadmaps for simultaneous global path planning.
Integration of a physically-based dynamics simulator to continuously update roadmaps.
Adaptive particle-based sampling for efficient link updates.
Development of 'link bands' to resolve multi-agent collisions.

Main Results:

The algorithm successfully performs real-time navigation for hundreds to thousands of agents.
Demonstrated effectiveness in both indoor and outdoor complex environments.
Handles dynamic obstacles and inter-agent forces through continuous roadmap adaptation.

Conclusions:

The proposed algorithm offers an efficient and effective solution for large-scale, multi-agent navigation.
Adaptive roadmaps and dynamic simulation provide robust collision-free path planning.
Enables real-time crowd simulation and navigation in complex scenarios.