Moving in a crowd: Safe and efficient navigation among heterogeneous agents

Julio Godoy, Ioannis Karamouzas, Stephen J. Guy, Maria Gini

Research output: Contribution to journalConference articlepeer-review

10 Scopus citations

Abstract

Multi-agent navigation methods typically assume that all agents use the same underlying framework to navigate to their goal while avoiding colliding with each other. However, such assumption does not hold when agents do not know how other agents will move. We address this issue by proposing a Bayesian inference approach where an agent estimates the navigation model and goal of each neighbor, and uses this to compute a plan that minimizes collisions while driving it to its goal. Simulation experiments performed in many scenarios demonstrate that an agent using our approach computes safer and more time-efficient paths as compared to those generated without our inference approach and a state-of-the-art local navigation framework.

Original languageEnglish (US)
Pages (from-to)294-300
Number of pages7
JournalIJCAI International Joint Conference on Artificial Intelligence
Volume2016-January
StatePublished - 2016
Event25th International Joint Conference on Artificial Intelligence, IJCAI 2016 - New York, United States
Duration: Jul 9 2016Jul 15 2016

Bibliographical note

Funding Information:
Partial support for this work is acknowledged from the University of Minnesota Informatics Institute and NSF through grants #CNS-1544887 and #CHS-1526693

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