Moving in a Crowd: Safe and Efficient Navigation among Heterogeneous Agents
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 294-299.
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 anda state-of-the-art local navigation framework.
@inproceedings{ijcai2016p49,
title = {Moving in a Crowd: Safe and Efficient Navigation among Heterogeneous Agents},
author = {Godoy, Julio and Karamouzas, Ioannis and Guy, Stephen J. and Gini, Maria L.},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {294--299},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/49},
}
TY - CPAPER TI - Moving in a Crowd: Safe and Efficient Navigation among Heterogeneous Agents AU - Godoy, Julio AU - Karamouzas, Ioannis AU - Guy, Stephen J. AU - Gini, Maria L. T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 294 EP - 299 UR - https://ijcai.org/proceedings/2016/49 ER -Copied
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