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2024 | OriginalPaper | Buchkapitel

A Meta-MDP Approach for Information Gathering Heterogeneous Multi-agent Systems

verfasst von : Alvin Gandois, Abdel-Illah Mouaddib, Simon Le Gloannec, Ayman Alfalou

Erschienen in: Robotics, Computer Vision and Intelligent Systems

Verlag: Springer Nature Switzerland

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Abstract

In this paper, we address the problem of heterogeneous multi-robot cooperation for information gathering and situation evaluation in a stochastic and partially observable environment. The goal is to optimally gather information about targets in the environment with several robots having different capabilities. The classical Dec-POMDP framework is a good tool to compute an optimal joint policy for such problems. However, its scalability is weak. To overcome this limitation, we developed a Meta-MDP model with actions being individual policies of information gathering based on POMDPs. We compute an optimal exploration policy for each couple of robot and target, and the Meta-MDP model acts as a long-term optimal task allocation algorithm. We experiment our model on a simulation environment and compare to an optimal MPOMDP approach and show promising results on solution quality and scalability.

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Titel: A Meta-MDP Approach for Information Gathering Heterogeneous Multi-agent Systems
verfasst von: Alvin Gandois
Abdel-Illah Mouaddib
Simon Le Gloannec
Ayman Alfalou
Verlag: Springer Nature Switzerland
Buch: Robotics, Computer Vision and Intelligent Systems
Print ISBN: 978-3-031-59056-6

Electronic ISBN: 978-3-031-59057-3

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-59057-3_22

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