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Mixed Observable RRT: Multi-Agent Mission-Planning in Partially Observable Environments

Johansson, Kasper and Rosolia, Ugo and Ubellacker, Wyatt and Singletary, Andrew and Ames, Aaron D. (2021) Mixed Observable RRT: Multi-Agent Mission-Planning in Partially Observable Environments. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20220224-200818977

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Abstract

This paper considers centralized mission-planning for a heterogeneous multi-agent system with the aim of locating a hidden target. We propose a mixed observable setting, consisting of a fully observable state-space and a partially observable environment, using a hidden Markov model. First, we construct rapidly exploring random trees (RRTs) to introduce the mixed observable RRT for finding plausible mission plans giving way-points for each agent. Leveraging this construction, we present a path-selection strategy based on a dynamic programming approach, which accounts for the uncertainty from partial observations and minimizes the expected cost. Finally, we combine the high-level plan with model predictive controllers to evaluate the approach on an experimental setup consisting of a quadruped robot and a drone. It is shown that agents are able to make intelligent decisions to explore the area efficiently and to locate the target through collaborative actions.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2110.01002arXivDiscussion Paper
ORCID:
AuthorORCID
Rosolia, Ugo0000-0002-1682-0551
Ubellacker, Wyatt0000-0002-4732-6185
Singletary, Andrew0000-0001-6635-4256
Ames, Aaron D.0000-0003-0848-3177
Record Number:CaltechAUTHORS:20220224-200818977
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220224-200818977
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:113582
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:28 Feb 2022 16:59
Last Modified:28 Feb 2022 16:59

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