The Distributed Bayesian Algorithm: Simulation and Experimental Results for a Cooperative Multi UAV Search Use-Case

Reference

J. Fransman, J. Sijs, H. Dol, E. Theunissen, and B. De Schutter, "The Distributed Bayesian Algorithm: Simulation and Experimental Results for a Cooperative Multi UAV Search Use-Case," Proceedings of the 11th International Workshop and Optimization and Learning in Multiagent Systems (OptLearnMAS 2020), Virtual conference, May 2020.

Abstract

In this work, the Distributed Bayesian (D-Bay) algorithm is applied to an autonomous search use case. Within the use case multiple unmanned aerial vehicles equipped with cameras cooperatively search an area and minimize the required time. The use case is modeled within the continuous Distributed Constraint Optimization Problem (DCOP) framework. This framework extends the (discrete) DCOP framework by allowing variables with continuous domains. Compared to similar DCOP solvers, the characteristics of the D-Bay algorithm are well suited for the use case and allow for the implementation on autonomous vehicles with limited resources (computational power, memory, and communication bandwidth). Experimental results are given and these results are used to validate a simulation environment. Within the simulation environment various scenarios are implemented. The D-Bay algorithm was able to find solutions within 3.5% of the optimal solution with a limited amount of samples per agent.

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Bibtex entry

@inproceedings{FraSij:20-020,
author={J. Fransman and J. Sijs and H. Dol and E. Theunissen and B. {D}e Schutter},
title={The Distributed {Bayesian} Algorithm: {Simulation} and Experimental Results for a Cooperative Multi {UAV} Search Use-Case},
booktitle={Proceedings of the 11th International Workshop and Optimization and Learning in Multiagent Systems (OptLearnMAS 2020)},
address={Virtual conference},
month=may,
year={2020},
doi={-}
}

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