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
@inproceedings{FraSij:20-020,
author = {Fransman, Jeroen and Sijs, Joris and Dol, Henry and Theunissen,
Erik and De Schutter, Bart},
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}
}