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.
Bibtex entry:
@inproceedings{FraSij:20-020,