Distributed constraint optimization for cooperative autonomous vehicles

After the Second World War, chemical warfare agents and munitions were dumped in the Baltic Sea and the North Sea.
In order to assess the severity of the environmental consequences, it is important that the chemical warfare agents are located and their condition is investigated as soon as possible.
In order to reduce this time, the search could be performed by Autonomous Underwater Vehicles (AUVs).
The goal of this thesis is to develop algorithms that can be applied during underwater operations to allow AUVs to optimize their actions based on a global objective function without centralized communications.

The search problem is modeled within the Distributed Constraint Optimization Problem (DCOP) framework to be able to explicitly define both computational agents and their communications.
In order to be applicable to AUV operations, both benchmark problems and real-world problems with continuous domains are modelled within the Continuous DCOP (C-DCOP) framework.
This preserves the flexibility of modeling inherent in a DCOP while removing the limitations imposed by the discrete definitions.

Two C-DCOP algorithms are presented in this thesis.
The Compression-DPOP (C-DPOP) algorithm discretizes the domain of each of the variables and compresses their domains in order to refine the search space at every iteration.
The Distributed Bayesian (D-Bay) algorithm leverages Bayesian optimization to solve C-DCOPs without any need for discretization by modelling the effects of the variables on the global utility as Gaussian processes.

Results from high-fidelity simulations and real-world experiments are given for real-world multi-agent search problems.
A mine countermeasures operation is simulated in which AUVs update their search areas during the search based on sonar performance.
Assigned areas are re-distributed in order to optimize metrics relating to the expected time of completion and the level of confidence that all mine-like objects have been detected.
Moreover, real-world experimental results are presented for a multi Unmanned Aerial Vehicle (UAV) search problem.

By improving the autonomy of AUVs, the search efficiency can be increased through the cooperative optimization of their actions during the operation.
The research in this thesis contributes to this strategy by means of the developed algorithms and their applicability to real-world problems.