A multi-objective genetic algorithm applied to autonomous underwater vehicles for sewage outfall plume dispersion observations

This work presents a multi-objective genetic algorithm to solve route planning problem for multiple autonomous underwater vehicles (AUVs) for interdisciplinary coastal research. AUVs are mobile unmanned platforms that carry their own energy and are able to move themselves in the water without intervention from an external operator. Using AUVs one can provide high-quality measurements of physical properties of effluent plumes in a very effective manner under real oceanic conditions. The AUV's route planning problem is a combinatorial optimization problem, where the vehicles must travel through a three-dimensional irregular space with all dimensions known. Therefore, minimization of the total travel distance while considering the maximum number of water samples is the main objective. Besides the AUV kinematics restrictions other considerations must be taken into account to the problem, like the ocean currents. The practical applications of this approach are the environmental monitoring missions which typically require the sampling of a volume of water with non-trivial geometry for which parallel line sweeping might be a costly solution. Some real-life test problems and related solutions are presented.