Comparing descent heuristics and metaheuristics for the vehicle routing problem
Computers and Operations Research
Genetic Algorithms for Adaptive Motion Planning of an Autonomous Mobile Robot
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Dynamic vehicle routing using genetic algorithms
Applied Intelligence
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
3D Space Path Planning of Complex Environmental Underwater Vehicle
CSO '09 Proceedings of the 2009 International Joint Conference on Computational Sciences and Optimization - Volume 02
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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.