Evolutionary Computation for Solving Path Planning of an Autonomous Surface Vehicle Using Eulerian Graphs

Abstract

An evolutionary-based path planning is designed for an Autonomous Surface Vehicle (ASV) used in environmental monitoring tasks. The main objective is that the ASV covers the maximum area of a mass of water like the Ypacarai Lake while taking water samples for sensing pollution conditions. Such coverage problem is transformed into a path planning optimization problem through the placement of a set of data beacons located at the shore of the lake and considering the relationship between the distance travelled by the ASV and the area of the lake covered. The optimal set of beacons to be visited by the ASV has been modeled through Eulerian circuits. Due to the complexity of the optimization problem, a metaheuristic technique like a Genetic Algorithm (GA) is used to obtain quasi-optimal solutions in both models. The parameters of the GA are tuned and then the obtained Eulerian Circuit is compared with a lawnmower and a random approaches obtaining an improvement of up to the double of the lake.