An Enhanced NSGA-II for Multiobjective UAV Path Planning in Urban Environments

Soheila Ghambari, Mahmoud Golabi, Julien Lepagnot, Mathieu Brévilliers, Laëtitia Jourdan, Lhassane Idoumghar

202019 citationsDOI

Abstract

This paper considers multiobjective UAV path planning in a real 3D environment with the objective to find a safe energy-efficient path. An Enhanced Non-dominated Sorting Genetic Algorithm-II, called ENSGA-II, is proposed and combines several sorts of heuristic information to customize crossover and mutation operators. Furthermore, a local search and a ranking-based roulette wheel selection are incorporated for the mating procedure. Experiment results confirm that ENSGA-II has a better convergence rate and spread of solutions on several new real-world datasets. The effectiveness of the local search component is also validated on the CrazyS robot operating system (ROS) package which consists of a pelican quadcopter's modeling.

Topics & Concepts

Fitness proportionate selectionCrossoverGenetic algorithmComputer scienceMotion planningRouletteSortingRanking (information retrieval)Mathematical optimizationHeuristicMulti-objective optimizationComponent (thermodynamics)Local search (optimization)Convergence (economics)Path (computing)RobotArtificial intelligenceMachine learningAlgorithmMathematicsFitness functionEconomicsProgramming languageEconomic growthGeometryThermodynamicsPhysicsRobotic Path Planning AlgorithmsRobotics and Sensor-Based LocalizationUAV Applications and Optimization