UAS Mission Path Planning System, (MPPS) using hybrid-game coupled to multi-objective optimizer


Autoria(s): Lee, Dong-Seop; Gonzalez, Luis Felipe; Periaux, Jacques
Data(s)

01/07/2010

Resumo

This paper presents the application of advanced optimization techniques to unmanned aerial system mission path planning system (MPPS) using multi-objective evolutionary algorithms (MOEAs). Two types of multi-objective optimizers are compared; the MOEA nondominated sorting genetic algorithm II and a hybrid-game strategy are implemented to produce a set of optimal collision-free trajectories in a three-dimensional environment. The resulting trajectories on a three-dimensional terrain are collision-free and are represented by using Bézier spline curves from start position to target and then target to start position or different positions with altitude constraints. The efficiency of the two optimization methods is compared in terms of computational cost and design quality. Numerical results show the benefits of adding a hybrid-game strategy to a MOEA and for a MPPS.

Formato

application/pdf

Identificador

http://eprints.qut.edu.au/34099/

Publicador

ASME - American Society Mechanical Engineering

Relação

http://eprints.qut.edu.au/34099/3/34099.pdf

DOI:10.1115/1.4001336

Lee, Dong-Seop , Gonzalez, Luis Felipe, & Periaux, Jacques (2010) UAS Mission Path Planning System, (MPPS) using hybrid-game coupled to multi-objective optimizer. Journal of Dynamic Systems, Measurement, and Control, 132(4).

Direitos

Copyright 2010 ASME - American Society Mechanical Engineering

Fonte

Australian Research Centre for Aerospace Automation; Faculty of Built Environment and Engineering

Palavras-Chave #010301 Numerical Analysis #010303 Optimisation #090104 Aircraft Performance and Flight Control Systems #Mission Path Planning System #Hybrid-Game #Optimisation #UAS
Tipo

Journal Article