Minehunting Mission Planning for Autonomous Underwater Systems Using Evolutionary Algorithms

• Tipo: Artigo em Revista Científica Internacional
• Data: 2014

Autores

Abreu, N (Autor) Outra; Aníbal Castilho Coimbra de Matos (Autor) FEUP

Revista

Título: Unmanned Systems

Vol. 2

Páginas: 323-349

ISSN: 2301-3850

Editora: World Scientific

Indexação

Scopus - 11 Citações

Outras Informações

ID Authenticus: P-00N-4E9

DOI: 10.1142/s2301385014400081

Abstract (EN): Autonomous underwater vehicles (AUVs) are increasingly being used to perform mine countermeasures (MCM) operations but its capabilities are limited by the efficiency of the planning process. Here we study the problem of multiobjective MCM mission planning with AUVs. The vehicle should cover the operating area while maximizing the probability of detecting the targets and minimizing the required energy and time to complete the mission. A multi-stage algorithm is proposed and evaluated. Our algorithm combines an evolutionary algorithm (EA) with a local search procedure, aiming at a more flexible and effective exploration and exploitation of the search space. An artificial neural network (ANN) model was also integrated in the evolutionary procedure to guide the search. The combination of different techniques creates another problem, related to the high amount of parameters that needs to be tuned. Thus, the effect of these parameters on the quality of the obtained Pareto Front was assessed. This allowed us to define an adaptive tuning procedure to control the parameters while the algorithm is executed. Our algorithm is compared against an implementation of a known EA as well as another mission planner and the results from the experiments show that the proposed strategy can efficiently identify a higher quality solution set. © 2014 World Scientific Publishing Company.

Idioma: Inglês

Tipo (Avaliação Docente): Científica