Um algoritmo PSO híbrido para planejamento de caminhos em navegação de veículos utilizando A*

• Ano de defesa: 2014
• Autor(a) principal: Gasperazzo, Stéfano Terci
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Espírito Santo; BR; Mestrado em Informática; Centro Tecnológico; UFES; Programa de Pós-Graduação em Informática
• Palavras-chave em Português: Autonomous Vehicles; path planning; Enxame de partículas – PSO (Particle Swarm Optimization); Veículos autônomos; A*; planejamento de caminhos; PSO; Algoritmos; Navegação de robôs móveis; Ciência da Computação; 004
• Link de acesso: http://repositorio.ufes.br/handle/10/1466

Autonomous robots with the ability of planning their own way is a challenge that attracts many researchers in the area of robot navigation. In this context, this work aims to implement a hybrid PSO algorithm for planning paths in static environments for holonomic and non-holonomic vehicles. The proposed algorithm has two phases: the first uses A* algorithm to generates an initial and feasible trajectory which is optimized by the PSO algorithm in the second stage. Finally a post path planning phase can be applied in order to adapt it to non-holonomic vehicle kinematic constraints. The Ackerman model has been considered for the experiments. The Carnegie Mellon Robot Navigation Toolkit (CARMEN) was used to perform the computational experiments considering five instances of maps artificially generated with obstacles. The performance of the A*PSO algorithm was compared with A*, PSO and A*-Hybrid State. The results of the dynamic instances were not compared with other algorithms. The computational results indicates that the algorithm A*PSO outperformes the PSO algorithm. With respect to the algorithm A*, the A*PSO achieved better solutions for 40% of the tested instances, but all of them, with less waypoints. For non-holonomic instances, the A*PSO obtained longer paths, however smoother and safer.