CBA2018-1081 Robótica

Path Planning of Mobile Robot with Growing Neural Gas and Ant Colony Optimization

Thiago Azevedo¹; Claudio César¹; Allan C. Gomes¹; Marcus Davi¹; Thiago A. Lima¹; Vinícius R. A. Ferreira¹; Wilkley B. Correia¹; Arthur P. S. Braga¹

¹ Universidade Federal do Ceará

Baixar PDF doi:10.20906/CPS/CBA2018-1081

Resumo

This paper presents a new path planning strategy obtained from the combination between Growing Neural Gas (GNG) and Ant Colony Optimization (ACO) algorithms. The proposed strategy was tested in a real mobile robot for two different scenarios and compared with the APF approach. Positive and negative points of the proposed strategy are highlighted throughout the text. As one positive point, the proposed path planning strategy presented a better end positioning of the robot in comparison with APF in the performed tests - an aspect of great interest for practical applications in industry and medical area.

Palavras-chave: Self-Organizing Maps; Metaheuristics; Artificial Intelligence; Path Planning; Mobile Robot

Como citar

Thiago Azevedo; Claudio César; Allan C. Gomes; Marcus Davi; Thiago A. Lima; Vinícius R. A. Ferreira; Wilkley B. Correia; Arthur P. S. Braga. “Path Planning of Mobile Robot with Growing Neural Gas and Ant Colony Optimization”. XXII Congresso Brasileiro de Automática. CBA2018. 2018. DOI: 10.20906/CPS/CBA2018-1081