PATH PLANNING OF MOBILE ROBOT WITH GROWING NEURAL GAS AND ANT COLONY OPTIMIZATION

THIAGO AZEVEDO; CLAUDIO CÉSAR; ALLAN  C. GOMES; MARCUS  DAVI; THIAGO A. LIMA; VINÍCIUS R. A. FERREIRA; WILKLEY  B. CORREIA; ARTHUR  P. S. BRAGA

doi:10.20906/CBA2022/506

THIAGO AZEVEDO Universidade Federal do Ceará
CLAUDIO CÉSAR Universidade Federal do Ceará
ALLAN C. GOMES Universidade Federal do Ceará
MARCUS DAVI Universidade Federal do Ceará
THIAGO A. LIMA Universidade Federal do Ceará
VINÍCIUS R. A. FERREIRA Universidade Federal do Ceará
WILKLEY B. CORREIA Universidade Federal do Ceará
ARTHUR P. S. BRAGA Universidade Federal do Ceará

DOI: https://doi.org/10.20906/CBA2022/506

Keywords: Self-Organizing Maps, Metaheuristics, Articial Intelligence, Path Planning, Mobile Robot

Abstract

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 diferent 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.