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

Abstract

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 APSO 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 APSO outperformes the PSO algorithm. With respect to the algorithm A, the APSO achieved better solutions for 40% of the tested instances, but all of them, with less waypoints. For non-holonomic instances, the APSO obtained longer paths, however smoother and safer.