Metaheurísticas para geração de alvos para robôs exploratórios autônomos

Abstract

Autonomous exploration, in robotics, can be defined as the act of moving into an unknown environment, at priori, while building up a map of the environment. A great deal of literature describes several problems that are relate to the strategy exploration: perception, location, trajectory control and mapping. This work aims to present an autonomous exploration algorithm based on metaheuristics. Therefore, the problem of autonomous exploration of mobile robots is formulated as an optimization problem, providing data for metaheuristics that are able to search points in the space of solutions that represent positions on the map under construction that best meet the objectives of the exploration. Metaheuristics are approximate methods that guarantee sufficiently good solutions to optimization problems. The proposal was implemented and incorporated as an optimization module in a simultaneous location and mapping system that was run on the Robot Operating System environment and proved to be able to guide a simulated robot without human intervention. Two optimization metaheuristics were implemented to guide target to simulated robot: Genetic Algorithm and Firefly Algorithm. Both algorithms have achieved good results, however the second one was able to guide robot by best trajectories.