Fuzzy logic and image based autonomous navigation

Fuzzy Logic and Image based Autonomous Navigation
Ana Paula Abrantes de Castro, Leandro Toss Hoffmann, José Demisio Simões da Silva 1 Instituto Nacional de Pesquisas Espaciais – INPE, Laboratório de Computação e Matemática Aplicada – LAC, 12227-970 São José dos Campos, Brasil.
{apaula, hoffmann, demisio}@lac.inpe.br

Abstract. This paper presents a computational model for adaptive autonomous navigation based on visual information from the environment and fuzzy logic control as a continuation of the initial developed in Castro et al [1]. A robot moves on a track environment from which it acquires images with the necessary information to guide itself as to the direction and speed to follow from each track position, based on a fuzzy logic decision system. The experiments were conducted in a controlled environment, but the positions of the robot varied depending upon the initial position on the track. The fuzzy logic system made decision based on the acquired track information related to the left and right strips of the track. An automaton based operator determined high contrast regions where the strips were located, leading to the angular direction of the strips, which consisted on the primary navigation information. Both direction and speed were inferred by the fuzzy logic system. In the paper, the performance of the robot navigation is shown. Keywords: autonomous navigation, fuzzy logic control, computer vision

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Introduction

Autonomous navigation is a well-studied topic in Artificial Intelligence [2][3][4], from which different paradigms try to approach the problem using different sensors to recover environmental information that may lead to a safe and efficient navigation. The autonomous navigation task is related to the ability of a vehicle to move itself without human interaction and reach a goal, in a known or unknown environment. The robot is guided by on-line information during navigation. These tasks require different abilities to reach a point,