Two methods for autonomous robot obstacle sensing and application programming interface for Fuzzy Rule Interpolation

Abstract

Environment detection is important task for an autonomous robot. Obstacle avoidance is a must when the robot do indoor activity. I this case robot movement is done in corridors and rooms, which is similar to a maze. Distance from the walls can be sensed with different sensors, in the experiment infra-red sensors were used. There are several methods for obstacle detection and calculating the distance from it. In the experiment Fuzzy Rule Interpolation (FRI) and Bayes classifier were used. On the other side a programmer friendly API was created for relive the Declarative Description Language used for different type of hardware. Using the FRI method to obstacle detection a rulebase was defined for each wall position (front, left, right). The Bayes classifier makes use of a big amount of data, which are collected from the sensors. The collected data are clustered for noise reduction and the amount of data is reduced. The method gave 7 classes according to possible wall positions, which appear in the maze around the robot. Both methods were tested on a robot. The results were compared and described in this paper.