Navigation learning system for mobile robot in heterogeneous environment: Inductive modeling approach

Abstract

One of the key tasks of mobile robotics is navigation, which for Outdoor-type robots is exacerbated by the functioning in a priori of an unknown environment. In this paper, for the first time, the learning navigation system for mobile robot based on inductive modeling approach is presented. This approach is based on the principles of the group method of data handling (GMDH), which is one of the first techniques of Deep Learning. The paper presents the results of training models for estimating the robot's coordinates and angular orientation in heterogeneous environment. In addition to the direct readings of the on-board sensors, additional parameters were introduced to train the models, reflecting how the robot perceives the surface terramechanics. The models for estimation of the coordinates on the surface areas of various types and classifiers of the surface type were trained. The obtained results testify the efficiency of the developed Navigation Leaning System for Mobile Robot (NLS MR).