A comparative study: the effect of the perturbation vector type in the differential evolution algorithm on the accuracy of robot pose and heading estimation

Abstract

Evolutionary algorithms (EAs) belong to a group of classic optimizers these days, and can be used in many application areas. Autonomous mobile robotics is not an exception. EAs are utilized profusely for the purposes of localization and map building of unknown environment—SLAM. This paper concentrates on one particular class of EA, the so called differential evolution (DE). It addresses the problem of selecting a suitable set of parameter values for the DE algorithm applied to the task of continuous robot localization in a known environment under the presence of additive noise in sensorial data. The primary goal of this study is to find at least one type of perturbation vector from a set of known perturbation vector types, suitable to navigate a robot using 2D laser scanner (2DLS) sensorial system. The basic navigational algorithm used in this study uses a vector representation for both the data and the environment map, which is used as a reference data source for the navigation. Since the algorithm does not use a probability occupancy grid, the precision of the results is not limited by the grid resolution. The comparative study presented in this paper includes a relatively large amount of experiments in various types of environments. The results of the study suggest that the DE algorithm is a suitable tool for continuous robot localization task in an indoor environment, with or without moving objects, and under the presence of various levels of additive noise in sensorial data. Two perturbation vector types were found as the most suitable for this task on average, namely rand/1/exp and randtobest/1/bin.