Cascaded Evolutionary Estimator for Robot Localization

All processes in the real world are burdened with interference to some extent. The present work shows a method permitting effective interference filtration using sensor data applied for localization possibilities in the known environment using 2D-laser-range-finder. So called cascaded estimator is utilized for filtration mechanism consisted of up to five serially arranged strategies that are able to navigate successfully in useful data. The interference level, at which the estimator devised is able to work, equals up to 100 percent of the original signal. The novelty of the cascaded estimator includes successful evolutionary computations replacing high-performance accelerator with keeping all necessary features of the original algorithms. It is possible to draw up a large quantity of various strategies having specific features. A behavioural analysis of various estimators is performed for verification of features of individual types with application of brute force and classic gradient algorithm. Comparison of efficiency and time requirements is executed utilizing evolutionary methods together with robustness demonstration and reliability of selected types in various kinds of environment. Their advantages, disadvantages, and efficiency are discussed in the course of classification. The number of experiments executed gives wider and mainly practical view on problems of cascaded estimator application for interference filtration and navigation.