GLOBAL ROBOT LOCALIZATION UNDER NOISE STRESS UTILIZING EA METHODS AND SEMISEMANTIC CLASSIFICATION OF A KNOWN ENVIRONMENT

摘要

Global localization algorithms belong to the key research areas in the field of autonomous mobile robotics. The ability to correctly estimate the initial position after activation or to recover the global position if orientation is lost is required from all modern autonomous systems. This article presents an algorithm for unmanned global navigation in a known environment containing noise and moving objects. Evolutionary algorithms (EA) form an important part of the discussed method. We also present a novel method of semisemantic classification of the environment in which a robot moves. This semisemantic description of the environment allows for a significantly better setup of the working parameters of individual EAs. It also enables to better connect EAs with the basic navigation methodology based on algebraic criteria, in other words, on the minimization of L1-norm. An extensive set of experimental results confirms that the connection of the semantic environment description and the navigation methods creates an important advantage.