Global self-localization for autonomous mobile robots using self-organizing Kohonen neural networks

Abstract

An approach to global self-localization for autonomous mobile robots has been developed using self-organizing Kohonen neural networks. This approach categorizes discrete regions of space using mapped sonar data corrupted by noise of varied sources and ranges. Our approach is similar to optical character recognition (OCR) in that the mapped sonar data can, over time, assume the form of a character unique to that room. Hence, it is believed that an autonomous vehicle can be capable of determining which room it is in based on mapped sensory data ascertained by wandering through and exploring that room. With some pre-processing and a robust explore routine, the solution becomes time-, translation- and rotation-invariant.