Autonomous mobile robot global self-localization using Kohonen and region-feature neural networks

This article presents and compares two neural network-based approaches to global self-localization (GSL) for autonomous mobile robots using: (1) a Kohonen neural network, and (2) a region-feature neural network (RFNN). Both approaches categorize discrete regions of space (topographical nodes) in a manner similar to optical character recognition (OCR). That is, the mapped sonar data assumes the form of a character unique to that region. Hence, it is believed that an autonomous vehicle can determine which room it is in from sensory data gathered from exploration. With a robust exploration routine, the GSL solution can be time-, translation-, and rotation-invariant. The GSL solution can also become independent of the mobile robot used to collect the sensor data. This suggests that a single robot can transfer its knowledge of various learned regions to other mobile robots. The classification rate of both approaches are comparable and, thus, worthy of presentation. The observed pros and cons of both approaches are also discussed. © 1997 John Wiley & Sons, Inc.