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Autonomous mobile robot global self-localization using Kohonen and region-feature neural networks. (English) Zbl 0876.70006

We present and compare 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. Both approaches categorize discrete regions of space (topographical nodes) in a manner similar to optical character recognition. 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.

MSC:

70B15 Kinematics of mechanisms and robots
92B20 Neural networks for/in biological studies, artificial life and related topics
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