Using the Kohonen topology preserving mapping network for learning the minimal environment representation

The authors present the application of the Kohonen self-organizing topology-preserving neural network for learning and developing a minimal representation for the open environment in mobile robot navigation. The input to the algorithm consists of the coordinates of randomly selected points in the open environment. No specific knowledge of the size, number, and shape of the obstacles is needed by the network. The parameter selection for the network is discussed. The neighborhood function, adaptation gain, and the number of training sample points have direct effect on the convergence and usefulness of the final representation. The environment dimensions and a measure of environment complexity are used to find approximate bounds and requirements on these parameters.<>