oaioai:CiteSeerX.psu:10.1.1.56.566

Efficient Learning of Variable-Resolution Cognitive Maps for Autonomous Indoor Navigation

Abstract

. This paper presents an adaptive method that allows mobile robots to learn cognitive maps of indoor environments incrementally and on-line. Our approach models the environment by means of a variable-resolution partitioning that discretizes the world in perceptually homogeneous regions. The resulting model incorporates both a compact geometrical representation of the environment and a topological map of the spatial relationships between its obstaclesfree areas. The efficiency of the learning process is based on the use of local memory-based techniques for partitioning and of active learning techniques for selecting the most appropriate region to be explored next. In addition, a feed-forward neural network interprets sensor readings efficiently. Finally, we present experimental results obtained with two extremely different mobile robots, namely a Nomad 200 and a Khepera, which confirm the validity and generality of our approach. keywords: map learning, variable-resolution partitioning,..

Similar works

Full text

thumbnail-image
oaioai:CiteSeerX.psu:10.1.1.56.566Last time updated on 10/22/2014

This paper was published in CiteSeerX.

Having an issue?

Is data on this page outdated, violates copyrights or anything else? Report the problem now and we will take corresponding actions after reviewing your request.