Mobile Robot Indoor Localization Using Artificial Neural Networks and Wireless Networks

Accurate position information of an agent (i.e. robot, animal, or people) is a requirement to accomplish several tasks. Some sensors like GPS provide global position estimation but it is restricted to outdoor environments and has an inherent imprecision of a few meters. In indoor spaces, other sensors like lasers and cameras can be used for position estimation, but they require landmarks (or maps) in the environment and a fair amount of computation to process complex algorithms. These sensors also have a limited field of view, which makes the localization task harder. In the case of video cameras, the variation of light is also a serious issue. Nowadays Wireless Networks (WN) are widely available in indoor environments and allow efficient global localization demanding relatively low computing resources. Other advantages of this approach are scalability, robustness, and independence of specific features of the environment. However, the inherent instability in the wireless signal does not allow its direct use for very accurate position estimation. In this paper we evaluate the use of an Artificial Neural Network (ANN) to improve the estimation of the position of a mobile node in indoor environment using data provided by wireless networks. Our approach uses the ANN capabilities of learning and generalization to reduce the effect of the unstable data, increasing the accuracy of the agent’s position. In order to validate our approach several ANN topologies have been evaluated in experimental tests performed with a mobile node in an indoor space.