This paper proposes a navigation scheme for robots in indoor environments which uses real-time analysis of raw data captured by an infra-red sensor. The infra-red sensor captures distance data in real time producing a large database that the robot analyzes according to previous experiences to directly define its movement strategy. Observing the dependence of data with the topology of the environment, this research proposes models based on a Long Short-Term Memory (LSTM) network. We demonstrate not only that the navigation scheme works successfully on a real prototype, but also that the strategy can be used in new unknown navigation environments with a high success rate.