Training Method Improvements of a WiFi Navigation System Based on POMDP

The framework of this paper is the robotics navigation inside buildings using WiFi signal strength measure. This navigation is achieved using a partially observable Markov decision process (POMDP). In the localization phase we used WiFi signal strength and ultrasound measures as observations. The localization system works in two stages: map construction and localization stage. The map construction stage usually requires a great effort, therefore in this paper we address the problem of minimizing this calibration effort using an automatic training method. We describe the method based on simultaneous localization and mapping (SLAM) techniques and in a robust local navigation task. This automatic method is compared with a manual method to obtain a deterministic map. Also we demonstrate that using this one in a on-line training stage the system is able to adapt the WiFi map to the variations of the WiFi signal measure. Additionally, we analyze the optimal parameters for this automatic training system. The system has been tested in a real environment using two commercial robotic platforms. Some experimental results and the conclusions are presented