Evolving an Indoor Robotic Localization System Based on Wireless Networks.

This work addresses the evolution of an Artificial Neural Network (ANN) to assist in the problem of indoor robotic localization. We investigate the design and building of an autonomous localization system based on information gathered from Wireless Networks (WN). The paper focuses on the evolved ANN which provides the position of one robot in a space, as in a Cartesian plane, corroborating with the Evolutionary Robotic research area and showing its practical viability. The proposed system was tested on several experiments, evaluating not only the impact of different evolutionary computation parameters but also the role of the transfer functions on the evolution of the ANN. Results show that slight variations in the parameters lead to huge differences on the evolution process and therefore in the accuracy of the robot position.