  We discuss the localization of radiation sources whose number and other
relevant parameters are not known in advance. The data collection is ensured by
an autonomous mobile robot that performs a survey in a defined region of
interest populated with static obstacles. The measurement trajectory is
information-driven rather than pre-planned, and the localization exploits a
regularized particle filter estimating the sources' parameters continuously.
Regarding the dynamic robot control, this switches between two modes, one
attempting to minimize the Shannon entropy and the other aiming to reduce the
variance of expected measurements in unexplored parts of the target area; both
of the modes maintain safe clearance from the obstacles. The performance of the
algorithms was tested in a simulation study based on real-world data acquired
previously from three radiation sources exhibiting various activities. Our
approach reduces the time necessary to explore the region and to find the
sources by approximately 40~\%; at present, however, the method is unable to
reliably localize sources that have a relatively low intensity. In this
context, additional research has been planned to increase the credibility and
robustness of the procedure and to improve the robotic platform autonomy.
