The article discusses 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. The localization exploits a regularized particle filter estimating the sources' parameters continuously. The dynamic robot control 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.

翻译：本文讨论了在辐射源数量及其他相关参数未知的情况下对其定位的问题。数据采集由一台自主移动机器人完成，该机器人在预设的静态障碍物区域内执行探测任务。测量轨迹基于信息驱动而非预先规划。定位采用正则化粒子滤波器连续估计辐射源参数。动态机器人控制在两种模式间切换：一种模式致力于最小化香农熵，另一种模式旨在降低目标区域未探测部分预期测量的方差；两种模式均与障碍物保持安全距离。基于先前从三个具有不同活度的辐射源获取的真实数据进行的仿真研究测试了算法性能。我们的方法将探索区域及寻找辐射源所需的时间减少了约40%；然而，目前该方法无法可靠定位强度相对较低的辐射源。在此背景下，已计划开展进一步研究，以提升该过程的可靠性与鲁棒性，并增强机器人平台的自主性。