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.

翻译：本文探讨了对数量及其他相关参数未知的辐射源进行定位的问题。数据采集由一台自主移动机器人在预设的、布满静态障碍物的感兴趣区域中执行巡测实现。测量轨迹采用信息驱动而非预规划方式，定位过程中利用正则化粒子滤波器连续估计辐射源参数。在动态机器人控制方面，该方法在两种模式间切换：一种尝试最小化香农熵，另一种旨在降低目标区域未探索部分预期测量的方差；两种模式均保持与障碍物的安全距离。该算法性能基于先前从三个不同活度辐射源获取的真实数据进行了仿真实验验证。本方法将探索区域及寻找辐射源所需时间缩短约40%；但目前该方法无法可靠定位强度相对较低的辐射源。针对此问题，已计划开展进一步研究以提升该流程的可靠性与鲁棒性，并增强机器人平台的自主性。