In active source seeking, a robot takes repeated measurements in order to locate a signal source in a cluttered and unknown environment. A key component of an active source seeking robot planner is a model that can produce estimates of the signal at unknown locations with uncertainty quantification. This model allows the robot to plan for future measurements in the environment. Traditionally, this model has been in the form of a Gaussian process, which has difficulty scaling and cannot represent obstacles. %In this work, We propose a global and local factor graph model for active source seeking, which allows the model to scale to a large number of measurements and represent unknown obstacles in the environment. We combine this model with extensions to a highly scalable planner to form a system for large-scale active source seeking. We demonstrate that our approach outperforms baseline methods in both simulated and real robot experiments.

翻译：在主动源搜索中，机器人通过重复测量在杂乱且未知的环境中定位信号源。主动源搜索机器人规划器的核心组件是一个能够估计未知位置信号并量化其不确定性的模型。该模型使机器人能够规划未来环境中的测量。传统上，这一模型采用高斯过程形式，但存在扩展困难且无法表征障碍物的问题。本研究提出一种用于主动源搜索的全局-局部因子图模型，该模型可扩展至大量测量数据，并能表征环境中的未知障碍物。我们将该模型与高度可扩展规划器的扩展方案相结合，构建了一套面向大规模主动源搜索的系统。在仿真与真实机器人实验中均证明，我们的方法性能优于基线方法。