Placement of electromagnetic signal emitting devices, such as light sources, has important usage in for signal coverage tasks. Automatic placement of these devices is challenging because of the complex interaction of the signal and environment due to reflection, refraction and scattering. In this work, we iteratively improve the placement of these devices by interleaving device placement and sensing actions, correcting errors in the model of the signal propagation. To this end, we propose a novel factor-graph based belief model which combines the measurements taken by the robot and an analytical light propagation model. This model allows accurately modelling the uncertainty of the light propagation with respect to the obstacles, which greatly improves the informative path planning routine. Additionally, we propose a method for determining when to re-plan the emitter placements to balance a trade-off between information about a specific configuration and frequent updating of the configuration. This method incorporates the uncertainty from belief model to adaptively determine when re-configuration is needed. We find that our system has a 9.8% median error reduction compared to a baseline system in simulations in the most difficult environment. We also run on-robot tests and determine that our system performs favorably compared to the baseline.

翻译：电磁信号发射设备（如光源）的部署在信号覆盖任务中具有重要应用。由于信号与环境的复杂交互作用（包括反射、折射和散射），自动部署这些设备极具挑战性。本研究通过交替执行设备部署与感知动作，迭代优化设备布局，同时修正信号传播模型中的误差。为此，我们提出了一种基于因子图的置信度模型，该模型融合机器人采集的测量数据与分析式光传播模型。该模型能够精确建模障碍物对光传播的不确定性，从而显著提升信息路径规划的效果。此外，我们提出了一种确定何时重新规划发射器部署的方法，以平衡对特定配置的信息获取与配置频繁更新之间的权衡。该方法通过整合置信度模型中的不确定性，自适应判断需要重新配置的时机。在最具挑战性的仿真环境下，我们的系统相较于基线系统实现了9.8%的中位误差降低。我们还进行了机器人实测验证，结果表明本系统性能优于基线方案。