  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.
