  In this paper, we study a navigation problem where a mobile robot needs to
locate a mmWave wireless signal. Using the directionality properties of the
signal, we propose an estimation and path planning algorithm that can
efficiently navigate in cluttered indoor environments. We formulate Extended
Kalman filters for emitter location estimation in cases where the signal is
received in line-of-sight or after reflections. We then propose to plan motion
trajectories based on belief-space dynamics in order to minimize the
uncertainty of the position estimates. The associated non-linear optimization
problem is solved by a state-of-the-art constrained iLQR solver. In particular,
we propose a method that can handle a large number of obstacles (~300) with
reasonable computation times. We validate the approach in an extensive set of
simulations. We show that our estimators can help increase navigation success
rate and that planning to reduce estimation uncertainty can improve the overall
task completion speed.
