  Indoor autonomous navigation requires a precise and accurate localization
system able to guide robots through cluttered, unstructured and dynamic
environments. Ultra-wideband (UWB) technology, as an indoor positioning system,
offers precise localization and tracking, but moving obstacles and
non-line-of-sight occurrences can generate noisy and unreliable signals. That,
combined with sensors noise, unmodeled dynamics and environment changes can
result in a failure of the guidance algorithm of the robot. We demonstrate how
a power-efficient and low computational cost point-to-point local planner,
learnt with deep reinforcement learning (RL), combined with UWB localization
technology can constitute a robust and resilient to noise short-range guidance
system complete solution. We trained the RL agent on a simulated environment
that encapsulates the robot dynamics and task constraints and then, we tested
the learnt point-to-point navigation policies in a real setting with more than
two-hundred experimental evaluations using UWB localization. Our results show
that the computational efficient end-to-end policy learnt in plain simulation,
that directly maps low-range sensors signals to robot controls, deployed in
combination with ultra-wideband noisy localization in a real environment, can
provide a robust, scalable and at-the-edge low-cost navigation system solution.
