Indoor autonomous navigation requires a precise and accurate localizationsystem 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 moving obstacles 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 guidancesystem complete solution . We tested the learnt point- to-point navigation policies in a real setting with more thantwo-hundred experimental evaluations using UWB

Author(s) : Enrico Sutera, Vittorio Mazzia, Francesco Salvetti, Giovanni Fantin, Marcello Chiaberge

Links : PDF - Abstract

Code :

https://github.com/google/dopamine




Keywords : point - navigation - indoor - uwb - obstacles -

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