Authors:
Enrico Sutera
1
;
2
;
Vittorio Mazzia
1
;
2
;
3
;
Francesco Salvetti
1
;
2
;
3
;
Giovanni Fantin
1
;
2
and
Marcello Chiaberge
1
;
2
Affiliations:
1
Department of Electronics and Telecommunications, Politecnico di Torino, 10124 Turin, Italy
;
2
PIC4SeR, Politecnico di Torino Interdepartmental Centre for Service Robotics, Turin, Italy
;
3
SmartData@PoliTo, Big Data and Data Science Laboratory, Turin, Italy
Keyword(s):
Indoor Autonomous Navigation, Autonomous Agents, Deep Reinforcement Learning, Ultra-Wideband.
Abstract:
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-hu
ndred 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.
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