Indoor Point-to-Point Navigation with Deep Reinforcement Learning and Ultra-wideband

• URL: http://arxiv.org/abs/2011.09241v1
• Date: Wed, 18 Nov 2020 12:30:36 GMT
• Title: Indoor Point-to-Point Navigation with Deep Reinforcement Learning and Ultra-wideband
• Authors: Enrico Sutera, Vittorio Mazzia, Francesco Salvetti, Giovanni Fantin and Marcello Chiaberge
• Abstract summary: Moving obstacles and non-line-of-sight occurrences can generate noisy and unreliable signals. We show how a power-efficient point-to-point local planner, learnt with deep reinforcement learning (RL), can constitute a robust and resilient to noise short-range guidance system complete solution. Our results show that the computational efficient end-to-end policy learnt in plain simulation, can provide a robust, scalable and at-the-edge low-cost navigation system solution.
• Score: 1.6799377888527687
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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-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.

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