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

Enrico Sutera, Vittorio Mazzia, Francesco Salvetti, Giovanni Fantin, Marcello Chiaberge

2021

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.

Paper Citation

in Harvard Style

Sutera E., Mazzia V., Salvetti F., Fantin G. and Chiaberge M. (2021). Indoor Point-to-Point Navigation with Deep Reinforcement Learning and Ultra-Wideband.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-484-8, pages 38-47. DOI: 10.5220/0010202600380047

in Bibtex Style

@conference{icaart21,
author={Enrico Sutera and Vittorio Mazzia and Francesco Salvetti and Giovanni Fantin and Marcello Chiaberge},
title={Indoor Point-to-Point Navigation with Deep Reinforcement Learning and Ultra-Wideband},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2021},
pages={38-47},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010202600380047},
isbn={978-989-758-484-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Indoor Point-to-Point Navigation with Deep Reinforcement Learning and Ultra-Wideband
SN - 978-989-758-484-8
AU - Sutera E.
AU - Mazzia V.
AU - Salvetti F.
AU - Fantin G.
AU - Chiaberge M.
PY - 2021
SP - 38
EP - 47
DO - 10.5220/0010202600380047