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

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