Neural-Network for Position Estimation of a Cable-Suspended Payload Using Inertial Quadrotor Sensing

Julien Mellet, Jonathan Cacace, Fabio Ruggiero, Vincenzo Lippiello

2023

Abstract

This paper considers a standard quadrotor drone with a cable-suspended payload and minimal sensor configuration. A neural network estimator is proposed to perform accurate real-time payload position estimation. A novel proprioceptive feedback measurement method is proposed, and a neural network has been trained with domain randomization. The network shows accurate zero-shot estimation, even with excitations never seen by the system before. This preliminary work has been tested in a simulated environment and aims to show that only onboard inertial sensing is enough to achieve the sought task. The presented work may open new applications for drone transportation in real environments subject to several perturbations.

Paper Citation

in Harvard Style

Mellet J., Cacace J., Ruggiero F. and Lippiello V. (2023). Neural-Network for Position Estimation of a Cable-Suspended Payload Using Inertial Quadrotor Sensing. In Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-670-5, SciTePress, pages 80-87. DOI: 10.5220/0012204100003543

in Bibtex Style

@conference{icinco23,
author={Julien Mellet and Jonathan Cacace and Fabio Ruggiero and Vincenzo Lippiello},
title={Neural-Network for Position Estimation of a Cable-Suspended Payload Using Inertial Quadrotor Sensing},
booktitle={Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2023},
pages={80-87},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012204100003543},
isbn={978-989-758-670-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Neural-Network for Position Estimation of a Cable-Suspended Payload Using Inertial Quadrotor Sensing
SN - 978-989-758-670-5
AU - Mellet J.
AU - Cacace J.
AU - Ruggiero F.
AU - Lippiello V.
PY - 2023
SP - 80
EP - 87
DO - 10.5220/0012204100003543
PB - SciTePress