Robot Collision Avoidance based on Artificial Potential Field with Local Attractors

Matteo Melchiorre, Leonardo Scimmi, Laura Salamina, Stefano Mauro, Stefano Pastorelli

2022

Abstract

This paper presents a novel collision avoidance technique that allows the robot to reach a desired position by avoiding obstacles passing through preferred regions. The method combines the classical elements of the artificial potential fields in an original manner by handling local attractors and repulsors. The exact solution, which is given in a closed form, allows to sculpt a potential field so that local minima related to the local attractors are prevented and the global minimum is unperturbed. The results show the algorithm applied to mobile robot navigation and prove the capability of local attractors to influence the robot path.

Paper Citation

in Harvard Style

Melchiorre M., Scimmi L., Salamina L., Mauro S. and Pastorelli S. (2022). Robot Collision Avoidance based on Artificial Potential Field with Local Attractors. In Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-585-2, pages 340-350. DOI: 10.5220/0011353200003271

in Bibtex Style

@conference{icinco22,
author={Matteo Melchiorre and Leonardo Scimmi and Laura Salamina and Stefano Mauro and Stefano Pastorelli},
title={Robot Collision Avoidance based on Artificial Potential Field with Local Attractors},
booktitle={Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2022},
pages={340-350},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011353200003271},
isbn={978-989-758-585-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Robot Collision Avoidance based on Artificial Potential Field with Local Attractors
SN - 978-989-758-585-2
AU - Melchiorre M.
AU - Scimmi L.
AU - Salamina L.
AU - Mauro S.
AU - Pastorelli S.
PY - 2022
SP - 340
EP - 350
DO - 10.5220/0011353200003271