A 3D Anti-collision System based on Artificial Potential Field Method for a Mobile Robot

Carlos Morais, Tiago Nascimento, Alisson Brito, Gabriel Basso

2017

Abstract

Anti-collision systems are based on sensing and estimating the mobile robot pose (coordinates and orientation), with respect to its environment. Obstacles detection, path planning and pose estimation are primordial to ensure the autonomy and safety of the robot, in order to reduce the risk of collision with objects and living beings that share the same space. For this, the use of RGB-D sensors, such as the Microsoft Kinect, has become popular in the past years, for being relative accurate and low cost sensors. In this work we proposed a new 3D anti-collision algorithm based on Artificial Potential Field method, that is able to make a mobile robot pass between closely spaced obstacles, while also minimizing the oscillations during the cross. We develop our Unmanned Ground Vehicles (UGV) system on a ’Turtlebot 2’ platform, with which we perform the experiments.

Paper Citation

in Harvard Style

Morais C., Nascimento T., Brito A. and Basso G. (2017). A 3D Anti-collision System based on Artificial Potential Field Method for a Mobile Robot . In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-219-6, pages 308-313. DOI: 10.5220/0006245303080313

in Bibtex Style

@conference{icaart17,
author={Carlos Morais and Tiago Nascimento and Alisson Brito and Gabriel Basso},
title={A 3D Anti-collision System based on Artificial Potential Field Method for a Mobile Robot},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2017},
pages={308-313},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006245303080313},
isbn={978-989-758-219-6},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - A 3D Anti-collision System based on Artificial Potential Field Method for a Mobile Robot
SN - 978-989-758-219-6
AU - Morais C.
AU - Nascimento T.
AU - Brito A.
AU - Basso G.
PY - 2017
SP - 308
EP - 313
DO - 10.5220/0006245303080313