Collision Detection for a Mobile Robot using Logistic Regression

Felix Becker, Marc Ebner

2019

Abstract

Collisions cannot be entirely avoided during normal operation of an autonomous mobile robot. Therefore, mobile robots need to detect collisions and react appropriately when they happen. We investigate whether logistic regression on acceleration data can be used for collision detection. We have collected training data from an acceleration sensor during normal driving behavior of a small mobile robot. Collisions were manually marked by a human operator. Accelerations occurring in a direction opposite to the current direction of motion are more likely to be actual collisions. Hence, we combine accelerometer data and motor commands in the logistic regression model. The trained model was able to detect 13 out of 14 collisions on a separate test set with no false positives.

Paper Citation

in Harvard Style

Becker F. and Ebner M. (2019). Collision Detection for a Mobile Robot using Logistic Regression.In Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-758-380-3, pages 167-173. DOI: 10.5220/0007768601670173

in Bibtex Style

@conference{icinco19,
author={Felix Becker and Marc Ebner},
title={Collision Detection for a Mobile Robot using Logistic Regression},
booktitle={Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2019},
pages={167-173},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007768601670173},
isbn={978-989-758-380-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - Collision Detection for a Mobile Robot using Logistic Regression
SN - 978-989-758-380-3
AU - Becker F.
AU - Ebner M.
PY - 2019
SP - 167
EP - 173
DO - 10.5220/0007768601670173