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Ernst Moritz Arndt Universität Greifswald, Institut für Mathematik und Informatik, Walther-Rathenau-Str. 47, 17487 Greifswald and Germany

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1 Papers

Collision Detection for a Mobile Robot using Logistic Regression

Topic: Mobile Robots and Autonomous Systems

Authors: Felix Becker and Marc Ebner

Affiliation: Ernst Moritz Arndt Universität Greifswald, Institut für Mathematik und Informatik, Walther-Rathenau-Str. 47, 17487 Greifswald and Germany

Digital Object Identifier: 10.5220/0007768601670173

Proceedings of ICINCO 2019, ISBN: 978-989-758-380-3

Quick 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.

Related Ontology Subjects/Areas/Topics (3)

Informatics in Control, Automation and Robotics

Mobile Robots and Autonomous Systems

Robotics and Automation