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Machine Learning for Capacitive Proximity Sensor Data

Madani, Badis; Alagi, Hosam ORCID iD icon; Hein, Bjoern; Arntzen, Aurilla Aurelie

Abstract (englisch):

In order to increase safety in human-robot interaction, it is important to find reliable methods and approaches allowing better detection of people and objects in the robot environment. One research area concerns the development of proximity sensing technology. This paper intends to address this challenging requirement by applying a machine learning model specifically dedicated to a new kind of capacitive tactile proximity sensor (CTPS) that has been developed by the Intelligent Process Automation and Robotics Lab (IPR-KIT) in Germany. Our research study focused on using machine learning approach to infer from the data collected by the sensor array in order to insure a safer human–robot interaction. To achieve our goal, we built a classifier and a regressor on the projected distance between objects and the sensor.

Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Fakultät für Informatik – Institut für Prozessrechentechnik, Automation und Robotik (IPR)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 11.2017
Sprache Englisch
Identifikator ISSN: 1090-9389
KITopen-ID: 1000122150
Erschienen in Proceedings of the Society for Design and Process Science Transformative Research and Education through Transdisciplinary MeansTM. Ed.: L. Jololian
Veranstaltung SDPS 22nd International Conference on Emerging Trends and Technologies in Convergence Solutions (2017), Birmingham, AL, USA, 05.11.2017 – 09.11.2017
Verlag Society for Design and Process Science
Seiten 214-219
Externe Relationen Abstract/Volltext
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