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Modelling Gripping Principles
Unsolved Problem
250 h
Robotics is getting more and more important for the automation industry. Thus, it is important to find the best gripper for an object. This project is trying to do exactly that. Most approaches try to learn as many objects as possible to learn based on that. This means, that whenever a new object appears, it has to be learned and trained. This project strives to find a model that makes it possible to handle unknown objects.
Some facts and related work on the problem. Maybe an explanatory example. Also, include videos and links.
A model that describes how to grip unknown objects. A prototype that shows the approach and how good it is.
https://blog.hackster.io/new-mit-robotic-system-can-pick-and-place-objects-that-its-never-seen-before-a19896a4d50d
http://amazonpickingchallenge.org/
https://berkeleyautomation.github.io/dex-net/
https://www.machinedesign.com/robotics/difference-between-robotic-grippers-parallel-three-finger-and-angled-designs
https://github.com/OntoBREP/ontobrep
H. Schunk und A. Wolf, Greifer in Bewegung / Faszination der Automatisierung von Handhabungsaufgaben. München: Carl Hanser Verlag, 2016.
G. Fantoni, S. Capiferri, und J. Tilli, „Method for Supporting the Selection of Robot Grippers“, Procedia CIRP, Bd. 21, S. 330–335, 2014.
J. Schmalz und G. Reinhart, „Automated Selection and Dimensioning of Gripper Systems“, Procedia CIRP, Bd. 23, S. 212–216, 2014.
J. Schmalz, L. Giering, M. Hölzle, N. Huber, und G. Reinhart, „Method for the Automated Dimensioning of Gripper Systems“, Procedia CIRP, Bd. 44, S. 239–244, 2016.
J. Bohg, A. Morales, T. Asfour, und D. Kragic, „Data-Driven Grasp Synthesis—A Survey“, IEEE Transactions on Robotics, Bd. 30, Nr. 2, S. 289–309, Apr. 2014.
S. Hesse, Grundlagen der Handhabungstechnik: mit 34 Tabellen sowie 23 Übungsaufgaben und 123 Kontrollfragen: mit einer CD-ROM mit Zusatzmaterialien, Videos und Wörterbüchern Deutsch - Englisch und Englisch - Deutsch, 4., überarbeitete und erweiterte Auflage. München: Hanser, 2016.
Object detection using boundary representations of primitive shapes