The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/21815511

Jongeneel, Maarten; Sander Dingemans (2023): I.AM. archive containing long range box-toss experiments with Box004 for validation of impact models. Version 3. 4TU.ResearchData. dataset. https://doi.org/10.4121/21815511.v3

Dataset

x choose version: version 2 - 2023-01-17 version 1 - 2023-01-05

• Mechanical Engineering

Collision, Friction, Impact, Impact-Aware Manipulation, Modeling, Nonsmooth Mechanics, Object, Robotics, Validation

CC BY-NC-SA 4.0

• Impact-Aware Robotics Archives Collection
• Data underlying the publication: Validating Rigid-Body Dynamics Simulators on Real-World Data for Robotic Tossing Applications
• Impact-Aware Robotics Database

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Maarten Jongeneel , Sander Dingemans

I.AM. archive as part of the Impact-Aware Robotics Archives Collection. This archive contains recordings of experiments that are executed under the scenario of TOSS. In these recordings, Box004 is manually tossed on a stationary conveyor. The purpose of these experiments is to validate the box-conveyor impact modeling framework to assess how well it can predict the rest-pose of the box. This modeling framework is used within the H2020 I.AM. project (www.i-am-project.eu). The involved contact is between the object and the environment, which in these recordings are Box004 and a conveyor (Conveyor002), respectively. All the recordings in the archive were performed at the Innovation Lab of Vanderlande, located within the TU/e campus. More information about the dataset, the objects, and the environments used, can be found on https://impact-aware-robotics-database.tue.nl/.      

• 2023-01-05 first online

• 2023-01-20 published, posted

4TU.ResearchData

md, HDF5

• https://impact-aware-robotics-database.tue.nl/

• Impact Aware Manipulation by Dexterous Robot Control and Learning in Dynamic Semi-Structured Logistic Environments (grant code 871899) [more info...] European Commission

Eindhoven University of Technology, Department of Mechanical Engineering, Dynamics and Control Group