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: 10.4121/21814998

Jongeneel, Maarten; Sander Dingemans (2023): I.AM. archive containing long-range tossing experiments for Trajectory Based Parameter Identification of Box004. Version 4. 4TU.ResearchData. dataset. https://doi.org/10.4121/21814998.v4

Dataset

x choose version: version 3 - 2023-01-17 version 2 - 2023-01-09 version 1 - 2023-01-05

• Mechanical Engineering

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

CC BY-NC-SA 4.0

• 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 belt. The purpose of these experiments is to obtain data for trajectory based parameter identification as part of a modeling framework. This modeling framework is used within the H2020 I.AM. project (www.i-am-project.eu) to predict the end pose of a certain box on a conveyor belt, after it is tossed. This means that the involved contact is between the object and the environment, which in these recordings are Box004 and a conveyor, respectively. All the recordings in the archive were performed at the Innovation Lab of Vanderlande, located within the TU/e campus. More information 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