Juan Heredia

These datasets collect sensorial information about collaborative robot functioning. We recorded information from two different kinds of robots UR3e and UR10e. This dataset is used for data-driving modeling of the power consumption of cobots. The datasets have the following information: recording time, trajectory ID, joints' positions, joints' velocities, motor currents, motor torques, motor voltages, end effector position, force and momentum exerted to the end effector, current and voltage of the robot.

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[1] Juan Heredia, Christian Schlette, Mikkel Baun Kjærgaard, "Dataset of Collaborative Robots for Energy Consumption Modeling", IEEE Dataport, 2021. [Online]. Available: http://dx.doi.org/10.21227/9wnt-8v86. Accessed: Mar. 18, 2025.
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doi = {10.21227/9wnt-8v86},
url = {http://dx.doi.org/10.21227/9wnt-8v86},
author = {Juan Heredia; Christian Schlette; Mikkel Baun Kjærgaard },
publisher = {IEEE Dataport},
title = {Dataset of Collaborative Robots for Energy Consumption Modeling},
year = {2021} }
TY - DATA
T1 - Dataset of Collaborative Robots for Energy Consumption Modeling
AU - Juan Heredia; Christian Schlette; Mikkel Baun Kjærgaard
PY - 2021
PB - IEEE Dataport
UR - 10.21227/9wnt-8v86
ER -
Juan Heredia, Christian Schlette, Mikkel Baun Kjærgaard. (2021). Dataset of Collaborative Robots for Energy Consumption Modeling. IEEE Dataport. http://dx.doi.org/10.21227/9wnt-8v86
Juan Heredia, Christian Schlette, Mikkel Baun Kjærgaard, 2021. Dataset of Collaborative Robots for Energy Consumption Modeling. Available at: http://dx.doi.org/10.21227/9wnt-8v86.
Juan Heredia, Christian Schlette, Mikkel Baun Kjærgaard. (2021). "Dataset of Collaborative Robots for Energy Consumption Modeling." Web.
1. Juan Heredia, Christian Schlette, Mikkel Baun Kjærgaard. Dataset of Collaborative Robots for Energy Consumption Modeling [Internet]. IEEE Dataport; 2021. Available from : http://dx.doi.org/10.21227/9wnt-8v86
Juan Heredia, Christian Schlette, Mikkel Baun Kjærgaard. "Dataset of Collaborative Robots for Energy Consumption Modeling." doi: 10.21227/9wnt-8v86