Work Description

Title: BIRDS Lab Multipod robot motion tracking data - VIDEOS dataset Open Access Deposited

h
Attribute Value
Methodology
  • We filmed the motion tracking trials of  https://doi.org/10.7302/m05a-0d90 (the "raw" dataset), which track Multipod robots with 4,6,8,10 and 12 legs. We ran each robot at multiple inter-segment phase differences and gait frequencies, and some robots were run with two different roll angle amplitudes for the leg motions. This deposit is the "videos" dataset, which contains video directories for each of the raw data directories.
Description
  • This dataset contains the videos used for  https://doi.org/10.7302/m05a-0d90 (the "raw" motion tracking dataset), and is intended to be unpacked into the same directory tree. The data were produced for ARO W911NF-14-1-0573 "Morphologically Modulated Dynamics" and ARO MURI W911NF-17-1-0306 "From Data-Driven Operator Theoretic Schemes to Prediction, Inference, and Control of Systems" to explore the trade-offs between various oscillator coupling models in modeling multilegged locomotion. The data were also used extensively in examining multi-contact slipping, in the studying the influence of number of legs on otherwise identical locomotion patterns, and in the use of geometric mechanics models for multilegged locomotion. Folder and file names encode the meta-data, with names following an informative naming convention documented in the README.
Creator
Depositor
  • shrevzen@umich.edu
Contact information
Discipline
Funding agency
  • Department of Defense (DOD)
Keyword
Date coverage
  • 2018-06 to 2020-12
Citations to related material
Resource type
Curation notes
  • You may need to use VLC to view the *.MTS video files.
Last modified
  • 11/19/2022
Published
  • 11/09/2021
Language
DOI
  • https://doi.org/10.7302/1y3q-1b42
License
To Cite this Work:
BIRDS Lab U. Michigan. (2021). BIRDS Lab Multipod robot motion tracking data - VIDEOS dataset [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/1y3q-1b42

Files (Count: 10; Size: 16.5 GB)

Video files for Multipod robot motion tracking data collected at the University of Michigan's Bio-Inspired Robotics and Dynamical Systems Lab
(BIRDS-Lab) https://www.birds.eecs.umich.edu Principal Investigator: Shai Revzen

Collection of these data is the work of many people over a period of several years.

Data is stored in folders whose names encode metadata, e.g. Roll=2500_Yaw=2000_Fr=2.664/8_Leg/
Roll motion amplitude set to 2500 centi-degrees
Yaw motion amplitude set to 2000 centi-degrees
Gait frequency set to 2.664 Hz
The sub-folder 8_Leg contains data from the 8 legged multipod under those conditions

This VIDEOS dataset is intended to be used in conjuction with the Multipod RAW dataset which contains the motion tracking data found at https://doi.org/10.7302/m05a-0d90
It contains bundles of videos that unpack into <<-data-folder->>/videos folders of the associated data files.

The robot is based on the CKBot modular robot architecture designed by ModLab at UPenn.
It extensively uses the ModLock ( Davey, et. al., IROS 2012 https://doi.org/10.1109/IROS.2012.6386190 )

The data in this dataset was used in the following publications:

Zhao, D. & Revzen, S.
Multi-legged steering and slipping with low DoF hexapod robots
Bioinspiration & biomimetics, 2020, 15, 045001
https://doi.org/10.1088/1748-3190/ab84c0

Zhao, D.
Ph.D. Thesis "Locomotion of low-DOF multi-legged robots"
University of Michigan 2021

and is currently being used in several additional manuscripts and pre-prints.

To Cite this Work:
BIRDS Lab, U. Multipod robot motion tracking data - VIDEOS dataset [Data set],
University of Michigan - Deep Blue Data. https://doi.org/10.7302/1y3q-1b42

Funding for the equipment was provided from:
University of Michigan start-up funds for Revzen
ARO W911NF-17-1-0243 "DRAKE: Dynamics, Robotics, And Kinematics Experiments"

Funding for researchers was provided from:
ARO W911NF-14-1-0573 "Morphologically Modulated Dynamics"
ARO MURI W911NF-17-1-0306 "From Data-Driven Operator Theoretic Schemes to Prediction, Inference, and Control of Systems"

With particular thanks to Haotian Li for his hard work on robot data collection.

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