Work Description

Title: Walking Like a Worm : dataset and figures Open Access Deposited

O
Attribute Value
Methodology
  • The data here is from multiple sources. It is consolidated in this one location to facilitate easy reproduction of the figures in the authors' manuscript titled "Walking like a worm : Stokesian kinematics govern legged locomotion". Each constituent data-set is separately available. Ant data was prepared for doi:10.1098/rsos.192068 BigANT robot data was prepared for doi:10.1088/1748-3190/ab84c0 and is available at doi:10.7302/024q-kk06 and doi:10.7302/jh82-fh69
Description
  • This repository contains both the data and python3 code that reads this data and reproduces the relevant figures. The code depends on NumPy >1.17 and matplotlib >3.1 and was tested on python 3.8
Creator
Depositor
  • shrevzen@umich.edu
Contact information
Discipline
Funding agency
  • National Science Foundation (NSF)
  • Department of Defense (DOD)
  • Other Funding Agency
Other Funding agency
  • D. Dan and Betty Kahn Michigan-Israel Partnership for Research and Education Autonomous Systems Mega-Project
Keyword
Resource type
Last modified
  • 11/17/2022
Published
  • 10/28/2021
Language
DOI
  • https://doi.org/10.7302/gqk6-3x41
License
  • GPL 3.0
To Cite this Work:
Revzen, S. (2021). Walking Like a Worm : dataset and figures [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/gqk6-3x41

Relationships

In Collection:

Files (Count: 3; Size: 120 MB)

Thumbnailthumbnail-column Title Original Upload Last Modified File Size Access Actions
wlam.tar.xz 2021-08-26 2021-08-26 120 MB Open Access
LICENSE 2021-08-26 2021-08-26 34.3 KB Open Access
README.txt 2021-08-26 2021-08-26 457 Bytes Open Access

Walking Like a Worm: Modeling and Analysis

Dependencies:
- numpy
- matplotlib

To run the code, use:

python3 run_analyses.py

or run interactively from ipython3

NOTE: even on a strong PC this can take several hours. After the first execution, the system caches
intermediate computations in the __data__ directory as .pkl pickle files. Thus, future runs are much
quicker, but may still take a few minutes.

Brian Bittner, Shai Revzen
Michigan, 2021

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