fts-assessment
Science Score: 67.0%
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✓CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
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✓DOI references
Found 3 DOI reference(s) in README -
✓Academic publication links
Links to: arxiv.org -
○Academic email domains
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○Scientific vocabulary similarity
Low similarity (13.4%) to scientific vocabulary
Repository
Basic Info
Statistics
- Stars: 1
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Metadata Files
README.md
FTS Assessment
This repository contains the scripts we used to assess force-torque sensor data for a paper that is currently under review.
This project is licensed under the MIT License.
Paper
A preprint of the paper is available at arxiv.org:
bibtex
@misc{gerdes2024fieldassessmentforcetorque,
title={Field Assessment of Force Torque Sensors for Planetary Rover Navigation},
author={Levin Gerdes and Carlos Pérez del Pulgar and Raúl Castilla Arquillo and Martin Azkarate},
year={2024},
eprint={2411.04700},
archivePrefix={arXiv},
primaryClass={cs.RO},
doi={10.48550/arXiv.2411.04700},
url={https://arxiv.org/abs/2411.04700},
}
The preprint was prepared with version v0.1-preprint of this code.
Both bibtex and formatted text citation can be copied using Github's "Cite this Repository" button in the right-hand side toolbar.
Dependencies
This repository uses Python 3.10. All requirements are listed in pyproject.toml.
Install them via uv
bash
uv sync
uv pip install -e .
or pip
bash
python3 -m venv .venv
source .venv/bin/activate
pip install -e .
Data path
Point the scripts to the Baseprod traverse data. You can use your own path like so:
Set the environment variable for your path, e.g.
bash export BASEPROD_TRAVERSE_PATH="/mnt/baseprod/sensor_data"Alternatively, modify the default path (if no correct environment variable is found) in preprocessing/traverse_overview.
Preprocessing
Check how much data is usable according to the distance computed by Fy/Tx with preprocessing/find_usable.py.
bash python -m preprocessing.find_usableGenerate the dataset for the later machine learning with preprocessing/exportclassificationstats.py.
bash python -m preprocessing.export_classification_stats
Creates three output files (training_data.csv, training_data_ft.csv,
training_data_imu.csv) containing the data for all sensors, only the force
torque sensors, and only the IMU data, respectively.
If only a subset of FTSs should be included, --fts_names FL FR can be passed
to only include the data from FTSs FL and FR (plus IMU) for example.
Plot FTS and IMU data with preprocessing/plotftsimu.py
bash python -m preprocessing.plot_fts_imu
Run classification training
From the project's root, invoke
bash
python -m ml.svm --csv training_data_ft.csv --data_source fts
or
bash
python -m ml.train --csv training_data.csv --data_source all
See all possible arguments by passing --help.
Owner
- Name: Space Robotics Lab at UMA
- Login: spaceuma
- Kind: organization
- Location: Universidad de Málaga
- Website: https://www.uma.es/robotics-and-mechatronics/info/107542/robotica-espacial/
- Repositories: 3
- Profile: https://github.com/spaceuma
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this software, please cite it using the 'preferred-citation' metadata."
authors:
- family-names: "Gerdes"
given-names: "Levin"
orcid: "https://orcid.org/0000-0001-7648-8928"
title: "FTS Assessment"
version: 0.0.1
repository-code: "https://github.com/spaceuma/fts-assessment"
date-released: "2024-10-31"
license: "MIT"
preferred-citation:
authors:
- family-names: "Gerdes"
given-names: "Levin"
orcid: "https://orcid.org/0000-0001-7648-8928"
- family-names: "Pérez del Pulgar"
given-names: "Carlos"
orcid: "https://orcid.org/0000-0001-5819-8310"
- family-names: "Castilla Arquillo"
given-names: "Raúl"
orcid: "https://orcid.org/0000-0003-4203-8069"
- family-names: "Azkarate"
given-names: "Martin"
orcid: "https://orcid.org/0000-0003-3284-5422"
title: Field Assessment of Force Torque Sensors for Planetary Rover Navigation
doi: "10.48550/arXiv.2411.04700"
url: "https://arxiv.org/abs/2411.04700"
type: article
year: "2024"
publisher: "arXiv"
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Dependencies
- actions/checkout v4 composite
- actions/setup-python v5 composite
- actions/checkout v4 composite
- astral-sh/ruff-action v1 composite
- matplotlib *
- matplotlib-stubs *
- numpy *
- opencv-python *
- pandas *
- pandas-stubs *
- pyqt6 *
- pytest *
- pytest-cov *
- scikit-learn *
- scipy *
- scipy-stubs *
- seaborn *
- torch *
- torcheval *