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https://github.com/ansh-info/ur3-cobotops-clustering
Clustering analysis of UR3 CobotOps dataset to identify patterns in robotic operations.
https://github.com/ansh-info/ur3-cobotops-clustering
dbscan jupyter-notebook kmeans machine-learning numpy pandas python
Last synced: 8 days ago
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Clustering analysis of UR3 CobotOps dataset to identify patterns in robotic operations.
- Host: GitHub
- URL: https://github.com/ansh-info/ur3-cobotops-clustering
- Owner: ansh-info
- Created: 2024-06-30T10:52:34.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-07-06T14:56:26.000Z (7 months ago)
- Last Synced: 2024-11-30T19:11:34.064Z (2 months ago)
- Topics: dbscan, jupyter-notebook, kmeans, machine-learning, numpy, pandas, python
- Language: Jupyter Notebook
- Homepage:
- Size: 4.5 MB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# UR3 CobotOps Clustering Analysis
## Overview
This project focuses on the clustering analysis of the UR3 CobotOps dataset. The dataset includes multidimensional time-series data from the UR3 cobot, offering insights into operational parameters and faults for machine learning in robotics and automation.
## Reproducing the Analysis
To reproduce the analysis, follow these steps:
1. Clone the repository:
```sh
git clone https://github.com/yourusername/ur3-cobotops-clustering.git
cd UR3-Cobotops-Clustering
```
2. Install the required dependencies:
```sh
pip install -r requirements.txt
```
3. Run the Jupyter notebook:
```sh
jupyter notebook UR3 CobotOps Clustering.ipynb
```
## Dataset
The UR3 CobotOps dataset is a comprehensive collection of data including:
- Electrical currents
- Temperatures
- Speeds across joints (J0-J5)
- Gripper current
- Operation cycle count
- Protective stops
- Grip losses
**Dataset Characteristics:**
- **Type:** Multivariate, Time-Series
- **Instances:** 7409
- **Features:** 20
- **Tasks:** Classification, Regression, Clustering
## Files in the Repository
1. `Dataset`: The dataset used for the analysis.
2. `UR3 CobotOps - UCI Machine Learning Repository.pdf`: Documentation explaining the dataset and its variables.
3. `Fuzzy Cognitive Maps.pdf`: Scientific paper providing theoretical background on Fuzzy Cognitive Maps (FCMs) used in the analysis.
4. `UR3 CobotOps Clustering.ipynb`: Jupyter notebook with code and visualizations for clustering analysis.
## Analysis and Methodology
The project employs clustering techniques to analyze the UR3 CobotOps dataset. The primary methodology involves:
1. Data Pre-processing: Handling missing values, normalizing data, and encoding categorical variables.
2. Clustering: Applying various clustering algorithms to identify patterns and anomalies.
3. Visualization: Creating visualizations to interpret the clustering results.
### Key Visualizations
Here are some of the key visualizations from the analysis:
*Description: This visualization shows the clustering results of the UR3 CobotOps dataset using K-Means algorithm.*
*Description: This plot depicts the importance of different features in determining the clusters.*
## References
- [UR3 CobotOps Dataset - UCI Machine Learning Repository](https://archive.ics.uci.edu/dataset/963/ur3+cobotops)
- Tyrovolas, M., Liang, X. S., & Stylios, C. (2023). Information flow-based fuzzy cognitive maps with enhanced interpretability. Granular Computing, 8, 2021-2038. [DOI: 10.1007/s41066-023-00417-7](https://doi.org/10.1007/s41066-023-00417-7)
## Acknowledgements
This work was supported by the Department of Informatics and Telecommunications, University of Ioannina, and the Industrial Systems Institute, Athena Research Center.