---
language:
- en
license: cc-by-nc-4.0
task_categories:
- object-detection
- image-classification
- tabular-regression
tags:
- robotics
- uav
- ugv
- drones
- computer-vision
- autonomous-systems
pretty_name: Robot2RobotIdentification
modality:
- image
size_categories:
- n<1k
---

# Robot2RobotIdentification

<div align="center">
  <p><strong>A dataset for machine-to-machine visual awareness.</strong></p>
  <p><em>Supported by <a href="https://a19lab.com">A19Lab, Inc.</a></em></p>
</div>

## Dataset Description

**Robot2RobotIdentification** is a vision dataset designed to help drones, UGVs, and autonomous robots detect and recognize each other in real-world environments.

As autonomous machines become more common in skies, streets, and industrial spaces, reliable machine-to-machine perception is essential for safety, coordination, and navigation. This dataset supports that need by linking **visual annotations** with **robotic specifications**, enabling both recognition and attribute-based understanding.

Note: To respect copyright and minimize size, this repository contains metadata manifests only. The full image dataset is hydrated locally from public video sources using the provided script.

---

## Repository Structure

- **`uav_ugv_dataset.json`** — Visual manifest containing video IDs, timestamps, labels, and bounding box annotations.
- **`uav_ugv_specs.csv`** — Feature database with physical, performance, and sensor attributes for each class.
- **`download_dataset.py`** — Script that downloads source videos and extracts frame-accurate images.

---

## Technical Specifications (`uav_ugv_specs.csv`)

Every class in this dataset is linked to a detailed Feature Vector stored in `uav_ugv_specs.csv`.

| Category | CSV Column | Type | Description |
| :--- | :--- | :--- | :--- |
| **Identity** | `class_name` | String | Unique directory ID (e.g., `dji-mavic-3`) |
| | `model` | String | Commercial product name |
| | `domain` | String | `UAV` (Aerial) or `UGV` (Ground) |
| | `type` | String | Configuration (e.g., `Quadcopter`, `Tracked`, `VTOL`) |
| **Physical** | `mass_kg` | Float | Total weight including standard batteries |
| | `payload_kg` | Float | Maximum additional carrying capacity |
| | `length_mm` | Int | Length (Front-to-Back) |
| | `width_mm` | Int | Width (Side-to-Side) |
| | `height_mm` | Int | Height (Ground-to-Top) |
| **Performance**| `speed_kmh` | Float | Maximum horizontal speed |
| | `ascent_speed_kmh` | Float | Vertical rise speed (UAV only) |
| | `descent_speed_kmh` | Float | Vertical drop speed (UAV only) |
| | `range_km` | Float | Operational distance |
| | `endurance_min` | Int | Max flight or drive time in minutes |
| **Features** | `has_tracks` | Bool | `TRUE` if vehicle uses continuous tracks |
| | `has_manipulator` | Bool | `TRUE` if equipped with a robotic arm/gripper |
| | `has_prop_guards` | Bool | `TRUE` if propellers are enclosed, caged, or ducted |
| | `has_thermal` | Bool | `TRUE` if equipped with thermal camera |
| | `has_lidar` | Bool | `TRUE` if equipped with LiDAR sensor |

---

## How to Use

1. **Clone the repository** to get the JSON manifests and the Python script.

2. **Install dependencies:**
   ```bash
   pip install yt-dlp
   ```
  **Install FFmpeg** (required for frame extraction):  
  [https://ffmpeg.org/download.html](https://ffmpeg.org/download.html)

3.  **Run the downloader:** `python download_dataset.py`
    * *This will download the necessary videos from YouTube, extract the specific annotated frames, and organize them into a `dataset_raw/` folder.*
4.  **Load in Python:** Use the `class_name` from the visual annotations to look up the corresponding row in `uav_ugv_specs.csv`.

---

## Disclaimer & License

**License:** [Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/)

**Data Source:** All data originates from publicly available videos. The dataset repository contains annotations and metadata only. Users must download the actual frames directly from the original video sources using the provided script and comply with all platform terms and copyright rules.

## Disclamer 
This dataset is published by A19Lab for research, development, and educational purposes only.
A19Lab makes no warranties or guarantees regarding the accuracy, completeness, reliability, legality, or fitness for any specific use of the data provided. All data is offered as-is.
A19Lab is not responsible for any direct, indirect, incidental, or consequential damages resulting from the use, misuse, or interpretation of this dataset or any derivatives thereof.
Users are solely responsible for ensuring their use of the data complies with applicable laws, regulations, platform terms of service, and ethical standards.
A19Lab does not endorse or condone any harmful, malicious, or unlawful applications of this dataset.

---

## Support & Contact

**Supported by A19Lab, Inc**
* **Web:** [https://a19lab.com](https://a19lab.com)

*