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

Clone the repository to get the JSON manifests and the Python script.
Install dependencies:
```
pip install yt-dlp
```

Install FFmpeg (required for frame extraction):
https://ffmpeg.org/download.html

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.
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)

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

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Robot2RobotIdentification

Dataset Description