license: mit
task_categories:
- robotics
- reinforcement-learning
- tabular-regression
tags:
- drone
- slam
- physics
- art
- telemetry
- obstacle-avoidance
- synthetic
- robotics
_ _ __ _ _ ____ ____ ____ _ _ ____ ____ ____ ( \/\/ ) /__\( \/ )( ___)( _ \( ___)( \( )( _ \( ___)( _ \ ) ( /(__)\\ / )__) ) _ < )__) ) ( )(_) ))__) ) / (__/\__)(__)(__)\/ (____)(____/(____)(_)\_)(____/(____)(_)\_)
V2
UNDER DEVELOPMENT
This dataset was generated using the WAVEBENDER app by webXOS
LINK: https://huggingface.co/datasets/webxos/wavebender_dataset/tree/main/generator (Download this app to create your own similar datasets)
Generated synthetic dataset for drone autonomy ML training, including telemetry signals (acceleration, gyro, altitude, velocity, battery, GPS), SLAM (obstacle detection/mapping), and avoidance maneuvers in simulated 3D environments with configurable parameters (complexity, noise, frequency, dynamic obstacles). Synthetic drone datasets are generally used to overcome real-world data limitations for unmanned aerial vehicles (UAVs).
Key Features
- Realistic 3D simulated environments
- Configurable parameters: scene complexity, sensor noise, update frequency, dynamic/moving obstacles
- Multi-modal data: raw telemetry + processed SLAM + maneuver labels
Included Signals
Telemetry
- Acceleration (3-axis)
- Gyroscope (3-axis)
- Altitude (barometric / fusion)
- Velocity vector
- Battery level / voltage
- GPS position & velocity
SLAM / Perception
- Obstacle detection & mapping output
- Distance to nearest obstacles
Labels / Actions
- Avoidance maneuver executed (direction, type, intensity)
Status
Under active development — v2 expands variety, realism, and annotation quality over v1 (link below) https://huggingface.co/datasets/webxos/wavebender_dataset