webxos/wavebender_dataset

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OVERVIEW

UNDER DEVELOPMENT

This dataset was generated using the WAVEBENDER app by webXOS, located in the /generator/ folder of this repo. Download WAVE BENDER to create your own similar datasets. (https://github.com/webxos for more info)

A sample generated of 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).

DETAILS

Structure & Content: Tiny tabular/text dataset (219 Bytes downloaded, ~4 KB in Parquet format) with 1 row and 8 columns:

complexity: int64 (value: 7)

noise: float64 (value: 2.5)

frequency: float64 (value: 1.8)

sample_rate: int64 (value: 100)

center_region_training: bool (value: true)

dynamic_obstacles: bool (value: true)

avoidance_training: bool (value: true)

dataset_id: string (value: "wave_bender_training_params")

USAGE

Load via Python libraries (e.g., from datasets import load_dataset; ds = load_dataset("webxos/wavebender_dataset") or pandas/parquet readers). Download the training app "WAVEBENDER" by webXOS in the /generator/ folder for configuring/training WaveBender— for a simulation involving waves, noise, frequency modulation, and obstacle avoidance (e.g., in physics, audio, or AI pathfinding).

DEVELOPER

webXOS

webxos.netlify.app

huggingface.co/webxos