README.md

---
license: mit
task_categories:
- robotics
- reinforcement-learning
- tabular-regression
tags:
- drone
- slam
- physics
- art
- telemetry
- obstacle-avoidance
- synthetic
- robotics
---

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 _    _    __  _  _  ____  ____  ____  _  _  ____  ____  ____ 
( \/\/ )  /__\( \/ )( ___)(  _ \( ___)( \( )(  _ \( ___)(  _ \
 )    (  /(__)\\  /  )__)  ) _ < )__)  )  (  )(_) ))__)  )   /
(__/\__)(__)(__)\/  (____)(____/(____)(_)\_)(____/(____)(_)\_)

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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.*

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).

# 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