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

<div style="
    background: #00FF00;
    border-left: 4px solid #00FF00;
    padding: 1.5rem;
    margin: 2rem 0;
    font-family: 'Fira Code', 'Courier New', monospace;
    color: #00FF00;
    border-radius: 0 8px 8px 0;
">
    <pre style="
        font-size: 8px;
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    ">
 _    _    __  _  _  ____  ____  ____  _  _  ____  ____  ____ 
( \/\/ )  /__\( \/ )( ___)(  _ \( ___)( \( )(  _ \( ___)(  _ \
 )    (  /(__)\\  /  )__)  ) _ < )__)  )  (  )(_) ))__)  )   /
(__/\__)(__)(__)\/  (____)(____/(____)(_)\_)(____/(____)(_)\_)

</div>
  
# OVERVIEW

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).

# 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

2026