license: mit
task_categories:
- robotics
- reinforcement-learning
- tabular-regression
tags:
- drone
- slam
- physics
- art
- telemetry
- obstacle-avoidance
- synthetic
- robotics
_ _ __ _ _ ____ ____ ____ _ _ ____ ____ ____ ( \/\/ ) /__\( \/ )( ___)( _ \( ___)( \( )( _ \( ___)( _ \ ) ( /(__)\\ / )__) ) _ < )__) ) ( )(_) ))__) ) / (__/\__)(__)(__)\/ (____)(____/(____)(_)\_)(____/(____)(_)\_)
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