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