Create README.md
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README.md
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---
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license: mit
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task_categories:
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- robotics
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- reinforcement-learning
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- tabular-regression
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tags:
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- drone
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- slam
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- physics
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- art
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- telemetry
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- obstacle-avoidance
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- synthetic
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- robotics
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---
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[](https://webxos.netlify.app)
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[](https://github.com/webxos/webxos)
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[](https://huggingface.co/webxos)
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[](https://x.com/webxos)
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<div style="
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background: #00FF00;
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border-left: 4px solid #00FF00;
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padding: 1.5rem;
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margin: 2rem 0;
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font-family: 'Fira Code', 'Courier New', monospace;
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color: #00FF00;
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border-radius: 0 8px 8px 0;
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">
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<pre style="
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font-size: 8px;
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line-height: 1.2;
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margin: 0;
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overflow-x: auto;
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color: #00FF00;
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">
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_ _ __ _ _ ____ ____ ____ _ _ ____ ____ ____
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( \/\/ ) /__\( \/ )( ___)( _ \( ___)( \( )( _ \( ___)( _ \
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) ( /(__)\\ / )__) ) _ < )__) ) ( )(_) ))__) ) /
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(__/\__)(__)(__)\/ (____)(____/(____)(_)\_)(____/(____)(_)\_)
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</div>
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# OVERVIEW
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*UNDER DEVELOPMENT*
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*This dataset was generated using the WAVEBENDER app by webXOS, located in the /generator/ folder of this repo. Download WAVE BENDER
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to create your own similar datasets.*
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Generated synthetic dataset for drone autonomy ML training, including telemetry signals
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(acceleration, gyro, altitude, velocity, battery, GPS), SLAM (obstacle detection/mapping),
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and avoidance maneuvers in simulated 3D environments with configurable parameters (complexity,
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noise, frequency, dynamic obstacles). Synthetic drone datasets are generally used to overcome
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real-world data limitations for unmanned aerial vehicles (UAVs).
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