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The integration of AI-driven voice-controlled systems is transforming human-drone interaction, enabling intuitive and efficient UAV operation. This paper presents a novel approach for voice-commanded drone navigation using a fine-tuned T5 model, ensuring structured execution through predefined flight functions. The system incorporates Whisper for speech-to-text conversion and AirSim for high-fidelity simulation via its Python API. By leveraging NLP and real-time processing, the model accurately maps user commands to drone actions while maintaining execution safety. Experimental results demonstrate 97% accuracy in function mapping, highlighting the system’s reliability and responsiveness. This work advances AI-powered UAV control, enabling applications in search-and-rescue, autonomous inspections, and hands-free drone operations.

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Words-to-Fly: A Function-Based Approach to Voice-Controlled UAVs

  • 会议论文

  • 发表日期：

    2026年

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  • K. V. Rama Satish,
  • J. Hemavathi,
  • Shrihari Kulkarni,
  • V. V. Sathya Sai Likhith,
  • A. Balasubramani,
  • C. S. Shashaank Raj

摘要

The integration of AI-driven voice-controlled systems is transforming human-drone interaction, enabling intuitive and efficient UAV operation. This paper presents a novel approach for voice-commanded drone navigation using a fine-tuned T5 model, ensuring structured execution through predefined flight functions. The system incorporates Whisper for speech-to-text conversion and AirSim for high-fidelity simulation via its Python API. By leveraging NLP and real-time processing, the model accurately maps user commands to drone actions while maintaining execution safety. Experimental results demonstrate 97% accuracy in function mapping, highlighting the system’s reliability and responsiveness. This work advances AI-powered UAV control, enabling applications in search-and-rescue, autonomous inspections, and hands-free drone operations.