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Updated: May 14, 2026

Visually Mediated Odor Tracking During Flight in Drosophila
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Chasing Ghosts: A Simulation-to-Real Olfactory Navigation Stack with Optional Vision Augmentation.

Kordel K France1, Ovidiu Daescu1, Latifur Khan1

  • 1Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an open-source aerial robot system for autonomous odor source localization. The unmanned aerial vehicle (UAV) navigates directly to a scent without mapping, demonstrating effective real-world performance.

Keywords:
UAVcomputer visionelectrochemical sensorsmetal oxide sensorsodor source localizationolfactory navigationreinforcement learningroboticssim-to-real transferstereo olfaction

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Environmental Science

Background:

  • Autonomous odor source localization for aerial robots is complex due to airflow turbulence and signal limitations.
  • Existing unmanned aerial vehicle (UAV) olfaction systems often require predefined patterns, external support, or extensive resources.

Purpose of the Study:

  • To develop a complete, open-source UAV system for online odor source localization with minimal sensing.
  • To enable direct navigation to odor sources without explicit gas mapping or external positioning.

Main Methods:

  • Integration of custom olfaction hardware, onboard sensors, and a learned navigation policy trained in simulation.
  • Deployment of the system on a real quadrotor, with optional vision integration for enhanced navigation.
  • Validation through real-world flight experiments in an indoor environment using an ethanol source.

Main Results:

  • The UAV successfully navigated directly to the odor source under realistic airflow conditions.
  • The system demonstrated consistent source-finding behavior without relying on gas distribution maps.
  • The framework proved effective even with minimal sensing assumptions.

Conclusions:

  • A reproducible system and methodological framework for UAV-based olfactory navigation and source finding has been established.
  • The open-source release of hardware designs, firmware, and datasets facilitates community adoption and further research.
  • This work advances the capabilities of aerial robots in complex olfactory navigation tasks.