TL;DR
This paper introduces an open-source UAV system for real-time odor source localization using minimal sensors, combining simulation-trained policies with optional vision, validated through indoor flight experiments.
Contribution
It presents a complete, reproducible UAV framework for olfactory navigation that operates without explicit gas maps or external infrastructure, integrating hardware, sensing, and learning-based control.
Findings
UAV successfully locates odor sources in indoor airflow conditions.
The system operates with minimal sensors and no external positioning.
Open-source code and hardware designs are provided for community use.
Abstract
Autonomous odor source localization remains a challenging problem for aerial robots due to turbulent airflow, sparse and delayed sensory signals, and strict payload and compute constraints. While prior unmanned aerial vehicle (UAV)-based olfaction systems have demonstrated gas distribution mapping or reactive plume tracing, they rely on predefined coverage patterns, external infrastructure, or extensive sensing and coordination. In this work, we present a complete, open-source UAV system for online odor source localization using a minimal sensor suite. The system integrates custom olfaction hardware, onboard sensing, and a learning-based navigation policy trained in simulation and deployed on a real quadrotor. Through our minimal framework, the UAV is able to navigate directly toward an odor source without constructing an explicit gas distribution map or relying on external positioning…
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