Chasing Ghosts: A Simulation-to-Real Olfactory Navigation Stack with Optional Vision Augmentation

TL;DR

This work presents a complete, open-source UAV system for online odor source localization using a minimal sensor suite, and elaborate on the hardware design and open source the UAV firmware, simulation code, olfaction-vision dataset, and circuit board to the community.

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 systems. Vision is incorporated as an optional complementary modality to accelerate navigation under certain conditions. We validate the proposed system through real-world flight experiments in a large indoor environment using an ethanol source, demonstrating consistent source-finding behavior under realistic airflow conditions. The primary contribution of this work is a reproducible system and methodological framework for UAV-based olfactory navigation and source finding under minimal sensing assumptions. We elaborate on our hardware design and open source our UAV firmware, simulation code, olfaction-vision dataset, and circuit board to the community. Code, data, and designs will be made available at https://github.com/KordelFranceTech/ChasingGhosts.

Figures (16)

• Figure 1: The UAV equipped with the olfactory processing unit (OPU) and sensor harnesses for scent-based navigation. The metal-oxide sensor configuration is shown in panel A with the electrochemical sensor configuration shown in panel B.
• Figure 2: The UAV equipped with the olfactory processing unit (OPU) and sensor harnesses. The figure shows a top-down view of the aircraft: (1) the 2 ballast points needed to balance the aircraft; (2) the battery to power the motherboard required for the olfactory sensors; (3) the wire harnessing leading to the motherboard attached to the belly of the aircraft; (4) the forward-looking time-of-flight sensor for obstacle avoidance; (5) the forward-looking camera; (6) the motherboard and downward-looking time-of-flight infrared sensors attach to the belly (not shown); (7) the two olfactory sensor antennae (MOX sensors shown, but EC sensors lie at the same location when in the proper configuration).
• Figure 3: (A) From top to bottom, this panel shows the altitude, velocity, pitch, and roll from the controller response (blue line) and the command (red line) from the controls algorithms. (B) A diagram showing a partial flight path of the UAV in simulation. (C) From top to bottom, this panel shows the olfactory signal responses for ethanol and nitrogen dioxide.
• Figure 4: Olfaction-Vision Model Architecture. Blue and green boxes construct Component 1 based on COLIP. The yellow boxes construct Component 2. The output from both models inform reasoning for navigation.
• Figure 5: Illustration of the course developed for olfactory navigation by the UAV. Figure A shows a bird's-eye-view of the course, where the ground station (laptop icon) is located at the top left and the UAV (quadcopter icon) takes off next to it; Room 2 hosts the target compound and the plume is formed from here. Figure B shows an isometric view of the ideal path the UAV should pursue to locate the source.