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

2026-02-23

AI Summary

This work addresses the challenge of autonomous odor source localization for drones operating under turbulent airflow, sparse and delayed olfactory signals, and stringent payload constraints. We present the first reproducible olfactory navigation framework relying solely on minimal sensing, integrating a custom lightweight gas sensor with a reinforcement learning–based navigation policy. Trained in simulation and successfully transferred to a real quadrotor platform, the system operates in either pure olfactory or olfactory-visual fusion modes, without requiring gas concentration mapping, predefined trajectories, or external positioning infrastructure. Real-world flight experiments in a large indoor environment using an ethanol source demonstrate robust odor source localization under complex airflow conditions. The project releases open-source firmware, simulation code, a multimodal dataset, and circuit designs to facilitate reproducibility and further research.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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.

Problem

• odor source localization
• aerial robots
• olfactory navigation
• turbulent airflow
• minimal sensing

Innovation

• olfactory navigation
• simulation-to-real
• minimal sensing
• learning-based policy
• vision-augmented UAV