# Sniffy Bug: A Fully Autonomous Swarm of Gas-Seeking Nano Quadcopters in Cluttered Environments

arXiv: https://arxiv.org/abs/2107.05490 · HTML: https://mlsanity.com/paper/2107.05490/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2021.

## Built on by (most cited first, 13 of 13)

- [Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots](https://mlsanity.com/paper/2205.05748/) (2022)
- [NanoFlowNet: Real-time Dense Optical Flow on a Nano Quadcopter](https://mlsanity.com/paper/2209.06918/) (2022)
- [Collaborative Target Search with a Visual Drone Swarm: An Adaptive Curriculum Embedded Multistage Reinforcement Learning Approach](https://mlsanity.com/paper/2204.12181/) (2022)
- [ReProHRL: Towards Multi-Goal Navigation in the Real World using Hierarchical Agents](https://mlsanity.com/paper/2308.08737/) (2023)
- [Low Latency Visual Inertial Odometry with On-Sensor Accelerated Optical Flow for Resource-Constrained UAVs](https://mlsanity.com/paper/2406.13345/) (2024)
- [A Map-free Deep Learning-based Framework for Gate-to-Gate Monocular Visual Navigation aboard Miniaturized Aerial Vehicles](https://mlsanity.com/paper/2503.05251/) (2025)
- [TiROD: Tiny Robotics Dataset and Benchmark for Continual Object Detection](https://mlsanity.com/paper/2409.16215/) (2024)
- [Channel-Aware Distillation Transformer for Depth Estimation on Nano Drones](https://mlsanity.com/paper/2303.10386/) (2023)
- [Position: Olfaction Standardization is Essential for the Advancement of Embodied Artificial Intelligence](https://mlsanity.com/paper/2506.00398/) (2025)
- [Diffusion Graph Neural Networks and Dataset for Robust Olfactory Navigation in Hazard Robotics](https://mlsanity.com/paper/2506.00455/) (2025)
- [Olfactory Inertial Odometry: Methodology for Effective Robot Navigation by Scent](https://mlsanity.com/paper/2506.02373/) (2025)
- [Olfactory Inertial Odometry: Sensor Calibration and Drift Compensation](https://mlsanity.com/paper/2506.04539/) (2025)
- [Towards Batch-to-Streaming Deep Reinforcement Learning for Continuous Control](https://mlsanity.com/paper/2603.08588/) (2026)

## Abstract (arXiv)

Nano quadcopters are ideal for gas source localization (GSL) as they are safe, agile and inexpensive. However, their extremely restricted sensors and computational resources make GSL a daunting challenge. In this work, we propose a novel bug algorithm named `Sniffy Bug', which allows a fully autonomous swarm of gas-seeking nano quadcopters to localize a gas source in an unknown, cluttered and GPS-denied environments. The computationally efficient, mapless algorithm foresees in the avoidance of obstacles and other swarm members, while pursuing desired waypoints. The waypoints are first set for exploration, and, when a single swarm member has sensed the gas, by a particle swarm optimization-based procedure. We evolve all the parameters of the bug (and PSO) algorithm, using our novel simulation pipeline, `AutoGDM'. It builds on and expands open source tools in order to enable fully automated end-to-end environment generation and gas dispersion modeling, allowing for learning in simulation. Flight tests show that Sniffy Bug with evolved parameters outperforms manually selected parameters in cluttered, real-world environments.