Next Article in Journal
An Adversarial-Risk-Analysis Approach to Counterterrorist Online Surveillance
Next Article in Special Issue
Analysis of Model Mismatch Effects for a Model-Based Gas Source Localization Strategy Incorporating Advection Knowledge
Previous Article in Journal
SLIC Superpixel-Based l2,1-Norm Robust Principal Component Analysis for Hyperspectral Image Classification
Previous Article in Special Issue
Chemical Source Searching by Controlling a Wheeled Mobile Robot to Follow an Online Planned Route in Outdoor Field Environments
Article Menu
Issue 3 (February-1) cover image

Export Article

Open AccessArticle
Sensors 2019, 19(3), 478; https://doi.org/10.3390/s19030478

Smelling Nano Aerial Vehicle for Gas Source Localization and Mapping

1
Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Baldiri Reixac 10-12, 08028 Barcelona, Spain
2
Department of Electronics and Biomedical Engineering, Universitat de Barcelona, Marti i Franqués 1, 08028 Barcelona, Spain
3
AASS Mobile Robot Olfaction Lab, Örebro University, SE 70182 Örebro, Sweden
*
Author to whom correspondence should be addressed.
Received: 11 December 2018 / Revised: 21 January 2019 / Accepted: 22 January 2019 / Published: 24 January 2019
Full-Text   |   PDF [10669 KB, uploaded 24 January 2019]   |  

Abstract

This paper describes the development and validation of the currently smallest aerial platform with olfaction capabilities. The developed Smelling Nano Aerial Vehicle (SNAV) is based on a lightweight commercial nano-quadcopter (27 g) equipped with a custom gas sensing board that can host up to two in situ metal oxide semiconductor (MOX) gas sensors. Due to its small form-factor, the SNAV is not a hazard for humans, enabling its use in public areas or inside buildings. It can autonomously carry out gas sensing missions of hazardous environments inaccessible to terrestrial robots and bigger drones, for example searching for victims and hazardous gas leaks inside pockets that form within the wreckage of collapsed buildings in the aftermath of an earthquake or explosion. The first contribution of this work is assessing the impact of the nano-propellers on the MOX sensor signals at different distances to a gas source. A second contribution is adapting the ‘bout’ detection algorithm, proposed by Schmuker et al. (2016) to extract specific features from the derivative of the MOX sensor response, for real-time operation. The third and main contribution is the experimental validation of the SNAV for gas source localization (GSL) and mapping in a large indoor environment (160 m2) with a gas source placed in challenging positions for the drone, for example hidden in the ceiling of the room or inside a power outlet box. Two GSL strategies are compared, one based on the instantaneous gas sensor response and the other one based on the bout frequency. From the measurements collected (in motion) along a predefined sweeping path we built (in less than 3 min) a 3D map of the gas distribution and identified the most likely source location. Using the bout frequency yielded on average a higher localization accuracy than using the instantaneous gas sensor response (1.38 m versus 2.05 m error), however accurate tuning of an additional parameter (the noise threshold) is required in the former case. The main conclusion of this paper is that a nano-drone has the potential to perform gas sensing tasks in complex environments. View Full-Text
Keywords: robotics; signal processing; electronics; gas source localization; gas distribution mapping; gas sensors; drone; UAV; MOX sensor; quadcopter robotics; signal processing; electronics; gas source localization; gas distribution mapping; gas sensors; drone; UAV; MOX sensor; quadcopter
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Burgués, J.; Hernández, V.; Lilienthal, A.J.; Marco, S. Smelling Nano Aerial Vehicle for Gas Source Localization and Mapping. Sensors 2019, 19, 478.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top