Numéro |
2019
19th International Congress of Metrology
|
|
---|---|---|
Numéro d'article | 26001 | |
Nombre de pages | 8 | |
Section | Industry 4.0 / Industrie 4.0 | |
DOI | https://doi.org/10.1051/metrology/201926001 | |
Publié en ligne | 23 septembre 2019 |
Autonomous vehicles for remote sensing use – Opportunities and challenges for Drone
1
INNOVIDEA SAS, President, 91370 Verrieres le Buisson, France
2
CDSI, Technical Director, 75008 Paris, France
3
HAZIEL, President, 95000 Cergy, France
4
INFRARED CAMERAS IncI, Chief Executive Officer, Beaumont, TX 77705 USA
* Corresponding author info@innovideasas.com
-Emergence of autonomous robotics and vehicles thanks to sensors, embedded processors and communication progress open new horizon for making remote sensing measurement without exposing human to unecessary risks. The collection of regular data in various environement (outdoor or indoor) allow more efficient predictive and preventive maintenance of assets that are difficult to access. Autonomous vehicle operating on ground, in the air or on water can shape a new future for science, industry and environnement. The optical and photonic industries offer now a wide range of compact sensor solutions using different wavelength not only to permit autoguidance of vehicles but also to complete the range of sensor already used for gathering various physical values with the advantage of non-contact. The application are numerous and encompass amongst other thermography, gas detection, hyperspectral imaging, security which can be extremely helpful for carrying day to day tasks for first responder, EHS officers, governmental regulatory organization, scientists…Routine checks can be thus envisaged that will save money by preventing or lessening risks or failure, better protecting environment and people, capitalizing on larger databases making use of AI. INNOVIDEA through its diverse industrial innovative partners will illustrate several example of use with drone (UAV/UAS/RPAS)
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.