Research Article
E-Nose design and structures from statistical analysis to application in robotic: a compressive review
@ARTICLE{10.4108/airo.v2i1.3056, author={Ata Jahangir Moshayedi and Amir Sohail Khan and Yang Shuxin and Geng Kuan and Hu Jiandong and Masoumeh Soleimani and Abolfazl Razi}, title={E-Nose design and structures from statistical analysis to application in robotic: a compressive review}, journal={EAI Endorsed Transactions on AI and Robotics}, volume={2}, number={1}, publisher={EAI}, journal_a={AIRO}, year={2023}, month={4}, keywords={ENose, pre-processing method, statistical analyse, Machine learning, robotic, odour source localization}, doi={10.4108/airo.v2i1.3056} }
- Ata Jahangir Moshayedi
Amir Sohail Khan
Yang Shuxin
Geng Kuan
Hu Jiandong
Masoumeh Soleimani
Abolfazl Razi
Year: 2023
E-Nose design and structures from statistical analysis to application in robotic: a compressive review
AIRO
EAI
DOI: 10.4108/airo.v2i1.3056
Abstract
Since 1982, the olfactory system of creatures has piqued the interest of academics who seek to create a comparable system. Despite its mysterious nature, the first stage has been successfully completed with the development of the E-nose. Its extended applications have opened new doors for researchers, ranging from food quality testing to bomb detection and even, more recently, identifying those infected with the coronavirus. In this talk, we will review the structure and sensor behavior of the E-nose, as well as its applications, such as odour source localization and various applications in agriculture. The challenge of odour identification has prompted researchers to employ robots with sensors to investigate and locate odour sources. The present study aims to synthesize documented research and provide a fresh perspective on odour localization research efforts and tests conducted. The study highlights previous attempts to equip robots with sensors to explore the real indoor or outdoor environment. Initially, a review was conducted to investigate various aspects of the sector and the obstacles involved.
Copyright © 2023 A. J. Moshayedi et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.