Mobile robot-based odor source localization (OSL) has broad applications in industrial and daily-life scenarios. However, subject to the limited sensing capacity of common metal oxide semiconductor (MOS) sensors, the OSL robots still lag far behind their biological counterparts. In this paper, we rethink the odor-source direction estimation paradigm of odor compass and propose a deep neural network (DNN) based method to improve both the accuracy and the generalization ability. The odor compass is composed of four wireless MOS sensors, and a DNN model, which contains a convolutional neural network (CNN) module and a long short-term memory (LSTM) module. An OSL strategy is further designed based on the proposed odor compass. Experimental results validate the feasibility of the proposed method.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Novel Odor Source Localization Method via a Deep Neural Network-Based Odor Compass


    Additional title:

    Lect. Notes in Networks, Syst.


    Contributors:

    Conference:

    Iberian Robotics conference ; 2022 ; Zaragoza, Spain November 23, 2022 - November 25, 2022



    Publication date :

    2022-11-19


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Reactive Searching and Infotaxis in Odor Source Localization

    Voges, Nicole / Chaffiol, Antoine / Lucas, Philippe et al. | BASE | 2014

    Free access

    Virtual cancelation plume for multiple odor source localization

    Cabrita, Goncalo / Marques, Lino / Gazi, Veysel | BASE | 2013

    Free access

    ODOR CONTROL DEVICE, AND ODOR CONTROL METHOD

    SAKAKIBARA KIYOMI / IWAI KOICHIRO / IMAEDA TAKAO et al. | European Patent Office | 2017

    Free access

    Particle Swarm Optimization Based on Shannon’s Entropy for Odor Source Localization

    Li, Nanqi / Lu, Qiang / He, Yang et al. | Springer Verlag | 2014


    Resource-aware Probability-based Collaborative Odor Source Localization Using Multiple UAVs

    Wang, Shan / Sun, Sheng / Liu, Min et al. | ArXiv | 2023

    Free access