A Deep Learning-Based Indoor Odor Compass

摘要

Mobile robot-based odor source localization (OSL) has broad applications in various industrial and daily-life scenarios. To this end, a deep learning-based odor compass is designed in this work. Functionally, the designed odor compass is divided into three primary modules, which are the sensing module (i.e., a sensor array composed of four metal-oxide-semiconductor (MOS) gas sensors), the communication module, and the remote data processing module (i.e., a deep learning-based algorithm). In particular, a deep learning-based odor attention (DL-OA) model is proposed to realize an end-to-end odor source direction estimation (OSDE) based on the responses of gas sensor array. Moreover, the proposed DL-OA model adopts a separated spatial-temporal attention-based encoder-decoder structure. Furthermore, the average validation error in estimating the OSD in an indoor environment is 4.98°, essentially demonstrating the effectiveness of designed odor compass.