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.
CITATION STYLE
Yan, Z., Jing, T., Chen, S. W., Jabeen, M., & Meng, Q. H. (2023). A Novel Odor Source Localization Method via a Deep Neural Network-Based Odor Compass. In Lecture Notes in Networks and Systems (Vol. 590 LNNS, pp. 189–200). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-21062-4_16
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