Efficiently localizing the source of odor in complex environments remains a critical challenge, especially in environments with obstacles or walls. We study diffusion state perception as an unsupervised classification problem that can inform downstream search behaviors. We built a dodecahedral gas-sensing device equipped with 11 ethanol sensors and introduced a novel diffusion state classification system designed to capture the odor diffusion state via multi-directional gas measurements. The proposed system employs a manifold learning method known as Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and K-means++ for clustering, enabling unsupervised classification of odor diffusion states. Experimental results demonstrated that the proposed method achieves clearer diffusion state classification compared to conventional two-directional sensor systems. Furthermore, the system was tested using data from unseen environments, suggesting its generalization capability. These findings are expected to contribute to the development of adaptive search algorithms and enhance the efficiency of odor source localization.
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