用低成本传感器实时识别霉菌并定位污染源
Scensory: Real-Time Robotic Olfactory Perception for Joint Identification and Source Localization
- 基于神经网络解析挥发性气体的时序动态，实现多任务感知
- 在3-7秒内达成89.85%物种识别与87.31%定位准确率
- 适合需要低成本环境监测的机器人应用
尽管机器人感知在视觉和触觉方面进展迅速，但从弱扩散型化学信号中推断室内霉菌污染仍是开放挑战。我们提出Scensory，一种基于学习的机器人嗅觉框架，通过低成本、交叉敏感的挥发性有机物（VOC）传感器阵列采集的短时序数据，同时完成霉菌种类识别与污染源定位。时间维度上的VOC动态编码了化学与空间特征，通过在机器人自动化数据采集与空间监督下训练的神经网络进行解码。在五种霉菌物种上，Scensory在环境条件下使用3-7秒传感器输入，实现了最高89.85%的物种识别准确率和87.31%的源定位准确率。结果表明，该方法可实现从扩散主导的化学信号中实时、空间锚定的感知，为机器人室内环境监测提供了可扩展、低成本的溯源方案。
原文摘要 · Abstract (English)
While robotic perception has advanced rapidly in vision and touch, enabling robots to reason about indoor fungal contamination from weak, diffusion-dominated chemical signals remains an open challenge. We introduce Scensory, a learning-based robotic olfaction framework that simultaneously identifies fungal species and localizes their source from short time series measured by affordable, cross-sensitive VOC sensor arrays. Temporal VOC dynamics encode both chemical and spatial signatures, which we decode through neural networks trained on robot-automated data collection with spatial supervision. Across five fungal species, Scensory achieves up to 89.85% species accuracy and 87.31% source localization accuracy under ambient conditions with 3-7s sensor inputs. These results demonstrate real-time, spatially grounded perception from diffusion-dominated chemical signals, enabling scalable and low-cost source localization for robotic indoor environmental monitoring.
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