用少量新数据1秒内适配新传感器阵列，误差从23毫米降到4毫米
A Novel Method with Encoder-Decoder for Cross-Sensor Adaptation in Surface Shape Sensing with Sparse Strain Sensors
- 设计编码器-解码器架构结合元学习，实现跨传感器阵列快速适应
- 仅需不到5%新标签数据，1秒内将形状感知误差降至4.0毫米
- 适合软体机器人与可穿戴设备中快速部署的高精度传感场景
传感器阵列因内在差异或安装条件不同，常导致形状感知结果不一致。传统方法需大量数据并为每组阵列重新训练模型，显著增加数据采集、传输和计算成本。本文提出一种基于稀疏应变传感器的编码器-解码器架构，并引入元学习与少样本适应策略，实现跨传感器阵列的快速适配。实验表明，新部署的传感器阵列在仅使用不足5.0%新标注数据且适应时间低于1秒的情况下，感知误差降至约4.0毫米，相比无适配时23.0毫米的误差和20分钟建模时间有显著提升；同时，误差低于5.0毫米的点数占比提高超过65.0%。该方法大幅降低了表面形状感知的成本与训练负担，具有在软体机器人和可穿戴设备中的广泛应用潜力。
原文摘要 · Abstract (English)
Performance variations in sensor arrays, caused by intrinsic differences or installation conditions, can lead to inconsistent results during shape sensing. To obtain accurate results, a large amount of data is usually required, and a separate model must be retrained for each sensor array, thereby increasing the cost and time of data acquisition, transmission, and computation. To address this issue, this work proposes an encoder-decoder architecture for surface shape sensing based on sparse strain sensors and further incorporates meta-learning and few-shot adaptation strategies to enable adaptation across different groups of sensor arrays. Experimental results demonstrate that, after the cross-sensor adaptation, a newly deployed sensor array achieves a sensing error of approximately 4.0 mm relying on less than 5.0% newly labeled data and requiring an adaptation time of under 1 second, which represents a substantial improvement from 23.0 mm error without adaptation and 20-minute data collection time required to train a new model. Moreover, the number of points with errors below 5.0 mm increased by more than 65.0%. These results indicate that the proposed method can substantially reduce the cost and training burden of surface shape sensing, and it has broad potential applications in soft robotics and wearable devices.
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