自监督掩码感知Transformer用于微创手术机器人中容错FBG力传感
Self-Supervised Mask-Aware Transformers for Fault-Tolerant FBG Force Sensing in Minimally Invasive Surgical Robotics
AI总结：
针对微创手术机器人中FBG传感器因通道耦合和断裂导致的力估计退化问题，提出统一的自监督掩码感知Transformer，通过掩码通道重建预训练和动态损坏课程微调，实现多通道故障下的优雅降级，在8通道数据集上达到0.0066 N均方根误差。
摘要：
在微创手术机器人中，导管级光纤布拉格光栅（FBG）传感器因其能够通过复用多个光学通道来估计多维力而具有前景。然而，部署这些紧凑的多通道传感器引入了两个关键工程挑战：复杂变形过程中固有的非线性交叉轴耦合，以及受限工作空间中光纤断裂导致的间歇性通道丢失。这些复合问题严重降低了力估计性能。现有的容错方法依赖于组合模型库，其随通道数量呈指数级扩展，并且需要昂贵的每模式校准。在本文中，我们提出了一种统一的、自监督的掩码感知Transformer，它显式地建模通道可用性，以在多样化和动态的传感器故障下实现优雅降级。编码器通过未标记数据流上的掩码通道重建进行预训练，并使用平衡的干净与损坏视图目标以及动态损坏课程进行力回归微调。此外，通过异方差高斯负对数似然训练的并行不确定性头，在单次前向传播中预测每轴置信度，避免了多遍集成的开销。在导管级8通道FBG数据集上评估，我们的单一统一模型实现了标称均方根误差（RMSE）0.0066 N，并在严重4通道故障下优雅降级至0.0126 N。这显著优于包含255个每模式神经网络的综合模型库（4通道丢失时为0.0154 N），同时消除了模式特定校准。
英文摘要：
In minimally invasive surgical robotics, catheter-scale Fiber Bragg Grating (FBG) sensors are promising due to their ability to estimate multi-dimensional forces by multiplexing several optical channels. However, deploying these compact multi-channel sensors introduces two critical engineering challenges: inherent nonlinear cross-axis coupling during complex deformations, and intermittent channel dropouts caused by fiber fractures in constrained workspaces. These compounding issues severely degrade force estimation. Existing fault-tolerant approaches rely on combinatorial model banks, which scale exponentially with the channel count and demand prohibitively expensive per-pattern calibration. In this paper, we propose a unified, self-supervised mask-aware Transformer that explicitly models channel availability to enable graceful degradation under diverse and dynamic sensor failures. The encoder is pretrained via masked-channel reconstruction on unlabeled data streams and fine-tuned for force regression using a balanced clean-and-corrupted-view objective alongside a dynamic corruption curriculum. Furthermore, a parallel uncertainty head, trained via heteroscedastic Gaussian negative log-likelihood, predicts per-axis confidence in a single forward pass, circumventing the overhead of multi-pass ensembles. Evaluated on a catheter-scale 8-channel FBG dataset, our single unified model achieves a nominal Root Mean Square Error (RMSE) of 0.0066~N and degrades gracefully to 0.0126~N under severe 4-channel failures. This significantly outperforms a comprehensive model bank of 255 per-pattern neural networks (0.0154~N at 4-channel loss) while eliminating pattern-specific calibration.
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