ロボット軟性内視鏡ナビゲーションにおける後退判断予測のためのマルチモーダル進捗残差融合
Multimodal Progress-Residual Fusion for Anticipating Navigation Retreat in Robotic Flexible Endoscopy
指令・電磁トラッキング・内視鏡映像を融合し、通常進行モデルからの残差を用いて操作者の後退判断を事前予測する因果モデルMPRFを提案し、大腸ファントム実験で有効性を示した。
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著者: Chi Kit Ng, Tianchun Wu, Jiatan Sun, Yuming Sun, Jianru Zhang, Hongliang Ren
分類: cs.RO
原文アブストラクト
Flexible robotic endoscopes provide access through tortuous anatomy, where friction, deformation, and wall interaction separate commanded insertion from useful distal progress. Operators interpret this mismatch and retreat to recover a navigable configuration. Recognizing the approach to that decision requires relating motor intent to distal response and the local visual scene. We introduce Multimodal Progress-Residual Fusion (MPRF), a causal model that anticipates operator retreat from commands, electromagnetic (EM) tracking, and endoscopic video. A nominal-progress model predicts the response expected in normal or successful navigation; a temporal predictor combines its EM residual with all three observed modalities. In a 150-episode colon-phantom collection, MPRF achieves 0.580 +/- 0.007 AUPRC across three seeds, compared with 0.511 for the strongest reduced-modality control. Complete multimodal histories provide the principal ranking advantage, while the residual adds an explicit intent-response representation with a small, uncertain increment over raw fusion. Analysis of frozen alarms relates warning timing to the behavioral label and explains the increase in event recall under a longer matching window. These results establish a basis for behavioral assistance during operator-controlled navigation.