根据医生操作意图动态调整机械臂运动缩放，提升手术精度与效率
SIMS: Surgeon-Intention-driven Motion Scaling for Efficient and Precise Teleoperation
- 通过速度、工具对齐和双臂协同特征实时判断操作意图
- 实验中碰撞率降低83%，工作负荷显著下降
- 无需额外传感器，适合临床部署的轻量级系统
远程手术常采用固定运动缩放因子（MSF）将医生手部动作映射为机器人器械运动，但存在精度与效率的权衡：小MSF利于精细操作但延缓大范围移动，大MSF加速传输却牺牲准确性。本文提出外科医生意图驱动的运动缩放（SIMS）系统，仅基于运动学信号实时动态调节MSF。SIMS提取线性速度、工具运动对齐度及双臂协调特征，通过模糊C均值聚类识别操作意图，并对双臂分别进行置信度加权更新。在da Vinci Research Kit上对10名受试者开展三项外科训练任务的用户研究显示，相比固定MSF，SIMS显著减少碰撞（平均降低83%），降低心理与生理负荷，同时保持任务完成效率。结果表明，SIMS是一种实用、轻量的自适应遥操作控制方案，可实现更安全、高效、人机协同的远程手术。
原文摘要 · Abstract (English)
Telerobotic surgery often relies on a fixed motion scaling factor (MSF) to map the surgeon's hand motions to robotic instruments, but this introduces a trade-off between precision and efficiency: small MSF enables delicate manipulation but slows large movements, while large MSF accelerates transfer at the cost of accuracy. We propose a Surgeon-Intention driven Motion Scaling (SIMS) system, which dynamically adjusts MSF in real time based solely on kinematic cues. SIMS extracts linear speed, tool motion alignment, and dual-arm coordination features to classify motion intent via fuzzy C-means clustering and applies confidence-based updates independently for both arms. In a user study (n=10, three surgical training tasks) conducted on the da Vinci Research Kit, SIMS significantly reduced collisions (mean reduction of 83%), lowered mental and physical workload, and maintained task completion efficiency compared to fixed MSF. These findings demonstrate that SIMS is a practical and lightweight approach for safer, more efficient, and user-adaptive telesurgical control.
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