用迭代学习控制提升机器人手术针精准旋转插入效果。
Improving Needle Penetration via Precise Rotational Insertion Using Iterative Learning Control
- 通过迭代学习控制，根据位置反馈调整关节指令，克服系统偏差。
- 在离体猪眼实验中，旋转插入成功率显著高于直线插入。
- 适合高精度微创手术等需要精确穿刺的机器人任务。
实现机器人工具路径的精确控制常受系统错位、未建模动态和驱动误差的挑战。本文提出一种迭代学习控制（ILC）策略，用于机器人手术中工具的精准旋转插入，相比直线插入，在视网膜下注射任务中提升了穿刺效率与安全性。采用4自由度机器人机械臂，第四关节的错位使针头旋转难以直接应用，因此引入基于光学相干断层扫描（OCT）体积成像反馈的ILC方法，通过多次迭代优化控制输入。首先校准所用手术工具的正向运动学以提高精度，再利用OCT扫描测量误差并修正指令。在离体猪眼的视网膜下注射实验中，优化轨迹显著提升了组织穿透和注射成功率，验证了该方法在克服错位问题上的有效性。该方案可推广至其他需可控穿刺的高精度机器人任务。
原文摘要 · Abstract (English)
Achieving precise control of robotic tool paths is often challenged by inherent system misalignments, unmodeled dynamics, and actuation inaccuracies. This work introduces an Iterative Learning Control (ILC) strategy to enable precise rotational insertion of a tool during robotic surgery, improving penetration efficacy and safety compared to straight insertion tested in subretinal injection. A 4 degree of freedom (DOF) robot manipulator is used, where misalignment of the fourth joint complicates the simple application of needle rotation, motivating an ILC approach that iteratively adjusts joint commands based on positional feedback. The process begins with calibrating the forward kinematics for the chosen surgical tool to achieve higher accuracy, followed by successive ILC iterations guided by Optical Coherence Tomography (OCT) volume scans to measure the error and refine control inputs. Experimental results, tested on subretinal injection tasks on ex vivo pig eyes, show that the optimized trajectory resulted in higher success rates in tissue penetration and subretinal injection compared to straight insertion, demonstrating the effectiveness of ILC in overcoming misalignment challenges. This approach offers potential applications for other high precision robot tasks requiring controlled insertions as well.
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