基于迭代学习算法的柔性针轨迹规划

doi:10.3901/JME.2021.11.128

机械工程学报 ›› 2021, Vol. 57 ›› Issue (11): 128-137.doi: 10.3901/JME.2021.11.128

• 特邀专栏：生物组织精准手术器械设计制造 • 上一篇下一篇

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基于迭代学习算法的柔性针轨迹规划

李沐蓉¹, 雷勇¹, 黄成¹, 胡英达¹, 杜世伦¹, 关昊天¹, 高德东²

1. 浙江大学流体动力与机电控制国家重点实验室杭州 310027;
2. 青海大学机械工程学院西宁 810016

收稿日期:2020-12-23 修回日期:2021-02-19 出版日期:2021-06-05 发布日期:2021-07-23
通讯作者: 雷勇(通信作者),男,1976年出生,博士,教授,博士研究生导师。主要研究方向为水下移动机器人、水下结构缺陷检测、水下结构钻凿、修复机器人、软组织交互形变建模及分析、机器人辅助介入治疗、医疗过程仿真。E-mail:ylei@zju.edu.cn
作者简介:李沐蓉,女,1994年出生,博士研究生。主要研究方向为针穿刺模型。E-mail:limurong@zju.edu.cn
基金资助:
国家重大科研仪器研制项目（81827804）、浙江省自然科学基金（LSD19 H180004）、国家自然科学基金创新群体科学基金（51821903）和国家自然科学基金（51665049）资助项目。

Flexible Needle Path Planning Based on the Iterative Learning Algorithm

LI Murong¹, LEI Yong¹, HUANG Cheng¹, HU Yingda¹, DU Shilun¹, GUAN Haotian¹, GAO Dedong²

1. State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027;
2. School of Mechanical Engineering, Qinghai University, Xining 810016

Received:2020-12-23 Revised:2021-02-19 Online:2021-06-05 Published:2021-07-23

摘要/Abstract

摘要： 轨迹规划在机器人辅助柔性针穿刺中有着重要作用。在针-组织交互大变形下，针-组织耦合模型可以对由于针组织交互作用产生的针轨迹误差进行预测。提出一种基于迭代学习的轨迹规划算法：首先在离线状态下，基于可达域理论生成刚性空间下的初始路径集，并通过遗传模拟退火优化算法选择出最优路径；再基于针-组织耦合力学模型对柔性空间的针挠曲、组织形变进行预测，并根据偏差预测值和迭代学习算法不断修正柔性空间中的针控序列以满足穿刺精度要求，最后得到最优规划参数并且对针轨迹规划算法中的参数进行全局敏感性分析；最后，搭建了针穿刺试验台对提出的针轨迹算法进行验证，试验结果表明算法的轨迹平均误差小于0.45 mm。

关键词: 迭代学习控制, 针轨迹规划, 微创针穿刺, 柔性针

Abstract: Path planning plays an important role in robot-assisted flexible needle insertion procedures. The trajectory errors derived from the needle-tissue interaction can be predicted based on a proper needle-tissue interaction model, which is important for needle path planning, especially when the needle is inserted into highly deformable tissues. A novel iterative learning-based path planning method is proposed. First, a set of candidate paths are offline generated according to the reachable domain analysis under non-deformable environment, wherein the best candidate path is selected based on genetic simulated annealing algorithm (GSAA). Then, a real-time needle-tissue interaction model is utilized to predict the target deformation and needle deflection, which is combined with an iterative learning control-based path correction algorithm to achieve satisfactory trajectory accuracy. Finally, a global sensitivity analysis is applied to the proposed planning framework for parameter optimization. Needle insertion experiments are conducted with pre-defined targets and obstacles in phantoms to verify the proposed path planning methods. The results show that the error of the proposed algorithm can reach an average error below 0.45 mm.

Key words: iterative learning control, needle trajectory planning, minimally invasive needle insertion, flexible needle

中图分类号:

TG156

李沐蓉, 雷勇, 黄成, 胡英达, 杜世伦, 关昊天, 高德东. 基于迭代学习算法的柔性针轨迹规划[J]. 机械工程学报, 2021, 57(11): 128-137.

LI Murong, LEI Yong, HUANG Cheng, HU Yingda, DU Shilun, GUAN Haotian, GAO Dedong. Flexible Needle Path Planning Based on the Iterative Learning Algorithm[J]. Journal of Mechanical Engineering, 2021, 57(11): 128-137.

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基于迭代学习算法的柔性针轨迹规划

Flexible Needle Path Planning Based on the Iterative Learning Algorithm

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