使用 Koopman 运算器设计肺介入手术连续机器人的 MPC

IF 4.6 2区 计算机科学 Q2 ROBOTICS
Yuhua Song;Lifeng Zhu;Jinfeng Li;Jiawei Deng;Cheng Wang;Aiguo Song
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引用次数: 0

摘要

这封信的重点是用于肺介入手术的支气管镜机器人的柔性管,该机器人被视为连续体机器人。我们基于 Koopman 算子提出了动力学模型,利用真实数据精确求解系统矩阵参数。为了提高控制精度,我们设计了一种模型预测控制(MPC)算法,旨在跟踪柔性管所需的曲率和偏转角。MPC 控制器使用来自电磁传感器的实时数据来调整管道形状,从而确保精确和灵敏的操控。在二进制实验平台上进行的大量实验验证了所提算法的有效性。与传统的开环控制方法相比,所提出的算法在跟踪性能和运行可靠性方面都有显著提高。所提算法的每个系统状态误差更小,这意味着在所提的 MPC 方案下可以获得更好的跟踪性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MPC Design of a Continuum Robot for Pulmonary Interventional Surgery Using Koopman Operators
This letter focuses on the flexible tube of a bronchoscope robot used in pulmonary intervention surgery, which is considered as a continuum robot. The dynamics model is proposed based on the Koopman operator, leveraging real data to solve for the system matrix parameters accurately. To enhance control precision, we designed a model predictive control (MPC) algorithm aimed at tracking the desired curvature and deflection angles of the flexible tube. The MPC controller uses real-time data from electromagnetic sensors to adjust the tube shape, ensuring accurate and responsive manipulation. The effectiveness of the proposed algorithm are validated through extensive experiments conducted on the Binary experimental platform. Compared with the traditional open-loop control method, the proposed algorithm has achieved significant improvements in tracking performance and operational reliability. Each system state error of the proposed algorithm is smaller, which means that better tracking performance can be obtained under the proposed MPC scheme.
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来源期刊
IEEE Robotics and Automation Letters
IEEE Robotics and Automation Letters Computer Science-Computer Science Applications
CiteScore
9.60
自引率
15.40%
发文量
1428
期刊介绍: The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.
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