A passive convex optimal control algorithm for teleoperating a redundant robotic arm in minimally invasive surgery

IF 3.2 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

International Journal of Robust and Nonlinear Control Pub Date : 2024-09-10 DOI:10.1002/rnc.7613

Nicola Piccinelli, Gianluca Colombo‐Taccani, Riccardo Muradore

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引用次数: 0

Abstract

In recent years, the remote center of motion (RCM) constraint has moved from purely mechanical to software implementation, enabling the use of serial manipulators in robotic‐assisted minimal invasive surgery. However, ensuring safety with software‐based RCM presents challenges. This article addresses this issue by introducing a novel control algorithm for a 7‐DOF redundant robotic arm, taking into account a software RCM while considering system passivity and kinematic constraints. The algorithm is both a control optimizer and a robot kinematics inversion, enabling precise and dexterous control for various surgical tasks and other control applications. By formulating a quadratic optimization problem under linear constraints, the algorithm guarantees the robotic arm's performance, safety, and stability under communication delay. Experimental validation demonstrates the effectiveness and accuracy of the control algorithm in executing a surgical training task (peg‐and‐ring) during bilateral teleoperation. The results highlight the successful implementation of the RCM constraint, ensuring patient safety and optimizing the manipulation capabilities of the robotic arm.

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微创手术中远程操作冗余机械臂的被动凸优化控制算法

近年来，远程运动中心（RCM）约束已从纯机械转向软件实现，使机器人辅助微创手术中使用串行机械手成为可能。然而，如何确保基于软件的 RCM 的安全性是一个挑战。本文针对这一问题，介绍了一种针对 7-DOF 冗余机械臂的新型控制算法，在考虑系统被动性和运动学约束的同时，还考虑了软件 RCM。该算法既是一种控制优化器，又是一种机器人运动学反转器，可为各种外科手术任务和其他控制应用提供精确灵巧的控制。通过提出线性约束下的二次优化问题，该算法保证了机械臂在通信延迟下的性能、安全性和稳定性。实验验证证明了该控制算法在双边远程操作过程中执行外科训练任务（钉环）时的有效性和准确性。结果凸显了 RCM 约束的成功实施，确保了病人的安全并优化了机械臂的操纵能力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Robust and Nonlinear Control 工程技术-工程：电子与电气

CiteScore

6.70

自引率

20.50%

发文量

505

审稿时长

2.7 months

期刊介绍： Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.