微创手术机器人力检测与力反馈技术研究现状

在外科手术中力觉发挥着重要的作用,可以为医生提供组织刚度、肿瘤位置等信息,防止医生用力过度对正常的组织造成损害,在进行一些精细操作时可令医生施加合适的作用力.然而,现有微创手术机器人在克服微创手术诸多不足为医生提供巨大帮助的同时,却令医生丧失了与患者组织之间接触时产生的交互作用力信息,给患者组织带来了过度创伤和损害.为了使医生重新获得接触力信息,研究人员利用各种检测方法和反馈技术将力觉信息引入到微创手术机器人系统中,包括基于电阻和光纤的直接检测技术,基于位移和执行器输入量的间接检测技术,以及直接反馈和感知替代两种力反馈方法.本文对微创手术机器人力检测和力反馈两项关键技术的研究现状进行总结,并对这两项技术的发展趋势进行展望.     

仿人机器人站立多平衡策略控制

设计了全状态反馈参数化控制器用于仿人机器人在站立平衡中应对外界冲击力与持续力. 采用多个机器人模型模仿人类站立平衡中的多个应对策略. 对每个模型, 设计了参数控制器作用于每一个状态误差, 并针对不同的作用力大小、方向和位置优化控制器参数. 应对不同外界作用力展现了该控制器的性能. 通过比较每一个策略处理干扰力的能力, 本文也探索了每个关节在站立平衡中的作用.     

一种基于弹簧模型的作用力视觉反馈方法

在虚拟拆装系统中,为了增强用户的"沉浸感",会引入力反馈设备,但目前力反馈设备价格昂贵,同时力反馈算法也不成熟,二者导致了力反馈的引入无法商用和推广.为使用户在无力反馈设备的虚拟拆装系统中感受到操作时作用力的变化,提出了一种基于弹簧模型的作用力视觉反馈方法,构造一个可形变、带标尺的弹簧来代表虚拟手,将拆装关系变化时引起的力反馈通过弹簧的实时形变来表达,用视觉反馈形象化地表现了力觉反馈,取得了良好的实际效果.实验证明,此方法既降低了虚拟拆装系统的研发难度,又节约了虚拟拆装系统开发与推广的成本,具有较好的推广和使用价值.     

一个七自由度带力反馈的遥操作主手的开发

本文展示了一个机器人系统中具有力反馈(Haptic/Force Feedback)功能的遥操作主手的开发。此主手具有七自由度的串联结构,并集成了多个电机以提供力反馈。通过ROS(Robot Operating System)的Rviz仿真环境进行了运动学验证。详述了算法开发过程,如运动学、雅克比矩阵、可操作性、正逆动力学。逆动力学计算的结果可以提供跟力控相关的功能,如重力补偿。通过以上工作,此主手不仅可以作为遥操作的输入端,而且可以将外界的接触力反馈回主手,使其成为一个双向遥操作仪器(Bilateral Teleoperation Device)。因此它可以成为机器人遥操作场景下,尤其是机器人辅助微创手术RAMIS(Robot-assisted Minimally Invasive Surgery)中重要的遥操作工具。     

力觉临场感遥控机器人的预测控制系统设计

在力觉临场感遥控机器人系统中,由于通信环节中存在时延,双向力反射系统易产生振荡;环境的不确定性会影响系统的性能。介绍了通过在线辨识系统模型,采用广义预测控制器来生成预测力,预测力反馈可克服时延影响,维待系统稳定。     

基于网络的力觉临场感的实现

文章利用游戏杆装置进行了遥操作机器人的力反馈控制。控制的关键是如何正确地描述移动机器人与环境之间的作用力,使之正确地反映给远端的操作者,从而有效地完成控制。本文利用“弹簧-阻尼器”系统模型来描述力,并将其映射到游戏杆上,变成操纵方向的约束力。通过采用微软公司的带有力反馈的操纵杆作为力觉提示装置控制移动机器人小车在非结构化环境下顺利运行,验证了力反馈描述方法的有效性。     

基于虚拟力引导的人机协同目标抓取方法

针对力反馈遥操作中传统人工势场法无法适应于机械臂整体的避障以及在作业过程中操作者难以控制机械臂到达所需位姿的问题,提出了一种基于虚拟力引导的人机协同目标抓取方法。力反馈设备向操作者提供力觉交互。通过结合人工势场法和虚拟夹具,构建管道形虚拟力场,生成实时虚拟力引导,实现协助操作者完成从端机器人的整体避障任务并在完成避障后引导机器人返回预定义路径并趋近目标点。当进行抓取任务时,构建锥形虚拟力场,实现协助操作者操作机械臂到达目标位置和姿态。此外,提出了一种机器人运动限制方法以降低操作者的操作失误对抓取任务的影响。实验证明,该方法能有效提高目标抓取操作的成功率和操作效率。     

血管介入手术机器人系统力反馈的模糊融合

为完善主从式血管介入手术机器人系统,构建了力反馈回路.通过集成两个微型力传感器,实现了对导管前端和侧壁与血管接触力的同时测量.鉴于主从模式与传统手动操作的不同,在进行力觉交互时,为保证医生成熟的手术经验继续发挥作用,采用模糊推理中的双输入–单输出模型完成两路数据的融合.通过标定隶属度函数和制定融合规则表,将积累的手术经验转化为数字化的力反馈策略,不但改善了低采集频率下的力反馈效果,更在准确表征介入状态的同时,针对不同的接触状态实现了不同的反馈分辨率,从而进一步保证了机器人手术系统的适应性、安全性和操作效率.     

基于非线性力反馈模型的软组织变形仿真

虚拟手术仿真研究中反馈力一般采用线性模型,但软组织的反馈力往往呈现出非线性的特点.通过对软组织生物力学特性的分析,将非线性性质应用到软组织力反馈模型的研究.在保持原线性反馈力方向不变的前提下,加入非线性扰动,建立了反馈力的非线性模型,从而使反馈力更加真实.首先构建了人体肝脏物理模型,然后给出了非线性力反馈模型方程,并对人体肝脏物理模型变形和反馈力进行了仿真.为了减小计算量采用了整体刚度矩阵缩减算法.仿真结果表明该模型有较好的非线性和可调节性.     

基于距离传感器的双边遥操作

为了改善双边遥操作的力反馈性能,本文根据从端操作臂上的传感器检测的目标距离信息,设计了新 的PD 双边控制器．证明了系统的稳定性条件,并通过单自由度双边遥操作实验系统,对提出的控制方法进行了实 验验证．实验结果表明,当从端操作臂靠近目标时,主端操作臂产生了逐渐增大的反馈力．这种控制策略为操作者 安全实现遥操作任务提供了有效手段．     

Observer-based force reflecting robust coordination control for networked bilateral shared telerobotic system

This paper proposes observer-based output feedback force reflecting robust coordination control for networked bilateral shared telerobotic system with asymmetrical delay and uncertainty. We design state feedback-based force reflecting control algorithm provided that all the states are available for feedback. We then replace the unknown velocity states by model-free observer to develop output feedback-based force reflecting robust coordination control algorithm for bilateral shared autonomous system. The coordination control algorithm is designed by combining delayed position and velocity states with the reflected interaction forces from human and environment. Robust adaptive control theory is employed to deal with uncertainty. The stability analysis is shown by using Lyapunov method. The method does not require linear matrix inequality and uncertainty. Compared with other force reflection methods, the design uses reflected forces from both interaction between master and human and between slave and environment. Experimental results are presented to demonstrate the validity of the proposed design for real-time applications.     

State and Impedance Reflection Based Control Interface for Bilateral Telerobotic System with Asymmetric Delay

In this paper, we investigate state and imped-ance reflection based robust control strategy for bilateral shared telerobotic system under unsymmetrical time varying delay. Shared input for both master and slave robot is designed by combining delayed position and position-velocity signals with impedance reflection properties of the interaction between slave and environment and between human and master robot manipulator. Adaptive control algorithm is proposed to estimate the interaction properties between human and master manipulator and between slave and remote environment. Then, the delayed estimated interaction properties are reflected back to the master and slave robot manipulator to match with the estimated impedance properties of the interaction between human and remote environment. We combine robust term with adaptive control term to deal with the uncertainty associated with gravity loading vector, unmodeled dynamic and external disturbance. The stability conditions with time varying delays are derived by using Lyapunov-Krasovskii functional. Experimental results are given to demonstrate the validity of the proposed design for real-time applications.     

Teleoperation Control of a Position‐Based Impedance Force Controlled Mobile Robot by Neural Network Learning: Experimental Studies

Effective haptic performance in teleoperation control systems can be achieved by solving two major problems: the time‐delay in communication channels and the transparency of force control. The time‐delay in communication channels causes poor performance and even instability in a system. The transparency of force feedback is important for an operator to improve the performance of a given task. This article suggests a possible solution for these two problems through the implementation of a teleoperation control system between the master haptic device and the slave mobile robot. Regulation of the contact force in the slave mobile robot is achieved by introducing a position‐based impedance force control scheme in the slave robot. The time‐delay problem is addressed by forming a Smith predictor configuration in the teleoperation control environment. The configuration of the Smith predictor structure takes the time‐delay term out of the characteristic equation in order to make the system stable when the system model is given a priori. Since the Smith predictor is formulated from exact linear modeling, a neural network is employed to identify and model the slave robot system as a nonlinear model estimator. Simulation studies of several control schemes are performed. Experimental studies are conducted to verify the performance of the proposed control scheme by regulating the contact force of a mobile robot through the master haptic device.     

工业机器人遥操作系统的空间映射与控制策略

针对工业机器人遥操作系统中存在的主从机器人工作空间差异以及运动控制精度与安全问题,提出了一种工作空间映射算法与位置—速度混合控制策略。首先,将遥操作划分为自由运动和交互两个阶段,在自由运动阶段采用映射算法使主从机器人的工作空间高度覆盖,使主机器人可操控的从机器人运动范围最大化。进一步,在交互阶段设计了一种位置—速度混合控制策略对工业机器人的运动进行准确的控制,使主从机器人的实际位置轨迹准确的跟随,并进一步引入反馈引导力以实现安全的控制。最后在Touch-ABB IRB120主从机器人遥操作实验平台上对所提控制方法进行验证,实验结果表明该方法使得主从机器人运动范围在高度覆盖的同时可以保证遥操作控制的精度。     

空间遥操作机器人力反馈实验系统的研究与实现

本文讨论了在空间遥操作机器人系统中实现力反馈的基本结构及控制方案,并 基于虚功原理给出了实现手控器腕关节力反馈的具体算法．     

A haptic interface for computer-integrated endoscopic surgery and training

Haptic feedback has the potential to provide superior performance in computer-integrated surgery and training. This paper discusses the design of a user interface that is capable of providing force feedback in all the degrees of freedom (DOFs) available during endoscopic surgery. Using the Jacobian matrix of the haptic interface and its singular values, methods are proposed for analysis and optimization of the interface performance with regard to the accuracy of force feedback, the range of applicable forces, and the accuracy of control. The haptic user interface is used with a sensorized slave robot to form a master–slave test-bed for studying haptic interaction in a minimally invasive environment. Using the master–slave test-bed, teleoperation experiments involving a single degree of freedom surgical task (palpation) are conducted. Different bilateral control methods are compared based on the transparency of the master–slave system in terms of transmitting the critical task-related information to the user in the context of soft-tissue surgical applications.     

A teleoperation system for micro positioning with haptic feedback

This paper presents the research work on a 1 Degree of Freedom (DOF) macro-micro teleoperation system which enables human operator to perform complex task in micro environment such as cell insertion with the capability of haptic feedback. To reach submicron resolution, a nano-motion piezo actuator was used as the slave robot and a servo DC motor was used as the master robot. Force sensors were implemented at both ends for haptic feedback and a microscope equipped with camera was employed for real-time visual feedback. The hysteresis nonlinearity of the piezo motor was modeled using LuGre friction model and compensated for. A Sliding Mode Based Impedance Controller (SMBIC) was designed at the slave side to ensure position tracking while an impedance force controller was designed at the master side to ascertain tracking of the force. Control parameters were chosen based on Llewellyn stability criteria such that the entire system stays stable against parameter uncertainties and constant time delay. The experimental results demonstrated capability of the proposed control frameworks in desirable tracking of the position and force signals while the entire system remained stable. The results of this study can be used for complex tasks in micron environment such as cell insertion.     

Neural Network Force Control for Industrial Robots

In this paper, we present a hierarchical force control framework consisting of a high level control system based on neural network and the existing motion control system of a manipulator in the low level. Inputs of the neural network are the contact force error and estimated stiffness of the contacted environment. The output of the neural network is the position command for the position controller of industrial robots. A MITSUBISHI MELFA RV-M1 industrial robot equipped with a BL Force/Torque sensor is utilized for implementing the hierarchical neural network force control system. Successful experiments for various contact motions are carried out. Additionally, the proposed neural network force controller together with the master/slave control method are used in dual-industrial robot systems. Successful experiments are carried out for the dual-robot system handling an object.     

PUMA760切割作业力与位置混合控制

本文介绍在 PUMA760 上实现了的切割作业控制系统。我们在 PUMA760 控制系统中引进了力传感器信号,加入了力控制算法,通过调整机器人手部位置,控制机器人与环境物体间的相互作用力。在 VAL 系统中加入了根据传感器信号动作的指令,实现了一种机器人力与位置的混合控制。用扩充后的 VAL 系统编程,成功地使 PUMA760完成了多种材料的切割作业。     

预估控制下的实时网络遥操作移动机器人

构建了能使操作者通过Internet远程实时控制的移动机器人系统．为了补偿网络时延和抵消其对遥操作系统的影响，基于我们以前提出的改进型Smith预估器原理，采用了预估控制策略．为了保证系统稳定性和透明性，基于主从端的传感器信息交换，设计了一个动态模型管理器，其中模型和力反馈误差调节通过模糊控制实现．除了力反馈外，为了增强遥操作的实时性，引入了预估的虚拟显示．为了精确地预测网络时延，提出了一个新颖的时钟同步算法．为了降低时延抖动，结合我们提出的两个算法，实现了数据缓冲策略．最后，通过长距离的网络遥操作实验验证了系统和控制策略的实用性和有效性．