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.     

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.     

遥操作机器人的新型力反馈算法*

在采用液压挖掘机改造的遥操作机器人双向伺服控制系统中,针对大臂和前臂两个自由度构建力反馈控制算法。以准确地获取从端机器人与环境的作用力,使反馈力能够更好地反映从端工作状况为目的,采用构建干扰力补偿项的方法消除干扰力对反馈力的影响;以机器人转角为输入,以空载时检测到的液压缸作用力为输出,通过径向基函数构建干扰力补偿项,此补偿项可对多种因机器人的机械本体动力学特性产生的干扰力之合力进行补偿。实验证明,在以液压机构为从手的双向力反馈系统中,通过构建干扰力补偿项的方法提高力反馈效果的方法是可行的,采用的带有干扰力     

Master-slave intelligent robot telepresence system

In this paper, the analysis and design of master-slave intelligent robot telepresence system are discussed. When the operator acts on the master manipulator, the position and attitude information of the master manipulator are gathered by the computer. After calculating and coordinate transforming, the data are send to the computer of the slave manipulator. Then the slave manipulator-PUMA562 robot follows the master manipulator's movement precisely. Six-dimension force/toque sensor(lord cell) is mounted on the slave manipulator. As the master manipulator and the toque on the slave manipulator are different in structure, the force and the slave manipulator should be send to the master manipulator computer and dissociated by the master manipulator computer. Proper ratio of the force on the master manipulator and the force on the slave manipulator is selected, and distribute to the master manipulator joints. So that the operator could feel the force from the master manipulator, which is obtained by the motors of the joints. The proposed control scheme is introduced to a prototype master-slave system and the experimental results show the validity of the proposed scheme.     

基于神经网络的力觉临场感系统的预测控制

针对力觉临场感系统传输通道中存在时交通讯时廷，造成系统不稳定和操作性能降低的问题，利用前向神经网络建立主机械手、从机械手和环境的模型，并通过神经网络模型预测主机械手速度和从机械手受力，以消除或减少通讯时廷对系统的影响。实验结果表明了该方法的有效性。     

Object handling control among two-wheel robots and a human operator: An empirical approach

This article presents object handling control between two-wheel robot manipulators, and a two-wheel robot and a human operator. The two-wheel robot has been built for serving humans in the indoor environment. It has two wheels to maintain balance and is able to make contact with a human operator via an object. A position-based impedance force control method is applied to maintain stable object-handling tasks. As the human operator pushes and pulls the object, the robot also reacts to maintain contact with the object by pulling and pushing against the object to regulate a specified force. Master and slave configuration of two-wheel robots is formed for handling an object, where the master robot or a human leads the slave robot equipped with a force sensor. Switching control from position to force or vice versa is presented. Experimental studies are performed to evaluate the feasibility of the object-handling task between two-wheel mobile robots, and the robot and a human operator.     

Neuro-fuzzy position control of demining tele-operation system based on RNN modeling

The paper considers the neuro-fuzzy position control of multi-finger robot hand in tele-operation system—an active master–slave hand system (MSHS) for demining. Recently, fuzzy control systems utilizing artificial intelligent techniques are also being actively investigated in robotic area. Neural network with their powerful learning capability are being sought as the basis for many adaptive control systems where on-line adaptation can be implemented. Fuzzy logic on the other hand has been proved to be rather popular in many control system applications providing a rule-base like structure. In this paper, the design and optimization process of fuzzy position controller is supported by learning techniques derived from neural network where a radial basis function (RBF) neural network is implemented to learn fuzzy rules and membership functions with predictor of recurrent neural network (RNN) model. The results of experiment show that based on the predictive capability of RNN model neuro-fuzzy controller with good adaptation and robustness capability can be designed.     

A study on constructing a neuro-interface using the concept of a virtual master–slave system

To ease the control of a nonholonomic robot by a non-expert, a neuro-interface is proposed by using the concept of a virtual master–slave system. The design procedure for the interface is elaborated for the control of nonholonomic two-wheeled robots. In particular, it is shown that if the coordinate transformation from the slave to the master is assumed to be known, the resultant inverse mapping of the master robot can be simply realized by a neural network (NN) with all linear units. The training of the NN is performed by an offline method. As a result, the effectiveness of the proposed method is shown for some simulations to solve a trajectory tracking control problem with a nonholonomic mobile robot.This work was presented, in part, at the 9th International Symposium on Artifical Life and Robotics, Oita, Japan, January 28–30, 2004     

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.     

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.     

空间遥操作机器人主从双边自适应内模控制

天地大时延严重破坏具有力反馈的空间遥操作机器人系统的稳定性和透明性;针对天地大时延和未知的从手操作环境,基于内模控制和自适应控制思想设计一种自适应内模控制器;推导从手环境参数辨识模型,基于带遗忘因子的递推最小二乘滤波辨识环境模型,并利用辨识信息构建自适应内模控制器;基于单参数SNPIDC算法分别设计主手和从手自适应控制器;大量的仿真结果证明,在设计的主从双边自适应内模控制器作用下,空间遥操作机器人系统稳定性好,对未知环境适应能力强,系统透明性好,阻抗匹配程度高,且操作人员临场感强。     

基于网络的机器人远程系统设计与实现

本文首先介绍了机器人远程系统的构成,它主要包括操作者、主机器人、网络、从机器人以及任务对象。然后介绍三种用于远程系统的控制方法:预测显示控制、非时间参照控制、迭代学习控制,论述了各种控制方法的原理。最后介绍了基于WEB的机器人远程系统的结构,较为详细地介绍了里面的几个主要模块。     

Improvement of Tactile Sensation of a Bilateral Forceps Robot by a Switching Virtual Model

Interest in tele-operation started many decades ago. Today, tele-operation is becoming a reality with improved tele-communication systems. Bilateral control is also a subset of tele-operation. Usually in bilateral control, force position information is exchanged between a master and a slave. This structure successfully transmits tactile sensation from the slave environment to the master operator. A bilateral control system is capable of transmitting tactile sensation to the master side. This capability is also known as the transparency of the system. If bilateral control is used in a surgery, ideally the medical doctor should sense the feeling coming from the body tissue together with the reaction force of the special surgical tool. We have previously proposed a method to eliminate the special surgical tool with a simple tool tip and a virtual tool model. However, a physical tool as well as the proposed virtual tool add impedance to the system. Even if a virtual model is used, impedances are essential to carry out the tool action. In this paper, a novel method is proposed to switch off and on the virtual tool model when the tool action can be replaced by the environmental reaction forces. An experiment was carried out using a bilaterally controlled forceps robot. Results show the applicability of the idea.     

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.     

3RPS/UPS并联机器人神经网络观测器反演控制

为了增强4D互动立体游戏仿真模拟平台的刚度和运动性能，将带冗余结构的3RPS/UPS并联机器人应用其中。首先对其结构进行介绍及逆运动学分析，然后针对传统PID控制在控制精度方面的不足，提出了一种基于神经网络观测器的反演控制方法。最后利用MATLAB对其进行建模以及系统仿真实验，并与传统PID控制以及一般的RBF神经网络自适应控制进行对比。由仿真结果可以看出，根据RBF神经网络观测器估计系统状态值，并应用反演控制理论设计控制器，能实现很好的状态观测，从而实现无需速度信号的位置跟踪。该方法也能够在一定程度上提高精度，且其整体控制效果优于传统PID控制器，相比于一般的RBF神经网络自适应控制也有了一定的改进。     

遥操作护理机器人系统的操作者姿态解算方法研究

设计了一种遥操作护理机器人系统,为实现从端同构式机器人的随动运动控制,对主端操作者人体姿态解算方法进行了研究.首先,构建由惯性传感单元构成的动作捕捉系统,对用作从端机器人动作指令的操作者人体姿态信息进行采集,采用四元数法对人体运动原始数据进行初步求解.其次,将四元数法得到的姿态数据解算成依据仿人结构设计的护理机器人各关节运动的目标姿态角,实现人体姿态到机器人动作的同构性映射.最后,为验证本文所提姿态解算方法的性能,设计了操作者控制护理机器人完成递送和拿取药瓶动作的实验.结果表明,本文姿态解算方法的解算性能与参考系统基本相同;在操作者动作姿态快速变化的时间段,系统仍可获得较高精度的目标姿态数据,其误差在动态条件下依旧能保持在2%以下;护理机器人可较好地实时复现操作者的人体动作.本文方法能满足机器人进行一般护理作业时对人体姿态数据处理的快速性和准确性要求.     

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。     

A Telepresence-Guaranteed Control Scheme for Teleoperation Applications of Transferring Weight-Unknown Objects

Currently, most teleoperation work is focusing on scenarios where slave robots interact with unknown environments. However, in some fields such as medical robots or rescue robots, the other typical teleoperation application is precise object transportation. Generally, the object’s weight is unknown yet essential for both accurate control of the slave robot and intuitive perception of the human operator. However, due to high cost and limited installation space, it is unreliable to employ a force sensor to directly measure the weight. Therefore, in this paper, a control scheme free of force sensor is proposed for teleoperation robots to transfer a weight-unknown object accurately. In this scheme, the workspace mapping between master and slave robot is firstly established, based on which, the operator can generate command trajectory on-line by operating the master robot. Then, a slave controller is designed to follow the master command closely and estimate the object’s weight rapidly, accurately and robust to unmodeled uncertainties. Finally, for the sake of telepresence, a master controller is designed to generate force feedback to reproduce the estimated weight of the object. In the end, comparative experiments show that the proposed scheme can achieve better control accuracy and telepresence, with accurate force feedback generated in only 500 ms.      

基于GA优化RBF神经网络的机器人轨迹规划

针对机器人在不确定环境下末端执行器运动轨迹的准确性及平稳性问题,采用基于遗传算法(GA)优化径向基函数(RBF)神经网络的轨迹规划方法对Kinova Mico2机器人进行轨迹规划研究。介绍了机器人的相关参数及坐标系、建立了D-H矩阵和运动学模型。提取机器人实际抓取物品的直线轨迹并等分插补,用GA优化并实时在线更新RBF神经网络的权值,以更优的权值参数建立新的RBF网络。研究结果表明:相比优化前,基于GA优化RBF的规划轨迹逼近误差小且平滑稳定,仿真结果较为稳定,轨迹规划的可行性满足机器人实际抓取工作的需要。     

Adaptive RBF neural network control of robot with actuator nonlinearities

In this paper, an adaptive neural network control scheme for robot manipulators with actuator nonlinearities is presented. The control scheme consists of an adaptive neural network controller and an actuator nonlinearities compensator. Since the actuator nonlinearities are usually included in the robot driving motor, a compensator using radial basis function (RBF) network is proposed to estimate the actuator nonlinearities and eliminate their effects. Subsequently, an adaptive neural network controller that neither requires the evaluation of inverse dynamical model nor the time-consuming training process is given. In addition, GL matrix and its product operator are introduced to help prove the stability of the closed control system. Considering the adaptive neural network controller and the RBF network compensator as the whole control scheme, the closed-loop system is proved to be uniformly ultimately bounded (UUB). The whole scheme provides a general procedure to control the robot manipulators with actuator nonlinearities. Simulation results verify the effectiveness of the designed scheme and the theoretical discussion.