A compliant end-effector coupling for vertical assembly: design and evaluation

Many manipulation tasks require compliance, i.e. the robot's ability to comply with the environment and accomplish force as well as position control. Examples are constrained motion tasks and tasks associated with touch or feel in fine assembly. Few compliance-related tasks have been automated, and usually by active means of active compliance control: the need for passive compliance offered by the manipulator itself has been recognized and has led to the development of compliant end-effectors and/or wrists. In this paper we present a novel passively compliant coupling, the compliant end-effector coupling (CEEC), which aids automated precision assembly. It serves as a mechanical interface between the end of the robot arm and the end-effector. The coupling has 6 degrees of freedom. The design of the coupling is based on a “lock and free” assembly idea. The coupling is locked and behaves like a stiff member during robot motion, and is free (compliant) during constrained motion. It features an air bearing, a variable stiffness air spring and a center-locking mechanism. The end-effector assembly, being centrally unlocked, will float within the designed compliance limits assisted by the air bearing. These frictionless and constraint-free conditions facilitate a fast correction of any initial lateral and angular misalignments. In a peg insertion assembly, such accommodation is possible provided that the tip of the peg is contained within the chamfer of the hole. A variable stiffness air spring was incorporated in the design to allow variable and passive vertical compliance. This vertical compliance allows the accommodation of angular and vertical errors. The center-locking mechanism will return the end-effector assembly to its initial position upon an error correction. In a robot application program, the CEEC can be locked during rapid motion to securely transport a part or be set free during assembly or disassembly processes when the motions are constrained.     

A peg-in-hole robot assembly system based on Gauss mixture model

Using robots to assemble parts has always been a research hotspot, but the traditional analysis model of assembly mainly focuses on establishing the linear equation between the feedback force and the relative position of the peg and hole, which leads to high requirements for the material properties and geometric parameters of the assembly parts. In this paper, a new assembly strategy is proposed that learns skills from manual teaching to carry out the assembly process. A Gauss mixture model and regression is used to fit the teaching data, and then, the compliance control method is applied to conduct assembly when the geometric profile parameters and material elastic parameters of the assembly are inaccurate. Finally, the experiment was implemented under the tolerance is 0.18 mm, and the success rate reaches 100%. These findings verified the effectiveness of the compliance control method.     

基于分层控制的移动机器人最优运动规划

研究移动机器人在带有多移动障碍物的动态环境中的运动规划问题，基于分层最控制理论，提出了一种新的运动规划方法来解决移动机器人的导航与避障问题，仿真实例证明了该方法的有效性。     

基于ARCC的无倒角轴孔装配实验研究

本文根据ＡＲＣＣ的工作原理，对机器人的无倒角插轴入孔装配作业进行了实验研究，介绍了实验系统的组成，ＡＲＣＣ系统闭环刚度和去耦力反馈矩阵的设置。给出在Ｍｏｖｅｍａｓｔｅｒ－ＥＸ和ＰＵＭＡ５６２机器人上进行自动寻孔和插轴入孔的实验结果。     

3D视觉结合图像检测与导纳控制的圆轴孔零件机器人装配

面向机器人柔顺装配圆轴与圆孔零件,建立基于3D、单目视觉与导纳控制的机器人自动装配系统,提出基于三维点云的轴线位姿估计算法、图像深度学习目标检测、导纳控制结合的圆轴孔零件的装配策略.针对3D视觉估计圆孔零件位姿问题,重点研究基于三维点云的轴线位姿估计算法.首先,介绍三维点云关键点选取方法;然后,以点云表面法线与轴线的几何约束为基础,提出并分析轴线粗估计的算法;最后,在轴线粗估计的基础上,提出并分析基于迭代鲁棒最小二乘的轴线位姿优化的算法.实验结果表明:轴线位姿估计的角度均方根误差为0.248°,位置均方根误差为0.463 mm,与现有流行的轴线估计方法相比,所提方法的精度更高,使装配策略很好地满足了机器人圆形轴孔零件装配的精度高、稳定可靠的要求.     

基于单目显微视觉的微球姿态测量方法

微零件的姿态测量对微装配具有重要的作用.但对于微球零件,其姿态的精确测量存在困难,影响了装配精度.针对带有微孔的微球,本文提出了一种基于单目显微视觉的微球姿态高精度测量方法.设计了一种由粗到精的微孔检测算法,实现了高精度的微孔定位.通过对相机光轴方向的标定,在相机运动后对微球图像坐标进行补偿,提高了在相机坐标系下的微球定位精度.通过对微球和微孔的精确定位,计算出微球球心与微孔圆心的空间相对位置,实现了相机坐标系下高精度的微球姿态测量.同时,根据标定出的相机坐标系与调整平台坐标系之间的旋转关系,将微球姿态转换到调整平台坐标系.实验结果表明,最大姿态测量误差0.3度,验证了本文方法的有效性.     

矩形销孔零件装配策略及几何约束

本文针对典型三维矩形销孔零件装配，提出一种实用，简单的装配策略，并推导出此装配策略销孔所产生接触状态几何约束方程，为三维矩形销孔零件的成功装配提供了理论依据，同时，观察所得到的几何约束方程和实际接触系统的自由度数，可以发现两者在理论上是一致的。     

基于RBFNN的非线性多变量系统的控制策略研究

针对非线性多变量大时滞系统,研究了一种基于神经网络的智能控制策略。它以经典的PID控制为基础,通过神经网络参数整定,用于多变量系统的解耦控制;用预测模型超前预测系统输出,以克服系统的时滞。该文给出了网络的结构和算法,对一组二变量强耦合时变系统进行了仿真。仿真结果表明,控制器能根据系统运行状态获得对应于某种最优控制律下的PID参数,解耦后的系统具有较好的动态和静态性能。该方法具有控制精度高、响应速度快的优点,并具有较强的自适应性和鲁棒性。     

被动柔顺装置选用策略及其应用软件

本文从理论上分析了选择被动柔顺装置ＲＣＣ进行成功装配的种约束条件，并运行分析结果开发了被动柔顺装置选用策略的软件，它能针对已有的机器人装配系统，根据轴孔的配合尺寸和成功装配条件，从已建立的被动柔顺装置技术参数库选择与之相匹配的被动柔顺装置，进行装配作业。     

SCARA based peg-in-hole assembly using compliant IPMC micro gripper

Robotic assembly is difficult as there always exist position errors between two mating parts. Compliance is added in a selective compliant assembly robot arm (SCARA) in the form of a two ionic polymer metal composite (IPMC) fingers based micro gripper. This micro gripper is integrated at the end effector position of a SCARA robot. Peg-hole interaction is analytically modeled and based on it the force required to correct the lateral and angular errors by IPMC is calculated. A proportional-derivative (PD) controller is designed to actuate the IPMC to get the desired force for correcting the peg position before assembly. Simulations and experiments were carried out by developing an IPMC micro gripper and using it to analyze various cases of peg in hole assembly. The experimental results prove that adding compliance through IPMC helps in peg-in-hole assembly.     

基于RBF神经网络逆系统的机械手解耦控制策略

针对机械手系统具有非线性时变、多变量、强耦合的特点,提出一种基于RBF神经网络逆系统的机械手解耦控制策略。首先证明了系统的可逆性,进一步通过神经网络在线逆辨识建立机械手的神经网络逆系统模型,并将辨识得到的逆模型作为控制器模型与机械手系统串联,构成伪线性复合系统,实现了将具有强耦合特性的多变量输入/输出机械手系统解耦成单个独立的伪线性对象。最后以两关节机械手为仿真对象进行了仿真,仿真结果验证了本方案的有效性和可行性。     

特征轨迹法解耦活套高度和张力控制系统

针对板带热连轧机活套高度和张力控制系统，给出一种新的多变量解耦控制策略．利用特征传递函数的轨迹来判定闭环系统的稳定性．利用失配角度曲线来检验系统的关联性．采用特征轨迹方法设计出的解耦控制器在最大程度上减弱了活套高度和张力控制系统的关联．最后，用Matlab中的系统仿真工具箱Simulink对活套多变量控制系统进行仿真实验，结果表明解耦后的活套控制系统可获得更好的控制性能．     

基于变结构控制的反鱼雷鱼雷姿态解耦控制

反鱼雷鱼雷由于攻击对象的特性要求具备较好的机动性,鱼雷作姿态机动时控制通道间的耦合,使得传统鱼雷控制技术无法达到控制目的,本文在构建反鱼雷鱼雷姿态运动子系统状态方程的基础上,采用变结构控制策略设计了反鱼雷鱼雷姿态控制器,理论推导和仿真表明:所设计的控制器能够使得反鱼雷鱼雷姿态角稳定地跟踪弹道所需航向角,实现了鱼雷姿态子系统间的解耦控制.     

Dynamics of cooperating manipulators for fixtureless assembly and robust control based on fuzzy logic

This article presents the modeling of the dynamics of two cooperating robot manipulators performing assembly tasks such as the peg‐in‐hole job while coordinating the payload along the desired trajectory. The system has uncertainty due to unknown mass and moment of inertia of the manipulators and the payload, and has disturbances due to coupling forces between manipulators and due to frictional forces during the assembly process. To control the uncertain system, a robust control algorithm with computed torque control is proposed. The robust control algorithm includes fuzzy logic that has two functions: one is to regulate the magnitude of the input torque of the manipulators to not go beyond the torque saturation. The other function is to reduce trajectory tracking errors. To validate the proposed control algorithm, a numerical example using dual 3 degree‐of‐freedom manipulators is shown. ©1999 John Wiley & Sons, Inc.     

机器人化装配

机器人化装配已成为机器人研究与应用的一个热点．１０年前，文献［１］已就此作了综述，本文旨在补充这以后的新成果．首先，简单介绍各种装配机器人和装配设计方法．然后对手腕的各种高精度插轴入孔措施，尤其是高速装配控制进行了详述．文末提出了一些研究方向．     

冻结站中盐水流量和温度的模糊解耦控制

针对现有冻结站盐水流量和温度控制方法存在控制精度低的问题,提出了一种盐水流量和温度的模糊解耦控制方法。首先根据盐水流量和温度控制精度的不同,采用模糊控制法对流量和温度进行模糊控制;然后分析和研究盐水流量和温度间的耦合关系,采用解耦控制法对模糊控制输出的流量和温度进行解耦控制。仿真结果表明,该控制方法无需对盐水流量和温度建立精确数学模型,可实现盐水流量和温度的解耦控制,控制精度高。     

基于小生境遗传算法的多维传感器动态解耦方法

多维传感器的动态耦合是指某一方向的动态输出信号中,含有其他方向输入量的影响,该项误差是影响传感器测量精度的一个重要因素.基于小生境遗传算法,提出了多维传感器动态解耦方法.该方法利用传递函数矩阵分析法的解耦思想,可以根据多维传感器的标定数据,应用系统辨识方法和小生境遗传算法直接求解解耦网络并进行优化,克服了原经典方法中对传感器模型精确已知的要求,具有一定的鲁棒性.仿真结果证明了该方法的正确性和有效性.     

凹印机套色系统的自抗扰解耦控制

无轴凹印机套色系统的运行过程中存在各种形式的扰动,且各色组的套色误差通过张力传递相互耦合,严重影响系统的套准精度,必须进行解耦和抗扰控制.本文在建立了凹印机套色系统的近似数学模型的基础上,将扩张状态观测器与前馈相结合,提出了一种新的自抗扰控制策略.把一个强耦合、大扰动、模型不确定的复杂非线性系统动态补偿为近似的二阶线性系统,降低了系统的控制难度,改善了系统的动态性能,并使系统具有自抗扰的能力.仿真结果表明,与传统的前馈控制和非线性控制相比,该算法使系统控制误差的收敛速度加快,动态响应性能更优越,实现了色组间的解耦并使系统具有良好的抗扰性能.     

Multivariable Intelligent Decoupling Control System and its Application

Many industrial processes have compositive complexities including multivariable, strong coupling, nonlinearity, time-variant and operating condition variations. Combining multivariable adaptive decoupling control with neural networks, this paper presents a multivariable neural network-based decoupling control algorithm. This control algorithm is integrated with distributed control technique and intelligent control technique, and a three-leveled intelligent decoupling control system consisting of basic control level, coordinating control level, and management and decision level is developed. The configuration and function of the control system are discussed in detail. This system has been successfully applied in ball mill pulverizing systems of 200MW power units, and remarkable benefits have been obtained.     

Constraint network analysis of 3-dimensional insertion tasks

Flexible assembly machines may be improved and costs reduced by relaxing constraints on part fixturing accuracy and employing compliant devices in assembly effectors. An effector-mounted remote center compliance device that corrects for spatial misalignments of prismatic parts of general cross section has recently been demonstrated. This spatial RCC was designed by planning for the possible fine motion assembly contact states given a range of initial position and orientation uncertainty between the mating parts. These fine motion contact states are arranged in a constraint network in the fashion of a Petri net controller, however, the control transitions are mediated by compliances reacting to contact forces rather than from force sensing and discrete event controller schemes. A path through the constraint network of a square peg and hole task is found from initial to final assembly states. This path defines a single compliance relationship that is realized with a practical assembly device. Extensions of this design technique to other assembly tasks are discussed.