基于双向LSTM神经网络的机器人机械臂智能轨迹控制系统

针对机械臂智能轨迹控制易受其不确定性与外部扰动影响的问题,设计基于双向LSTM神经网络的机器人机械臂智能轨迹控制系统,实现对机器人机械臂轨迹的实时控制,使其能够快速响应环境变化和执行新任务。利用远程控制模块输入用户设定的机械臂运行参数;主控制模块的主控单元利用双向LSTM神经网络,估计机械臂的不确定性数据,并依据用户设定的相关参数,结合自适应滑模控制器,设计机械臂智能轨迹复合控制律,由主站通讯板卡传输至伺服模块;伺服模块接收智能轨迹复合控制律后,利用伺服驱动器启动直流力矩电机,按照复合控制律驱动机械臂运动,完成机械臂智能轨迹控制。实验证明：该系统可有效完成机器人机械臂智能轨迹控制,且控制后的运行轨迹与设定轨迹非常接近;经过控制后的机械臂关节角度变化曲线较平滑,且连续,具备较优的智能轨迹控制效果。     

空间机械臂在轨维修的视觉伺服操控策略

首先分析了空间机械臂/机械手系统在轨旋拧螺钉任务的尺寸链误差．为修正微重力环境和机械臂、模拟维修单机在轨安装导致的位姿偏差,特别是消除机械手抓取电动工具导致的随机误差影响,提出了航天员在轨标定电动工具位姿/全局相机测量电动工具位姿并引入机械臂进行视觉伺服的控制策略．设计了视觉伺服控制器,给出了该控制算法的收敛证明和稳定性分析．通过在轨实施,该策略实现了机械臂/机械手系统拧松螺钉时位姿误差不超过3 mm/2°的任务要求．     

一种景物平面法向已知条件下的单应矩阵快速分解算法

针对机器人混合视觉伺服控制中的摄像机位姿估计问题,分析欧氏单应矩阵的基本性质,提出一种景物平面法向已知情况下的欧氏单应矩阵快速分解算法.仿真实验结果表明,所提出的算法实现简单,避免了矩阵奇异值分解运算和从多组分解结果中确定惟一解的过程,能够较好地满足视觉伺服控制系统的实时性要求.     

架空输电线路巡线机器人越障视觉伺服控制

为解决巡线机器人越障问题，设计了基于图像的越障视觉伺服控制方案．采用傅里叶描述子构造了具有平移、旋转、尺度缩放及起始点不变性的轮廓形状特征向量，实现了驱动轮的识别；进而抽取图像特征和估计驱动轮—相线在图像空间中的相对位姿，并设计了带有死区的比例控制律来实现驱动轮—相线“对中”视觉伺服控制．模拟线路实验结果表明，该方案能可靠地完成巡线机器人驱动轮—相线“对中”控制任务．     

基于GPR和KRR组合模型的机械臂抓取研究

为了避免机械臂自主抓取方法中普遍存在的运动学求逆耗时和视觉系统标定计算复杂度高的问题,提出一种基于高斯过程回归(GPR)和核岭回归(KRR)组合模型的机械臂抓取方法。在学习阶段,训练基于Mask-RCNN的目标检测和实例分割算法及GPR和KRR的机械臂抓取策略;在抓取阶段,首先使用目标检测和实例分割算法获取目标物体的位姿,然后根据目标物体位姿和机械臂关节角的映射关系,结合GPR和KRR的组合模型预测出机械臂关节角并控制机械臂完成抓取任务。实验结果表明:所提出的方法无需视觉系统的标定和机械臂运动学求逆,能够准确地获取目标物体的位姿,AUBO i5机械臂验证,本方法能够实现对目标物体较为准确的抓取。     

基于位置的机械臂视觉伺服预测控制

基于位置的机械臂视觉伺服控制需要解决位姿估计和目标跟踪控制问题.为了克服机械臂运动过程中的运动空间约束,提出了基于滚动时域估计(MHE)的机械臂位姿估计方法.在位姿估计的基础上,利用对偶梯度上升方法以及拉格朗日乘子处理具有控制输入约束的优化问题,并给出了基于迭代线性二次调节(iLQR)的视觉伺服预测控制器设计方法.进一步,采用极点配置方法设计了扩张状态观测器(ESO)用于解决估计误差和线性化误差引起的扰动问题.进而,给出了保证闭环系统稳定的充分条件.最后,通过仿真对比验证了本文所提算法的有效性和优越性.     

基于线特征与内区域特征的视觉伺服解耦控制

针对多自由度机械臂快速趋近任意四边形态目标的视觉伺服控制难题，提出了结合线特征与内区域特征的机器人视觉伺服解耦控制方法.构建了目标内区域特征以指导相机的平移运动速率，利用目标的线特征给出相机的旋转角速率，并通过引入内区域特征的矢量补偿和质心坐标的位置补偿，实现了平移和旋转控制的部分解耦.最后，对机器人视觉伺服控制系统进行了稳定性分析.仿真验证结果表明所提方法能控制相机以较快而平滑的动作收敛到期望位姿，且在相机光轴与目标平面近似垂直的条件下能较好地克服深度估计造成的不确定性问题.     

一种基于运动的线结构光视觉测量系统标定方法

针对船体分段数字化测量提出了一种基于直角坐标机器人的线结构光视觉测量系统.通过控制机器人的运动,安装在其末端的线结构光视觉传感器对固定的平面靶标成像,实现了摄像机坐标系与机器人坐标系位姿关系标定;同时利用交比不变性原理获取平面靶标上的特征点坐标,完成了结构光平面参数的标定.该方法易于实现,通过对已知的标准工件进行测量实...     

基于视觉伺服的工业机器人位姿跟踪方法

目前，工业机器人工作大多是基于先精确示教后运行的，工作效率较低，因此采用视觉提高机器人的智能水平，以实现对目标物体的自动检测和定位抓取，具有重要的现实意义和研究价值。文章以机器人视觉伺服为研究对象，利用视觉伺服使机器人末端跟踪标定板的位姿，包括摄像机标定、机器人手眼标定、机器人控制，可为机械臂的定位抓取提供帮助。     

基于双目视觉的机器人目标定位与机械臂控制

为了更好地与复杂多变的非结构化环境进行交互,完成对目标物体的识别和抓取,提出了一种应用于服务机器人平台的基于双目视觉的仿人机械臂控制方法。文中首先用D-H方法对机械臂进行建模,并对这个模型做了改进,给出了一种更加简便的3+1自由度仿人机械臂的逆解算法,采用基于双目视觉与颜色分割的目标识别方法;然后根据识别出的目标三维坐标信息控制机械臂完成抓取任务;最后,本方法在家庭服务机器人上得到了验证,机器人能够完成对目标物体的识别和抓取动作。     

Model Predictive Control for Constrained Image‐Based Visual Servoing in Uncalibrated Environments

This paper presents a novel approach for image‐based visual servoing (IBVS) of a robotic system by considering the constraints in the case when the camera intrinsic and extrinsic parameters are uncalibrated and the position parameters of the features in 3‐D space are unknown. Based on the model predictive control method, the robotic system's input and output constraints, such as visibility constraints and actuators limitations, can be explicitly taken into account. Most of the constrained IBVS controllers use the traditional image Jacobian matrix, the proposed IBVS scheme is developed by using the depth‐independent interaction matrix. The unknown parameters can appear linearly in the prediction model and they can be estimated by the identification algorithm effectively. In addition, the model predictive control determines the optimal control input and updates the estimated parameters together with the prediction model. The proposed approach can simultaneously handle system constraints, unknown camera parameters and depth parameters. Both the visual positioning and tracking tasks can be achieved desired performances. Simulation results based on a 2‐DOF planar robot manipulator for both the eye‐in‐hand and eye‐to‐hand camera configurations are used to demonstrate the effectiveness of the proposed method.     

基于遗传算法模式匹配的机器人实时视觉伺服

对于机器人手臂来讲 ,对工作环境的识别是完成一个智能任务的最重要的问题之一 .因为这种智能可以使它工作在一个变化的环境中 .本文提出了一种新的机器人手臂的控制策略 ,可以利用视觉信息来指导机器人的手臂在它的工作空间中捡起一个已知形状但任意位置和方向的物体 .在对物体的搜索过程中 ,利用基于视觉闭环的视觉伺服来完成对机器人手臂的运动控制 .本系统利用遗传算法 (Genetic Algorithm ,GA)和模式匹配技术完成对搜索空间的搜索并获得了良好的结果 .本文完成了对带有两连杆手臂的视觉伺服系统的仿真 ,仿真结果证明了算法的有效性     

GA-pattern matching-based manipulator control system for real-time visual servoing

_—In robotic applications, tasks of picking and placing are the most fundamental ones. Also, for a robot manipulator, the recognition of its working environment is one of the most important issues to do intelligent tasks, since this aptitude enables it to work in a variable environment. This paper presents a new control strategy for robot manipulators, which utilizes visual information to direct the manipulator in its working space, to pick up an object of known shape, but with arbitrary position and orientation. During the search for an object to be picked up, vision-based control by closed-loop feedback, referred to as visual servoing, is performed to obtain the motion control of the manipulator hand. The system employs a genetic algorithm (GA) and a pattern matching technique to explore the search space and exploit the best solutions by this search technique. The control strategy utilizes the found results of GA-pattern matching in every step of GA evolution to direct the manipulator towards the target object. We named this control strategy step-GA-evnlution. This control method can be applied for manipulator real-time visual servoing and solve its path planning problem in real-time, i.e. in order for the manipulator to adapt the execution of the task by visual information during the process execution. Simulations have been performed, using a two-link planar manipulator and three image models, in order to find which one is the best for real-time visual servoing and the results show the effectiveness of the control method.     

Uncalibrated Eye-to-Hand Visual Servoing Using Inverse Fuzzy Models

A new uncalibrated eye-to-hand visual servoing based on inverse fuzzy modeling is proposed in this paper. In classical visual servoing, the Jacobian plays a decisive role in the convergence of the controller, as its analytical model depends on the selected image features. This Jacobian must also be inverted online. Fuzzy modeling is applied to obtain an inverse model of the mapping between image feature variations and joint velocities. This approach is independent from the robot's kinematic model or camera calibration and also avoids the necessity of inverting the Jacobian online. An inverse model is identified for the robot workspace, using measurement data of a robotic manipulator. This inverse model is directly used as a controller. The inverse fuzzy control scheme is applied to a robotic manipulator performing visual servoing for random positioning in the robot workspace. The obtained experimental results show the effectiveness of the proposed control scheme. The fuzzy controller can position the robotic manipulator at any point in the workspace with better accuracy than the classic visual servoing approach.     

Robust exponential stabilization of nonholonomic wheeled mobile robots with unknown visual parameters

The visual servoing stabilization of nonholonomic mobile robot with unknown camera parameters is investigated.A new kind of uncertain chained model of nonholonomic kinemetic system is obtained based on the visual feedback and the standard chained form of type (1,2) mobile robot.Then,a novel time-varying feedback controller is proposed for exponentially stabilizing the position and orientation of the robot using visual feedback and switching strategy when the camera parameters are not known.The exponential stability of the closed-loop system is rigorously proven.Simulation results demonstrate the effectiveness of the method proposed in this paper.     

A new formulation of visual servoing based on cylindrical coordinate system

Image-based visual servoing is a flexible and robust technique to control a robot and guide it to a desired position only by using two-dimensional visual data. However, it is well known that the classical visual servoing based on the Cartesian coordinate system has one crucial problem, that the camera moves backward at infinity, in case that the camera motion from the initial to desired poses is a pure rotation of 1800 around the optical axis. This paper proposes a new formulation of visual servoing, based on a cylindrical coordinate system that can shift the position of the origin. The proposed approach can interpret from a pure rotation around an arbitrary axis to the proper camera rotational motion. It is shown that this formulation contains the classical approach based on the Cartesian coordinate system as an extreme case with the origin located at infinity. Furthermore, we propose a decision method of the origin-shift parameters by estimating a rotational motion from the differences between initial and desired image-plane positions of feature points.     

A review on vision-based control of flexible manipulators

In this review, recent developments in the field of flexible robot arm control using visual servoing are reviewed. In comparison to rigid robots, the end-effector position of flexible links cannot be obtained precisely enough with respect to position control using kinematic information and joint variables. To solve the task here the use of a vision sensor (camera) system, visual servoing is proposed to realize the task of control flexible manipulators with improved quality requirements. The paper is organized as follows: the visual servoing architectures will be reviewed for rigid robots first. The advantages, disadvantages, and comparisons between different approaches of visual servoing are carried out. The using of visual servoing to control flexible robot is addressed next. Open problems such as state variables estimation as well as the combination of different sensor properties as well as some application-oriented points related to flexible robot are discussed in detail.     

A real-time fuzzy reasoning based control system for catching a moving goldfish

Designing a real-time visual tracking system to catch a goldfish is a complex task because of the large amount of streaming video data that must be transmitted and processed immediately when tracking the goldfish. Usually, building such visual servoing systems requires the application of high-cost specialized hardware and the development of complicated visual control software. In this paper, a novel low-cost, real-time visual servo control system is presented. The system uses stereo vision consisting of two calibrated cameras to acquire images of the goldfish, and applies the continuously adaptive mean shift (CAMSHIFT) vision-tracking algorithm to provide feedback of a fish’s real-time position at a high frame rate. It then employs a 5-axis robot manipulator controlled by a fuzzy reasoning system to catch the fish. This visual tracking and servoing system is less sensitive to lighting influences and thus performs more efficiently. Experiments with the proposed method yielded very good results, as the system’s real-time 3D vision successfully tracked two fish and guided the manipulator, which has a net attached to its end effector, to catch one of them.     

基于图像矩与神经网络的机器人四自由度视觉伺服

针对传统的视觉伺服方法中图像几何特征的标记、提取与匹配过程复杂且通用性差等问题,本文提出了一种基于图像矩的机器人四自由度(4DOF)视觉伺服方法.首先建立了眼在手系统中图像矩与机器人位姿之间的非线性增量变换关系,为利用图像矩进行机器人视觉伺服控制提供了理论基础,然后在未对摄像机与手眼关系进行标定的情况下,利用反向传播(BP)神经网络的非线性映射特性设计了基于图像矩的机器人视觉伺服控制方案,最后用训练好的神经刚络进行了视觉伺服跟踪控制.实验结果表明基于本文算法可实现0.5 mm的位置与0.5°的姿态跟踪精度,验证了算法的的有效性与较好的伺服性能.