基于宏微机器人的焊缝跟踪研究

提出了一种基于宏微运动机器人的焊缝跟踪方法．首先,通过若干点的简单示教获得焊缝位置信息,并通过拟合建立焊缝模型．在该模型的基础上,对机器人的宏动进行运动规划．采用激光结构光视觉测量焊缝坐标,并根据焊缝图像偏差控制机器人的微动．结合机器人的宏动规划运动和微动自动调整,实现大范围、高精度的焊缝跟踪．利用宏微运动平台进行了焊缝跟踪实验,实验结果验证了所提出方法的有效性．     

移动机器人路径规划问题的解决方案

针对机器人在未知环境中对障碍物的发现与学习以及对路径的规划问题,提出一个新的解决方案,即增加机器人对环境信息的提取并建立地图模型的过程,采用判断当前位置与目标位置的连接线段与所建立的地图模型中障碍物冲突关系的方法,将机器人的行为分为两类：沿障碍物边界行走或向目标点位置移动。同时提出一种新的机器人面对障碍物时对运动方向的计算、选择方法与一种简化学习障碍物信息并建立地图模型的方法。     

救援机器人目标跟踪控制的研究

在分析了腿轮式救援机器人运动原理的基础上,建立了机器人的动力学模型,并根据该模型提出了一种基于线性反馈控制的目标跟踪控制方法,详细介绍了该控制方法的实现。实验结果证明了所提出控制方法的有效性。     

逆变器非线性运行的同步电动机无源性控制研究

通过严格的理论分析和数学计算,建立了同步电动机在同步坐标系中E-L方程形式的数学模型,给出了逆变器的非线性模型。以这些模型为基础,研究并设计了在负载转矩(转速、位置)为任意时变未知情形下同步电动机的无源性渐近跟踪控制器。该控制器在实现机械控制目标的同时实现了对磁场方向和磁场幅值的控制,即同时实现了磁场矢量控制。仿真结果表明,该无源性控制器可以很好地实现转速和位置的跟踪控制,具有很高的动态控制性能和鲁棒特性。     

采用测地线活动轮廓模型检测与跟踪运动目标

水平集几何活动轮廓模型能较好地适应曲线的拓扑变化.为了跟踪和获取刚体和非刚体运动目标的轮廓信息,提出了一种基于改进测地线活动轮廓(GAC)模型和Kalman滤波相结合的算法以检测和跟踪运动目标.该算法首先采用高斯混合模型和背景差分获取目标的运动区域,在运动区域内采用引入距离规则化项的GAC模型进行曲线演化,使改进GAC模型在运动目标的真实轮廓处收敛;然后通过结合Kalman滤波预测目标下一帧的位置,实现对目标轮廓跟踪.实验结果表明,该方法适用于刚体和非刚体目标,在部分遮挡的情况下也能保持良好的检测和跟踪效果.     

基于平面模板的机器人双目标定与目标定位

视觉是机器人获取外界信息最主要的途径,通过视觉系统准确地定位目标物体是机器人控制中的关键技术。为了使机器人准确地获取目标物体的位置,文中采用平面模板法对双目摄像头进行标定,构建机器人坐标系,完成对目标物体的定位。经过标定,双目摄像头可以获取目标物体在空间坐标系中的位置,经过坐标系转换,可以获得目标物体在机器人世界坐标系中的坐标值,该值是机器人实现对目标物体伺服跟踪和抓取的重要数据。最后,本方法在家庭机器人上得到了验证,机器人能够准确地定位目标物体。     

仿海蟹机器人浮游步态动力学建模与运动控制

为实现足桨耦合推进仿海蟹机器人在未知海流扰动作用下对目标点的跟踪控制,对仿海蟹机器人浮游步态的动力学和运动控制进行研究.综合考虑重力、浮力、游泳足拍动产生的推力以及水动力的影响,建立了仿海蟹机器人水下复杂环境的动力学模型.在此基础上,设计了一种基于指数趋近律的滑模变结构控制器,将游泳足上下拍翼运动和摇翼运动的相位差作为被控量,对机器人的转艏角速率进行控制.通过李亚普诺夫直接法,证明该系统可实现全局渐近稳定.最后进行了单一目标点和多目标点跟踪运动仿真和实验,结果表明:该方法可以使机器人具有良好的目标点跟踪能力,并对系统动力学参数不确定性及外界扰动具有较高鲁棒性.     

基于活动轮廓的机器人视觉伺服

本文提出了基于活动轮廓的视觉伺服反馈控制方法．利用活动轮廓对运动物体的图像进行实 时跟踪,抽取物体图像的边缘信息,并以此信息控制摄像头的运动,达到机器人定位、跟踪 等目的．该方法跟踪精度高,鲁棒性好,便于实时跟踪．实验结果表明了该方法的正确性和 有效性．     

机器人系统的加权快速终端滑模主动容错控制

针对受执行器故障的非线性机器人系统,提出一种加权快速终端滑模主动容错控制方法。首先利用观测器估计机器人系统中的执行器故障信息,并通过自适应律对故障未知的界进行估计。然后根据机器人各关节的加权位置误差进一步设计快速终端滑模控制器对获得的故障信息做出补偿,从而实现有限时间主动容错控制。通过李雅普诺夫函数法证明了闭环系统的稳定性,并采用两关节机器人验证了方案的有效性。该方案可以通过对不同关节位置误差权重值的分配来相应地补偿故障的影响,能够在有限时间内使得机器人位置跟踪误差快速收敛且跟踪精度得到提高。     

基于高增益观测器的AUV水平面轨迹跟踪控制

通过研究欠驱动自治水下机器人（AUV）在水平面的运动规律,针对欠驱动AUV水平面轨迹跟踪控制中的非完整约束、模型中的耦合性和非线性、海流干扰、跟踪精度问题进行了深入的研究,运用高增益观测器结合反步控制方法对欠驱动AUV轨迹跟踪进行有效控制。定义了AUV的地面坐标系和船体坐标系,并完成了地面坐标系向船体坐标系的转换,建立了欠驱动AUV的水平面运动学模型和动力学模型。为欠驱动AUV所提出的高增益观测器结合反步控制方案,在保证跟踪系统稳定性的前提下,可以有效地提高跟踪精度,能够消除外界干扰对控制效果的影响,并使得控制输入满足实际工程的应用约束条件。控制方法的稳定性均采用Lyapunov稳定性理论加以证明,并通过数值仿真验证了所设计控制器的有效性。     

An exponentially stable adaptive control for force and positiontracking of robot manipulators

The problem of controlling a robot manipulator while the end effector is in contact with an environment of finite but unknown stiffness is considered. An exponentially stable control law is derived starting from a passivity-based position control algorithm. The original position trajectory is scaled along the interaction direction so as to achieve force tracking as well as position tracking along the unconstrained directions. A passivity-based adaptive algorithm is designed to avoid the explicit computation of the scaling factor, which depends on the unknown stiffness of the environment, leading to time-varying PID control actions on the force error     

Simulation systems for the design of robot mechanisms

Building environmental models by a vision-guided mobile robot is a key problem in robotics. This paper presents a new strategy of the vision-guided mobile robot for building models of an unknown environment by panoramic sensing. The mobile robot perceives with two types of panoramic sensing: one is for acquiring omnidirectional visual information at an observation point to find the outline structure of the local environment and the other is for acquiring visual information along a route to build local environmental models. Before exploring the environment, the robot looks around and finds the outline structure of the local environment as a reference frame for acquiring the local models. Then the robot builds the local models while moving along the directions of the outline structure (the outline structure is represented by a simple convex polygon, each side of which has a direction). We have implemented the above-mentioned robot behaviors into a mobile robot which has multiple vision agents. The multiple vision agents can simultaneously execute different vision tasks needed for panoramic sensing.     

A neural network-based approach for an assembly cell control

In several robotics applications, the robot must interact with the workspace, and thus its motion is constrained by the task. In this case, pure position control will be ineffective since forces appearing during the contacts must also be controlled. However, simultaneous position and force control called hybrid control is then required. Moreover, the nonlinear plant dynamics, the complexity of the dynamic parameters determination and computation constraints makes more difficult the synthesis of control laws. In order to satisfy all these constraints, an effective hybrid force/position approach based on artificial neural networks for multi-inputs/multi-outputs systems is proposed. This approach realizes, simultaneously, an identification and control of systems, and it is implemented according to two phases: At first, a neural observer is trained off-line on the basis of the data acquired during contact motion, in order to realize a smooth transition from free to contact motion. Then, an online learning of the neural controller is implemented using neural observer parameters so that the closed-loop system maintains a good performance and compensates for uncertain/unknown dynamics of the robot and the environment. A typical example on which we shall focus is an assembly task. Experimental results on a C5 links parallel robot demonstrate that the robot's skill improves effectively and the force control performances are satisfactory, even if the dynamics of the robot and the environment change.     

On-line exploration of an unknown surface by a robotic probe

A control strategy is developed for a robotic probe with tactile sensing to explore an unknown surface without losing contact. Digital computer simulations are performed of a three-link, planar manipulator exploring an ellipsoidal surface in order to test the control strategy. The equations of motion are written and linear, time-varying, state-variable feedback is applied to stabilize and decouple the system. A tactile sensor is simulated to supply the normal force between the robot and the surface. From the magnitude and direction of this force, the desired trajectory is determined. An inverse plant and force feedback are implemented to provide the required input torques to the robot's actuators.     

Tracking and force control for a class of robotic manipulators

The increasing utilization of robotic manipulators in industrial tasks, such as assembly, forming or shaping of surfaces, and handling hazardous materials, depends greatly on available hybrid force and position control schemes. Since the robot and its environment are often subject to parameter uncertainties that cannot be neglected, it is necessary to design controllers that are robust with respect to these uncertainties. In addition, the dynamics of the robot are nonlinear, requiring consideration of nonlinear control concepts. Another aspect to be taken into account is the relative stiffness of the robot, the force sensor, and the manipulated surface. That is, the behavior of the system normal to the surface is relatively stiff, while that tangential to the surface is relatively free. Separation of the controller for these two directions is therefore indicated. We propose a controller design that accounts for this point of view and demonstrate its efficacy with respect to robustness and accuracy of position and force tracking by means of numerical simulations. The design is based on the control concept of Corless and Leitmann [l]. The example considered is a Manutecr3 robot with three degrees of freedom. In addition, we account for the dynamics of the actuator, which also possesses three degrees of freedom. The considered parameter uncertainties are friction moments in the links and friction between the end effector and the manipulated object, as well as nonlinear dynamics, which are difficult to characterize.Recommended by J. Skowronski     

Reasoning about nature of contact for planning manipulators tasks

Extracting information about contact between two convex bodies from the measured force vector is a prerequisite for any fine compliant motion control strategy. Contact information contains the direction and orientation of the contact surface normal and its relative location and orientation with respect to the compliant reference frame system.A method for interpreting the contact force feedback during compliant robot motion control, using kinematic screws, is presented. Domain specific rules combined with partial a priori knowledge of mating parts geometry and interpreted force signals are used to reason and make inferences about the initial contact configuration. The likely contact surfaces are predicted and point(s) or line(s) of contact are fully defined. These surfaces are idealized and represented by quadratic equations or polyhedral surfaces. The geometric properties of surfaces at the contact location are used to select the contact configuration when multiple solutions exist.An algorithm for predicting the Expected Contact Configuration (ECC) has been developed and is illustrated here with examples. Experimental validation of the developed expert system prototype, using a 6R manipulator, a six-axis force sensor, and a host computer is described.     

基于机器人力控制表面跟踪的遥控焊接虚拟环境标定

提出了基于机器人力控制表面跟踪的遥控焊接虚拟环境标定方法．采用基于力觉传感器坐标系横切面轮廓线的力控制策略对工件表面进行跟踪,并精确测量接触点的空间位置．通过椭圆跟踪轨迹直接最小二乘拟合算法和T形管工件标定算法,解决了T形管工件的虚拟环境标定问题．实验结果表明,该方法可以实现高精度的虚拟环境标定,虚拟环境模型精度能够满足以被动柔顺方式完成遥控焊接接触任务的需要．     

机器人力控制仿真试验与研究

为了解决机器人在特定接触环境操作时对可以产生任意作用力柔性的高要求和机器人在自由空间操作时对位置伺服刚度及机械结构刚度的高要求之间的矛盾.对机器人力控制问题进行了研究,利用机械动力学仿真软件ADAMS/VIEW建立关节机器人的虚拟样机模型,通过其输入输出接口实现与MATLAB的通信,基于SIMULINK建立关节机器人力控制系统模型,将联合仿真概念引入到机器人力控制领域,最后进行仿真试验,对控制算法进行仿真验证,以提高控制精度和控制质量,通过对仿真结果的分析和处理证明此方法的合理性和有效性,为机器人力控制提供了一套有效的分析方法.     

磁场与视觉共融的多模态胶囊机器人人机交互控制

为了实现磁驱动胶囊内窥镜基于定点悬停调姿的全景观察,提出一种欠驱动双半球胶囊机器人,突破了悬停调姿与滚动行走双重工作模态转换关键技术．为了在肠道弯曲环境内实现滚动行走,提出与视觉相融合的空间万向旋转磁场人机交互控制策略．理论上,依据正交变换推得经纬坐标系下以磁场轴线的侧摆角与俯仰角为独立变量的三相电流形式空间万向旋转磁场叠加公式,完成控制变量降维与解耦,实现磁场轴线沿侧摆或俯仰方向的单独调整;实践上,通过磁矩随动效应带动双半球形胶囊机器人摄像头分别实现侧摆与俯仰2个方向的独立扫描,使机器人轴线对准各段肠道弯曲方向,沿弯曲方向施加滚动磁矩实现胶囊机器人滚动转弯．最后,采用离体猪大肠模拟环境验证人机交互性能．试验表明,通过结合弯曲肠道图像与磁场方位的人机交互控制,既能实现双半球形胶囊机器人在被动模态下的姿态任意调整与全景观察,也能实现在主动模态下沿弯曲环境的滚动行走．     

Equilibrium point control of a robot manipulator using biologically-inspired redundant actuation system

Unlike their robotic counterparts, humans excel at various contact tasks even in unknown environments by utilizing their ability to adaptively modulate the arm impedance. As one of many theories in human motor control, the equilibrium point control hypothesis suggests that multi-joint limb movements can be achieved by shifting the equilibrium positions defined by the central nervous system and utilizing the spring-like property of the peripheral neuromuscular system. To generate human arm-like compliant motion, this study implements the equilibrium point control on a robot manipulator using redundant actuation: two actuators are installed on each joint: one to control the joint position and the other to control the joint stiffness, respectively. With the double-actuator unit, the equilibrium position and stiffness (or impedance) can be independently programmed. Also, it is possible to estimate the contact force based on angle measurement with a user-specified stiffness. These features enable the robot manipulator to execute stable and safe movement in contact tasks. A two-link manipulator equipped with the double-actuator units was developed, and experimental results from teleoperated contact tasks show the potential of the proposed approach.