Scheduling algorithm of dual-armed cluster tools with residency time and reentrant constraints

To solve the scheduling problem of dual-armed cluster tools for wafer fabrications with residency time and reentrant constraints,a heuristic scheduling algorithm was developed.Firstly,on the basis of formulating scheduling problems domain of dual-armed cluster tools,a non-integer programming model was set up with a minimizing objective function of the makespan.Combining characteristics of residency time and reentrant constraints,a scheduling algorithm of searching the optimal operation path of dual-armed transport module was presented under many kinds of robotic scheduling paths for dual-armed cluster tools.Finally,the experiments were designed to evaluate the proposed algorithm.The results show that the proposed algorithm is feasible and efficient for obtaining an optimal scheduling solution of dual-armed cluster tools with residency time and reentrant constraints.

The Fuzzy Neural Network Control Scheme With H∞ Tracking Characteristic of Space Robot System With Dual-arm After Capturing a Spin Spacecraft

In this paper,the dynamic evolution for a dualarm space robot capturing a spacecraft is studied,the impact effect and the coordinated stabilization control problem for postimpact closed chain system are discussed.At first,the pre-impact dynamic equations of open chain dual-arm space robot are established by Lagrangian approach,and the dynamic equations of a spacecraft are obtained by Newton-Euler method.Based on the results,with the process of integral and simplify,the response of the dual-arm space robot impacted by the spacecraft is analyzed by momentum conservation law and force transfer law.The closed chain system is formed in the post-impact phase.Closed chain constraint equations are obtained by the constraints of closed-loop geometry and kinematics.With the closed chain constraint equations,the composite system dynamic equations are derived.Secondly,the recurrent fuzzy neural network control scheme is designed for calm motion of unstable closed chain system with uncertain system parameter.In order to overcome the effects of uncertain system inertial parameters,the recurrent fuzzy neural network is used to approximate the unknown part,the control method with H∞tracking characteristic.According to the Lyapunov theory,the global stability is demonstrated.Meanwhile,the weighted minimum-norm theory is introduced to distribute torques guarantee that cooperative operation between manipulators.At last,numerical examples simulate the response of the collision,and the efficiency of the control scheme is verified by the simulation results.

Singular perturbation composite control of a free-floating flexible dual-arm space robot

The Free-floating Flexible Dual-arm Space Robot is a highly nonlinear and coupled dynamics system. In this paper, the dynamic model is derived of a Free-floating Flexible Dual-arm Space Robot holding a rigid payload. Furthermore, according to the singular perturbation method, the system is separated into a slow subsystem representing rigid body motion of the robot and a fast subsystem representing the flexible link dynamics. For the slow subsystem, based on the second method of Lyapunov, using simple quantitative bounds on the model uncertainties, a robust tracking controller design is used during the trajectory tracking phase. The optimal control method is designed in the fast subsystem to guarantee the exponential stability. With the combination of the two above, the system can track the expected trajectory accurately, even though with uncertainty in model parameters, and its flexible vibration gets suppressed, too. Finally, some simulation tests have been conducted to verify the effectiveness of the proposed methods.

Adaptive neural network control for coordinated motion of a dual-arm space robot system with uncertain parameters

Control of coordinated motion between the base attitude and the arm joints of a free-floating dual-arm space robot with uncertain parameters is discussed. By combining the relation of system linear momentum conversation with the Lagrangian approach, the dynamic equation of a robot is established. Based on the above results, the free-floating dual-arm space robot system is modeled with RBF neural networks, the GL matrix and its product operator. With all uncertain inertial system parameters, an adaptive RBF neural network control scheme is developed for coordinated motion between the base attitude and the arm joints. The proposed scheme does not need linear parameterization of the dynamic equation of the system and any accurate prior-knowledge of the actual inertial parameters. Also it does not need to train the neural network offline so that it would present real-time and online applications. A planar free-floating dual-arm space robot is simulated to show feasibility of the proposed scheme.

COLLISION-FREE MOTION PLANNING OF DUAL-ARM ROBOT

A profound approach about dual arm robot collision free motion planning is made. The method of configuration space is first and successfully applied to the collision free motion planning of dual arm robot, and a new concept, slave arm collision state graph, is presented. In this algorithm ,the problem of dual arm robot collision free motion planning is reduced to a search in the collision state graph. With this algorithm, a time optimum trajectory would be found, or the condition that there is no feasible solution for the slave arm is proved. A verification of this algorithm is made in the dual arm horizontal articulated robot SCARATES, and the results ascertain that the algorithm is feasible and effective.

Compliance motion control of the hydraulic dual-arm manipulator with adaptive mass estimation of unknown object

Given the limited operating ability of a single robotic arm,dual-arm collaborative operations have become increasingly prominent.Compared with the electrically driven dual-arm manipulator,due to the unknown heavy load,difficulty in measuring contact forces,and control complexity during the closed-chain object transportation task,the hydraulic dual-arm manipulator(HDM)faces more difficulty in accurately tracking the desired motion trajectory,which may cause object deformation or even breakage.To overcome this problem,a compliance motion control method is proposed in this paper for the HDM.The mass parameter of the unknown object is obtained by using an adaptive method based on velocity error.Due to the difficulty in obtaining the actual internal force of the object,the pressure signal from the pressure sensor of the hydraulic system is used to estimate the contact force at the end-effector(EE)of two hydraulic manipulators(HMs).Further,the estimated contact force is used to calculate the actual internal force on the object.Then,a compliance motion controller is designed for HDM closed-chain collaboration.The position and internal force errors of the object are reduced by the feedback of the position,velocity,and internal force errors of the object to achieve the effect of the compliance motion of the HDM,i.e.,to reduce the motion error and internal force of the object.The required velocity and force at the EE of the two HMs,including the position and internal force errors of the object,are inputted into separate position controllers.In addition,the position controllers of the two individual HMs are designed to enable precise motion control by using the virtual decomposition control method.Finally,comparative experiments are carried out on a hydraulic dual-arm test bench.The proposed method is validated by the experimental results,which demonstrate improved object position accuracy and reduced internal force.

Adaptive Sliding-Mode Disturbance Observer-Based Nonlinear Control for Unmanned Dual-Arm Aerial Manipulator Subject to State Constraints

The unmanned dual-arm aerial manipulator system is composed of a multirotor unmanned aerial vehicle(UAV)and two manipulators.Compared to a single manipulator,dual-arm always provides greater°exibility and versatility in both goods delivery and complex task execution.However,the practical application of the system is limited due to nonlinearities and complex dynamic coupling behavior between the multirotor and the manipulator,as well as the one between the inner and outer loop of the multirotor.In this paper,a holistic model of the dual-arm aerial manipulator system is¯rst derived with complete model information.Subsequently,an adaptive sliding-mode disturbance observer(ASMDO)is proposed to handle external disturbances and unmeasurable disturbances caught by unmeasurable angular velocity and acceleration of the manipulators.Moreover,for safety concerns and transient performance requirements,the state constraints should be guaranteed.To this end,an auxiliary term composed of constrained variable signals is introduced.Then,the performance of the designed method is proven by rigorous analysis.Finally,the proposed method is validated through two sets of simulation tests.

双悬臂截割机器人相对动力学建模与力位混合控制研究

双悬臂截割机器人可解决传统单臂掘进机在截割大尺寸断面时效率低下的难题,但其与煤岩的动态交互影响控制性能。现有研究以双臂接触同一对象形成运动闭链为前提,无法满足双悬臂截割机器人双臂运动及末端截割头输出力的控制要求。针对该问题,设计了一种基于机器人相对动力学模型的力位混合控制系统。建立双悬臂截割机器人运动学和动力学模型,基于机器人的相对雅可比矩阵及虚位移与虚功原理推导出机器人的相对动力学模型,通过单一变量同时描述机器人双臂的运动状态,将机器人双臂独立的动力学模型整合为一个整体。基于机器人的相对动力学模型,设计了机器人双臂力位混合控制系统,通过李雅普诺夫函数验证了系统的稳定性和可行性。仿真结果表明:双悬臂截割工艺较单悬臂截割拥有更大的工作空间,具有一次性实现大断面截割的能力;双悬臂截割机器人力位混合控制系统能够完成对期望相对位置和期望相对力的同步跟踪,对截割头期望位置跟踪的绝对误差在0.3132 m以内,均方根误差为0.1447 m。

Contact detumbling toward a nutating target through deformable effectors and prescribed performance controller

Detumbling operation toward a rotating target with nutation is meaningful for debris removal but challenging. In this study, a deformable end-effector is first designed based on the requirements for contacting the nutating target. A dual-arm robotic system installed with the deformable end-effectors is modeled and the movement of the end-tips is analyzed. The complex operation of the contact toward a nutating target places strict requirements on control accuracy and controller robustness. Thus, an improvement of the tracking error transformation is proposed and an adaptive sliding mode controller with prescribed performance is designed to guarantee the fast and precise motion of the effector during the contact detumbling.Finally, by employing the proposed effector and the controller,numerical simulations are carried out to verify the effectiveness and efficiency of the contact detumbling toward a nutating target.

基于机械臂运动轨迹控制双生成对抗网络的设计

为了提高机械臂的运动准确性,以改善机械臂的工作效率,设计了一种用于机械臂运动轨迹控制的双生成对抗网络。首先,对机械臂的结构进行了分析,根据机械臂的结构特点建立了机械臂的坐标系,在该坐标系上,通过机械臂各关节点的变换矩阵,求取了机械臂的运动学方程;然后,将机械臂末端执行器的位置向量、关节速度等信息与编码器相结合,以获取双向生成性对抗网络,并在双向生成性对抗网络的基础上,设计了双生成对抗网络,以对机械臂的运动轨迹进行控制;最后,利用所提方法与干扰观测器方法对机械臂的运动轨迹进行控制测试。测试结果显示,所提方法的控制准确度比干扰观测器方法的控制准确度提高了40.13%,而且所提方法控制机械臂运动过程中,出现的波动也比干扰观测器方法的控制过程要小。说明所提方法不仅能较为准确地控制机械臂运动,而且控制过程较为平稳,有助于提高机械臂的工作效率。

基于力引导的空间舱外双臂机器人遥操作同步过程优化

为提高空间舱外双臂机器人遥操作同步过程中操作效率,提出了一种基于力引导的遥操作同步过程优化方法。通过改进的蒙特卡洛法对双臂机器人协作的工作空间进行分析,提出了一种最优协作位置预测方法,并通过力引导方式,辅助引导遥操作双臂到达最优协作位置。实验结果表明:相比于只有视觉反馈的遥操作过程,同步过程优化后到达同步工作状态的时间减少了84.26%,同步过程通过的路径长度缩短了35.43%;对比运动学引导方法,各项指标方差更小,稳定性更强。

Area-oriented coordinated trajectory planning of dual-arm space robot for capturing a tumbling target

The growing amount of space debris poses a threat to operational spacecraft and the long-term sustainability of activities in outer space. According to the orbital mechanics, an uncontrolled space object will be tumbling, bringing great challenge to capture and remove it. In this paper, a dual-arm coordinated ‘‘Area-Oriented Capture'(AOC) method is proposed to capture a non-cooperative tumbling target. Firstly, the motion equation of the tumbling target is established, based on which, the dynamic properties are analyzed. Then, the ‘‘Area-Oriented Capture'concept is presented to deal with the problem of large pose(position and attitude) deviation and tumbling motion. An area rather than fixed points/devices is taken as the object to be tracked and captured. As long as the manipulators’ end-effectors move to a specified range of the objective areas(not fixed points on the target, but areas), the target satellite will be hugged by the two arms.At last, the proposed method and the traditional method(i.e. fixed-point oriented capture method)are compared and analyzed through simulation. The results show that the proposed method has larger pose tolerance and takes shorter time for capturing a tumbling target.

双臂皮革绷板机器人的仿真分析

为了解决目前皮革绷板干燥工序中专用绷板设备自动化、智能化程度低且操作工人劳动强度大的现状,本文利用双臂协同技术,设计了一款皮革绷板机器人。采用D-H法为双机械臂建立了空间坐标系与运动学模型,在MATLAB软件中分析了双机械臂的操作空间。利用ADAMS软件完成了整机虚拟样机模型的建立,对双机械臂进行了仿真。结果表明,双臂操作空间可满足皮革绷板工艺需求,机械臂选型合理;机械臂各关节角速度及角加速度曲线平稳,说明双机械臂可完成皮革的绷板操作。

考虑基座振动抑制的双空间机械臂协同阻抗控制

针对柔性基座双空间机械臂紧耦合协同过程中面临的对物体内/外力的柔顺响应、基座的振动抑制以及双臂避碰等问题,提出了一种反作用最优协同阻抗控制方法。首先建立了柔性基座双空间机械臂的紧耦合系统模型。然后,利用物体级阻抗控制器和末端执行器级阻抗控制器分别实现了双空间机械臂对物体所受外力和内力的柔顺响应。然后,利用基座能量衰减最优控制和人工势场法得到了双空间机械臂的最优零空间自运动向量,通过双冗余空间机械臂的零空间自运动同时实现了基座振动抑制和双臂间的避碰。最后,在动力学仿真软件中验证了协同阻抗控制方法在解决物体内/外力柔顺响应、基座振动抑制、双臂避碰问题上的有效性。

基于自适应神经网络的工业机器人双臂协同鲁棒控制

为了克服机械摩擦、外界干扰和模型误差等不确定性对工业机器人双臂运动轨迹控制精度的影响,设计了一种基于自适应神经网络的工业机器人双臂协同鲁棒控制方法。首先,建立了带有各类不确定性的工业机器人双臂动力学模型;然后,通过构造障碍Lyapunov函数设计了带有不确定性的协同控制律,并设计了自适应神经网络对系统中的不确定性进行估计,从而得到工业机器人双臂协同鲁棒控制律;最后,利用Lyapunov稳定性理论证明了设计的协同鲁棒控制律能够将工业机器人双臂的轨迹跟踪误差、速度跟踪误差和不确定性估计误差约束在一个任意小的邻域内。仿真结果表明,设计的自适应神经网络可准确估计出工业机器人双臂系统中的不确定性,最大估计误差仅为0.04 N·m,提出的协同鲁棒控制律能够稳定、准确地跟踪轨迹控制指令,最大轨迹跟踪误差仅为1.3 mm,从而验证了设计方法的合理性。在三维空间固定坐标定位测试中,提出的协同鲁棒控制律与其他几种方法相比具有更高的控制精度,平均定位误差和最大定位误差分别仅为1.1 mm和1.4 mm,表现出了更强的鲁棒性和更优的工程适用性。

双臂移乘护理机器人起抱位置与移乘姿态规划

首先,建立被护理人的4连杆模型,并结合机器人姿态信息建立人机系统力学模型,分析移乘中被护理人的受力(内力、外力);其次,通过问卷调查,确定各内力、外力对人体舒适性影响的权重,建立舒适性评价函数,并运用所建立的舒适性评价函数以优化被护理人移乘舒适性为目的规划起抱位置与姿态。本文进行了接触力和表面肌电(sEMG)测试实验。结果显示:本文方法规划的起抱位置与姿态,能够有效降低被护理人与机器人之间的接触压力,并把移乘中的腰部内力负担减轻44.2%,提高了移乘的舒适性。

Detumbling strategy based on friction control of dual-arm space robot for capturing tumbling target

The rotational motion of a tumbling target brings great challenges to space robot on successfully capturing the tumbling target.Therefore,it is necessary to reduce the target's rotation to a rate at which capture can be accomplished by the space robot.In this paper,a detumbling strategy based on friction control of dual-arm space robot for capturing tumbling target is proposed.This strategy can reduce the target's rotational velocity while maintaining base attitude stability through the establishment of the rotation attenuation controller and base attitude adjustment controller.The rotation attenuation controller adopts the multi-space hybrid impedance control method to control the friction precisely.The base attitude adjustment controller applies the dual-arm extended Jacobian matrix to stabilize the base attitude.The main contributions of this paper are as follows:(1)The compliant control method is adopted to achieve a precise friction control,which can reduce the target angular velocity steadily;(2)The dual-arm extended Jacobian matrix is applied to stabilize the base attitude without affecting the target capture task;(3)The detumbling strategy of dualarm space robot is designed considering base attitude stabilization,realizing coordinated planning of the base attitude and the arms.The strategy is verified by a dual-arm space robot with two 7-DOF(degrees of freedom)arms.Simulation results show that,target with a rotation velocity of 20(°)/s can be effectively controlled to stop within 30 s,and the final deflection of the base attitude is less than 0.15°without affecting the target capture task,verifying the correctness and effectiveness of the strategy.Except to the tumbling target capture task,the control strategy can also be applied to other typical on-orbit operation tasks such as space debris removal and spacecraft maintenance.

空间机动目标双臂协调柔顺操作控制方法

针对被操作目标运动状态变化导致双臂与目标间内力发生突变的问题,提出了一种空间机动目标双臂协调柔顺操作控制方法。首先,针对空间双臂协调柔顺操作任务,分别建立双臂空间机器人开链动力学模型和闭链动力学模型;其次,考虑目标机动产生的扰动,设计阻抗参数自主调整策略,在双臂末端与目标间作用力发生较大变化时,可以保证双臂协调操作任务的平稳性和安全性;最后,搭建双臂协调柔顺操作地面物理试验系统,并开展物理仿真试验验证,试验结果表明了本文所提方法的有效性。

一种空间双臂机器人有限时间柔顺抓捕策略

提出了一种面向非合作目标捕获的空间双臂机器人有限时间柔顺抓捕策略。首先建立了自由漂浮基空间机械臂以末端执行器位姿自由度为广义坐标表示的末端执行器动力学模型。在此基础上基于全局快速非奇异终端滑模控制理论设计了有限时间轨迹跟踪控制器,实现了末端执行器的有限时间轨迹跟踪控制。进一步采用一种三指末端抓握机构,建立了末端执行机构-目标抓捕位置子系统动力学模型,在该模型基础上基于柔顺控制理论设计了复合柔顺抓握控制策略,解决了末端执行器对目标的柔顺抓握控制。仿真结果表明,所设计的有限时间柔顺抓捕控制策略能够实现空间双臂机器人在有限时间内对翻滚目标的柔顺抓握控制。

基于残差BP神经网络的Baxter机器人逆运动学分析方法

提出1种基于残差BP(back propagation)神经网络的自适应逆运动学分析方法,围绕数据采集至实时控制的整个运动规划流程,采集140组位置和欧拉角数据,利用残差BP神经网络对Baxter机械臂进行逆运动学分析,拟合得到机械臂7个关节角度;将训练好的关节角度以话题的形式发布,通过在抓取物体的脚本中订阅该话题实现通讯;结合Rviz进行可视化展示和实物双臂协同实验,对4种物体模型分别用残差BP神经网络和普通BP神经网络进行抓取实验,验证所提方法的有效性。结果表明:所提方法的计算单点时间约8.1 ms,远小于机械臂的控制周期,可实现实时性的要求;在进行1500次训练的情况下,残差BP神经网络模型的均方误差为0.006,相比普通BP神经网络模型,误差降低0.077,提高了模型的准确性;所提方法的抓取成功率为87.5%,比普通BP神经网络提高了22.5%,验证了本文所提方法的有效性和实用性。