基于WAVE技术实现数码相机Top-Down结构设计

一、Top-Down设计与装配模型 产品的设计是个渐进的过程,一般经过概念设计、参数化设计和详细设计三个阶段.如图1,这种渐进的设计过程,称为自顶向下(Top-Down)设计.     

基于RBF神经网络的轮式移动机器人轨迹跟踪控制

针对一类非完整移动机器人的轨迹跟踪控制系统,提出一种基于RBF神经网络的滑模控制与转矩控制相结合的智能控制方法。该方法同时考虑机器人运动学和动力学模型,通过RBF神经网络进行移动机器人运动过程学习,与速度误差结合构成力矩控制器,可保证闭环误差系统一致最终渐进稳定。采用基于李亚普诺夫(Lyapunov)稳定性理论的判稳方法,证明整个闭环控制系统的稳定性。仿真结果表明,该控制方案具有较强的鲁棒性。     

基于物理模型的虚拟装配技术研究

虚拟装配是虚拟现实技术在产品设计领域的一个重要应用 .为了从运动学与动力学的角度来考察虚拟装配过程中零件的运动 ,以便更真实地反映虚拟环境中零件装配运动的本质规律 ,提出了一种虚拟装配环境中用于将装配几何约束自动映射为运动副约束的基于运动自由度分析的物理约束生成方法 ,同时 ,提出了基于变刚度弹簧模型的装配力交互输入方法 ,并实现了位移输入与装配力输入的映射 .另外 ,还给出了基于物理模型的虚拟装配的基本过程 .该方法在虚拟设计与装配原型系统的研究与开发中已得到实现 .     

机器人捕捉运动目标的动力学视觉伺服方法

提出一种机器人捕捉运动目标的动力学视觉伺服方法。基于位置阻抗控制器,通过双目立体视觉检测并跟踪运动目标的位置,结合CMAC网络,采用以视觉阻抗的二次型为训练目标的学习型视觉阻抗控制器,用于克服控制器对系统结构参数变化适应性差的缺点,并对阻抗参数进行优化。实验结果表明,该视觉伺服方法在机器人捕捉运动目标时具有良好的动力学特性和轨迹控制效果。     

虚拟装配中基于导纳控制的力觉渲染技术

传统的力触觉渲染多采用阻抗控制,不能很好地满足虚拟装配的应用要求,相比之下导纳控制模式更适用这一领域.为此提出一种基于导纳控制的双线程力觉渲染构架,并给出相应的力觉渲染算法.首先建立用于导纳控制的动力学模型,并讨论了碰撞和约束这2个状态下的力觉渲染;为了使用力觉交互接口进行虚拟装配中的小间隙装配,提出物理约束与几何约束结合的力觉渲染方法;最后针对物理计算和力反馈循环2个线程刷新频率不匹配的问题,利用二次拉格朗日多项式进行数值插值,实现了力觉交互接口的平稳输出.通过力反馈设备与自主开发的虚拟装配原型系统VAPP的连接与应用,验证了所提出的算法满足虚拟装配系统中力觉交互的应用要求.     

面向装配工序交叉的虚拟装配工艺信息模型

虚拟装配工艺规划中目前大都采用装配顺序描述装配过程,但尚无法表达装配工序交叉和设计中的安装布置'捌整等问题.考虑设计阶段和制造阶段的装配要求,建立了基于装配任务的装配工艺信息模型.该模型以多层次的装配进程表达产品装配工艺过程,通过任务对象的交叉安装表达工序交叉和安装布置凋整,并给出了装配调整策略;针对冗余装配任务提出了装配任务合并方法.最后通过实例验证了文中模型的正确性和有效性.     

基于框架的虚拟装配运动引导

为了在交互式装配过程中精确地定位零件模型,提出一种基于框架的装配运动引导方法.该方法将典型装配过程性知识封装到过程框架中,将蕴藏在零件模型B-rep中的工程信息封装到特征框架中,与场景匹配引擎一起构成框架系统;框架系统采用场景匹配的方式来捕获操作者的装配意图(即匹配成功的过程框架),由匹配成功的过程框架来引导装配运动,并由过程框架中规定的动作和时机来完成装配件的精确定位.应用实例结果表明,文中方法有效地克服了操作者通过交互设备对装配零件运动控制的不精确性问题.     

基于实例的推理在装配过程设计中的应用

通过研究确定装配顺序的方法,以装配零部件构成和确定的装配顺序为输入,采用装配功能树到装配过程机制树映射的方法实现装配过程设计。在此基础上提出从功能需求、行为模型和过程机制模型3个层次对装配实例进行描述的方法和装配过程实例相似性系数的计算方法。实例运行结果验证了该方法的有效性。     

近邻渐进直推式支持向量机算法

针对渐进直推式支持向量机箅法训练速度慢和学习性能不稳定的问题,提出一种近邻渐进直推式支持向量机算法.该算法利用支持向量机中支持向量信息,选择支持向量附近的无标签样本点进行标注,采用支持向量预选取的方法减少训练集的规模,提高算法的速度.实验结果表明了该算法的有效性.     

虚拟装配仿真系统相关技术的研究

研究了一种等离子体增强化学气相沉积(PECVD)装备虚拟装配仿真系统,介绍了系统的装配模型以及装配顺序和路径的规划.建立了虚拟装配仿真场景结构图,并采用细节层次技术对仿真场景进行优化.为了能够实时、方便地修改装配序列和装配路径,准确地描述零部件在装配或拆卸过程中的位置姿态信息,提出了一种虚拟装配的描述语言代码(V代码)和虚拟装配函数(V函数).最后采用基于包围盒逐层分解的干涉检验方法,实现装配过程的干涉检验.该仿真系统有助于缩短产品开发周期、提高设计质量、降低装配成本.     

Progressive learning and its application to robot impedancelearning

An approach to learning control using an excitation scheduling technique is developed and applied to an impedance learning problem for fast robotic assembly. Traditional adaptive and learning controls incur instability depending on the reference inputs provided to the system. This technique avoids instability by progressively increasing the level of system excitation. Called progressive learning, it uses scheduled excitation inputs that allow the system to learn quasistatic parameters associated with slow input commands first, followed by the learning of dynamic parameters excited by fast input commands. As learning progresses, the system is exposed to a broader range of input excitation, which nonetheless does not incur instability and unwanted erratic responses. In robotic assembly, learning starts with a slow, quasistatic motion and goes to a fast, dynamic motion. During this process, the stiffness terms involved in the impedance controller are learned first, then the damping terms and finally by the inertial terms. The impedance learning problem is formulated as a model-based, gradient following reinforcement learning. The method allows the suppression of excessive parameter changes and thereby stabilizes learning. By gradually increasing the motion speed command, the internal model as well as the control parameters can be learned effectively within a focused, local area in the large parameter space, which is then gradually expanded as speed increases. Several strategies for motion speed scheduling are also addressed.     

Optimizing task scheduling in human-robot collaboration with deep multi-agent reinforcement learning

Human-Robot Collaboration (HRC) presents an opportunity to improve the efficiency of manufacturing processes. However, the existing task planning approaches for HRC are still limited in many ways, e.g., co-robot encoding must rely on experts’ knowledge and the real-time task scheduling is applicable within small state-action spaces or simplified problem settings. In this paper, the HRC assembly working process is formatted into a novel chessboard setting, in which the selection of chess piece move is used to analogize to the decision making by both humans and robots in the HRC assembly working process. To optimize the completion time, a Markov game model is considered, which takes the task structure and the agent status as the state input and the overall completion time as the reward. Without experts’ knowledge, this game model is capable of seeking for correlated equilibrium policy among agents with convergency in making real-time decisions facing a dynamic environment. To improve the efficiency in finding an optimal policy of the task scheduling, a deep-Q-network (DQN) based multi-agent reinforcement learning (MARL) method is applied and compared with the Nash-Q learning, dynamic programming and the DQN-based single-agent reinforcement learning method. A height-adjustable desk assembly is used as a case study to demonstrate the effectiveness of the proposed algorithm with different number of tasks and agents.     

The Application of Connectionist Structures to Learning Impedance Control in Robotic Contact Tasks

The goal of this paper is to consider the synthesis of learning impedance control using recurrent connectionist structures for on-line learning of robot dynamic uncertainties in the case of robot contact tasks. The connectionist structures are integrated in non-learning impedance control laws that are intended to improve the transient dynamic response immediately after the contact. The recurrent neural network as a part of hybrid learning control algorithms uses fast learning rules and available sensor information in order to improve the robotic performance progressively for a minimum possible number of learning epochs. Some simulation results of deburring process with the MANUTEC r3 robot are presented here in order to verify the effectiveness of the proposed control learning algorithms.     

Adaptive Impedance Control for Upper-Limb Rehabilitation Robot Using Evolutionary Dynamic Recurrent Fuzzy Neural Network

Control system implementation is one of the major difficulties in rehabilitation robot design. A newly developed adaptive impedance controller based on evolutionary dynamic fuzzy neural network (EDRFNN) is presented, where the desired impedance between robot and impaired limb can be regulated in real time according to the impaired limb??s physical recovery condition. Firstly, the impaired limb??s damping and stiffness parameters for evaluating its physical recovery condition are online estimated by using a slide average least squares (SALS)identification algorithm. Then, hybrid learning algorithms for EDRFNN impedance controller are proposed, which comprise genetic algorithm (GA), hybrid evolutionary programming (HEP) and dynamic back-propagation (BP) learning algorithm. GA and HEP are used to off-line optimize DRFNN parameters so as to get suboptimal impedance control parameters. Dynamic BP learning algorithm is further online fine-tuned based on the error gradient descent method. Moreover, the convergence of a closed loop system is proven using the discrete-type Lyapunov function to guarantee the global convergence of tracking error. Finally, simulation results show that the proposed controller provides good dynamic control performance and robustness with regard to the change of the impaired limb??s physical condition.     

基于深度强化学习的动态装配算法

针对动态装配环境中存在的复杂、动态的噪声扰动,提出一种基于深度强化学习的动态装配算法。将一段时间内的接触力作为状态,通过长短时记忆网络进行运动特征提取;定义序列贴现因子,对之前时刻的分奖励进行加权得到当前时刻的奖励值;模型输出的动作为笛卡尔空间位移,使用逆运动学调整机器人到达期望位置。与此同时,提出一种对带有资格迹的时序差分算法改进的神经网络参数更新方法,可缩短模型训练时间。在实验部分,首先在圆孔–轴的简单环境中进行预训练,随后在真实场景下继续训练。实验证明提出的方法可以很好地适应动态装配任务中柔性、动态的装配环境。     

基于接触状态感知发育的机器人柔性装配方法

为了提高工业机器人装配的实时性、自适应性和鲁棒性,借鉴人类后天感知学习方式,提出一种基于接触状态感知发育的柔性装配方法.采用机器人末端的位姿和力/力矩来描述装配接触状态,结合支持向量数据描述和改进极限学习机对接触状态感知发育,形成可自我更新成长的经验知识库,预测机器人的装配动作,完成柔性装配任务.为验证所提出方法的有效性,以小型断路器卡合装配为例进行实验,实验结果表明,采用接触状态感知发育可实现装配经验知识库的自我更新,完成机器人的柔性装配,验证了所提出方法的可行性和有效性.     

Neocognitron学习算法分析

现有的对Ｎｅｏｃｏｇｎｉｔｒｏｎ的分析都采用代数法，因而无法研究它的动态特性．本文把Ｎｅｏｃｏｇｎｉｔｒｏｎ及其学习算法推广到连续时域，借助微分方程来研究Ｎｅｏｃｏｇｎｉｔｒｏｎ．文中给出了无教师学习算法一般情况下，学习过程中Ｕｓ层神经元输出变化规律的微分方程，指出其增加的条件，并推出权ａ、ｂ初始值选择的一个必要条件；进一步得出无教师算法代表稳定后和有教师学习情况下Ｕｓ变化的一种等效显式函数，指出此时学习过程是Ｕｓ层神经元输出向一个系数的逼近过程，且有教师学习过程的最后状态与可变权初值和学习率无关．并讨论了影响Ｕｓ终值和逼近速度的因素．     

Robust implementation of generalized impedance control for robot manipulators

Compliant manipulation requires the robot to follow a motion trajectory and to exert a force profile while making compliant contact with a dynamic environment. For this purpose, a generalized impedance in the task space consisting of a second-order function relating motion errors and interaction force errors is introduced such that force tracking can be achieved. Using variable structure model reaching control, the generalized impedance is realized in the presence of parametric uncertainties. The proposed control method is applied to a multi-d.o.f. robot for an assembly task of inserting a printed circuit board into an edge connector socket. It is suggested that an assembly strategy which involves a sequence of planned target generalized impedances can enable the task to be executed in a desirable manner. The effectiveness of this approach is illustrated through experiments by comparing the results with those obtained using a model-based control implementation.     

面向虚拟装配的混杂系统建模和控制器设计

针对装配的动态过程显著地表现出连续与离散共存的混杂特性,研究了面向虚拟装配的混杂系统建模以及控制器设计方法．提出了混杂基本结构对应于单个装配对象的建模方法,有效地避免了由于装配零部件过多给模型造成的复杂性,并在此基础上,运用最小干预安全控制器对装配过程中的不合法结构实施控制,最后以某汽车底盘总装中两个装配对象为例,给出了具体的动态描述及其建模和控制器设计。证明了该方法的可行性和有效性.     

Robotic assembly automation using robust compliant control

Industrial robots used to perform assembly applications are still a small portion of total robot sales each year. One of the main reasons is that it is difficult for conventional industrial robots to adapt to any sort of change. This paper proposes a robust control strategy to perform an assembly task of inserting a printed circuit board (PCB) into an edge connector socket using a SCARA robot. The task is very challenging because it involves compliant manipulation in which a substantial force is needed to accomplish the insertion operation and there are some dynamic constraints from the environment. Therefore, a robust control algorithm is developed and used to perform the assembly process. The dynamic model of the robotic system is developed and the dynamic parameters are identified. Experiments were performed to validate the proposed method. Experimental results show that the robust control algorithm can deal with parameter uncertainties in the dynamic model, thus achieve better performance than the model based control method. An abnormal case is also investigated to demonstrate that the robust compliant control method can deal with the abnormal situation without damaging the system and assembly parts, while pure position control method may cause damages. This strategy can also be used in other similar assembly processes with compliant applications.