基于函数逼近技术的双机械臂自适应阻抗力控制

为提高机器人的智能性与灵活性,文章将人的示教引导作用引入到机器人控制系统中来,通过设计具有适应性的人机交互控制策略,使机器人能在人的引导下,智能地配合操作人员完成指定的任务.这种基于人机交互策略思想所设计的机器人,克服了早期机器人仅能按照预先设定好的程序来完成指定动作序列的弊端,拓宽了机器人的使用范围,为了实现上述目标,文章设计了一个双臂机器人在操作人员引导下移动物体的实验.在实验中,机器人的主动臂直接由操作人员通过力/触觉反馈设备进行控制,从动臂能够自动调整其运动状态以配合主动臂的运动,最终,物体在两臂的夹持下,按照主动臂的运动轨迹移动.文章采用了一种基于函数逼近技术(Function Approximation Techniques-FAT)的自适应阻抗力控制器并将其应用到上述实验中从动臂的运动控制,在仿真实验中,通过与PD(比例微分)+前馈补偿控制器的结果比较,验证了所设计的基于FAT的自适应控制器具有较好的控制效果。     

基于自抗扰控制的制导与运动控制一体化设计

针对机动目标的拦截问题,将自抗扰控制的思想用于制导与运动控制一体化设计.其中,基于自抗扰控制的制导律产生在目标机动的情况下使视线角速度快速趋于零所需的制导律;而基于自抗扰控制的运动控制律可以在非线性不确定动态情况下实现所需加速度,从而保证快速有效地完成拦截任务.进一步分析所设计方案对制导与运动控制一体化设计的适用性,并通过计算机仿真验证该方案的有效性.     

同伦论的一个应用——拟人双臂动态协调装配的数学模型

本文在表示刚体运动的4×4矩阵中引入时间参数,由此构造出描述在计算机控制下机器人拟人双臂协调运动的同伦函数,用来统一地、完整地刻划动态协调装配的全部过程。     

仿生机器鱼研究的进展与分析

仿生机器鱼,作为一种高效、高机动的水下机器人,得到研究人员的广泛关注.介绍了国内外在仿生机器鱼推进机理和系统研制方面的主要研究进展.通过分析,归纳总结了仿生机器鱼研究的主要问题,并重点讨论了推进机理、机构优化和运动控制的研究思路.     

具有控制输入约束的轮式移动机器人轨迹跟踪最优保性能控制

针对考虑模型不确定性因素影响及控制输入约束的轮式移动机器人的轨迹跟踪问题,文章提出了一种具有控制输入约束的轨迹跟踪最优保性能控制器.依据实际机器人与虚拟机器人的相对位姿关系建立轨迹跟踪误差动态模型.采用平衡点线性化方法将非线性形式的轨迹跟踪误差动态模型转换为线性形式的轨迹跟踪误差动态模型.通过线性矩阵不等式处理方法,给出了具有鲁棒性能上界的最优保性能控制器的存在条件及设计方法.仿真及实验结果表明,文章所提控制器能较好抑制模型不确定性的影响,提高移动机器人的轨迹跟踪性能和鲁棒性.     

力觉临场感遥操作机器人系统的鲁棒控制

力觉临场感遥操作机器人系统的通信通道中存在通信时延,而且在机器人和环境建模中,系统参数存在不确定性,以致可能造成系统不稳定和操作性能降低.针对通信时延和系统不确定性,建立系统的状态方程,利用鲁棒控制理论,提出用力、位置和速度反馈的控制方法.分析与实验表明,用该方法设计的控制器能使系统鲁棒渐近稳定,而且能使系统完全透明.     

超级画板的程序vs几何画板的迭代

1引言超级画板和几何画板都是动态几何软件,都能在动态中保持图形内在的几何关系不变,能够动态地展现数和形的变化,适合在课堂上使用,是进行探究性学习的有力工具.超级画板(Super Sketchpad)是张景中院士及其团队研发的具有自主知识产权的     

用旋量方法规划机器人运动轨迹

以姿态旋量描述机器人的位置姿态,在对偶空间中通过姿态旋量映射的点规划机器人的终端轨迹,具有直观、简便的独特优点。规划中直接根据跟踪误差进行收敛,提高了轨迹运行的动态精度,并适合于冗余自由度操作器。     

赋范线性空间中动态目标集的最大时间函数的Fréchet次微分

本文考虑赋范线性空间中动态目标集的最大时间函数.该函数由动态的目标集和非空仿射控制集决定,以最远距离函数为其特例.本文建立了动态目标集的最大时间函数的Frechet次微分的上下估计式,该估计式由相应的法锥和控制集的支撑函数表示.     

自主仿生机器鱼水下电场通信的设计与实现

水下机器人由于工作环境的特殊性,导致其通信困难.文章首先设计一套水下电场通信系统,并且将此通信系统集成到仿生机器鱼上.值得注意的是,它解决了多个水下机器人在传统的的通信方式下不能正常通信的问题.其次,针对电流场的物理特性设计了一系列的实验来验证理论的可行性,引入误码率(BER)来评估通信系统的稳定性.最后,通过外部发射装置并应用水下电场通信系统来控制机器鱼的各种游动模态.该通信系统在水下机器人的近距离通信中有很好的应用前景.     

A genetic-fuzzy approach for mobile robot navigation among moving obstacles

In this paper, a genetic-fuzzy approach is developed for solving the motion planning problem of a mobile robot in the presence of moving obstacles. The application of combined soft computing techniques — neural network, fuzzy logic, genetic algorithms, tabu search and others — is becoming increasingly popular among various researchers due to their ability to handle imprecision and uncertainties that are often present in many real-world problems. In this study, genetic algorithms are used for tuning the scaling factors of the state variables (keeping the relative spacing of the membership distributions constant) and rule sets of a fuzzy logic controller (FLC) which a robot uses to navigate among moving obstacles. The use of an FLC makes the approach easier to be used in practice. Although there exist many studies involving classical methods and using FLCs they are either computationally extensive or they do not attempt to find optimal controllers. The proposed genetic-fuzzy approach optimizes the travel time of a robot off-line by simultaneously finding an optimal fuzzy rule base and optimal scaling factors of the state variables. A mobile robot cant then use this optimal FLC on-line to navigate in presence of moving obstacles. The results of this study on a number of problem scenarios show that the proposed genetic-fuzzy approach can produce efficient knowledge base of an FLC for controlling the motion of a robot among moving obstacles.     

On-Line Constrained Predictive Control Algorithm using Multi-Objective Fuzzy-Optimization and a Case Study

Model predictive control (MPC) has been used in process control systems with constraints, however, the constrained optimization problem involved in control systems has generally been solved in practice in a piece-meal fashion. To solve the problem systemically, in this paper, the Multi-Objective Fuzzy-Optimization (MOFO) is used in the constrained predictive control for online applications as a means of dealing with fuzzy goals and fuzzy constraints in control systems. The conventional model predictive control is integrated with the techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. The information regarding the fuzzy goals and the fuzzy constraints of the control problem is combined by using a decision function from the fuzzy theory, so it is possible to aggregate the fuzzy goals and the fuzzy constraints using fuzzy operators, e.g. t-norms, s-norms or the convex sum. It is shown that the model predictive controller based on MOFO allows the designers a more flexible aggregation of the control objectives than the usual weighting sum of squared errors in MPC. The efficiency of the presented algorithm is validated by the visual robot path planning.     

Heuristic fuzzy-neuro network and its application to reactive navigation of a mobile robot

A novel pattern recognition approach to reactive navigation of a mobile robot is presented in this paper. A heuristic fuzzy-neuro network is developed for pattern-mapping between quantized ultrasonic sensory data and velocity commands to the robot. The design goal was to enable an autonomous mobile robot to navigate safely and efficiently to a target position in a previously unknown environment. Useful heuristic rules were combined with the fuzzy Kohonen clustering network (FKCN) to build the desired mapping between perception and motion. This method provides much faster response to unexpected events and is less sensitive to sensor misreading than conventional approaches. It allows continuous, fast motion of the mobile robot without any need to stop for obstacles. The effectiveness of the proposed method is demonstrated in a series of practical tests on our experimental mobile robot.     

Sequentially switched fuzzy-model-based control for wheeled mobile robot with visual odometry

This study focused on the Takagi–Sugeno (T–S) fuzzy-model-based control design for the differentially-driven wheeled mobile robot with visual odometry. The position and posture of the mobile robot are estimated by visual odometry. The polar kinematic model of the mobile robot is exactly converted to the T–S fuzzy model and then the fuzzy control design is synthesized to the fuzzy model. The sequentially switched fuzzy control design includes turning, forward motion as well as position and posture control modes. The stabilization is guaranteed based on the Lyapunov stability criterion. The practical constraints on the visual odometry are also satisfied in the control design. Finally, the experiment results demonstrate the effectiveness of the fuzzy-model-based control design for the mobile robot with visual odometry.     

Application of a rule self-regulating fuzzy controller for robotic deburring on unknown contours

Most metal parts made by machining operations contain burrs, which can be removed by robotic manipulators. Modeling a deburring robot on unknown contours is a relatively difficult task. In this study, we present a novel compliant motion controller that uses a modified on-line rule self-regulating fuzzy control (RSFC) and depends on no mathematical models. In the proposed controller, a Cartesian robot on which a grinding tool is mounted rigidly performs edge following (precision deburring) and chamfering on unknown contours. The manipulator is controlled along the tangential direction of a constrained surface and its cutting force is maintained at a desired level. Experimental results demonstrate the effectiveness of this control strategy in terms of automatically deburring the edges of parts with an unknown geometrical configuration.     

Dynamic motion planning in low obstacle density environments

A fundamental task for an autonomous robot is to plan its own motions. Exact approaches to the solution of this motion planning problem suffer from high worst-case running times. The weak and realistic low obstacle density (L.O.D.) assumption results in linear complexity in the number of obstacles of the free space (Van der Stappen et al., 1997). In this paper we address the dynamic version of the motion planning problem in which a robot moves among polygonal obstacles which move along polylines. The obstacles are assumed to move along constant complexity polylines, and to respect the low density property at any given time. We will show that in this situation a cell decomposition of the free space of size O(n²(n) log² n) can be computed in O(n²(n) log² n) time. The dynamic motion planning problem is then solved in O(n²(n) log³ n) time. We also show that these results are close to optimal.     

Modeling and control of nonlinear systems using novel fuzzy wavelet networks: The output adaptive control approach

This paper proposes an observer based self-structuring robust adaptive fuzzy wave-net (FWN) controller for a class of nonlinear uncertain multi-input multi-output systems. The control signal is comprised of two parts. The first part arises from an adaptive fuzzy wave-net based controller that approximates the system structural uncertainties. The second part comes from a robust H_∞ based controller that is used to attenuate the effect of function approximation error and disturbance. Moreover, a new self structuring algorithm is proposed to determine the location of basis functions. Simulation results are provided for a two DOF robot to show the effectiveness of the proposed method.     

Computed torque control of an under-actuated service robot platform modeled by natural coordinates

The paper investigates the motion planning of a suspended service robot platform equipped with ducted fan actuators. The platform consists of an RRT robot and a cable suspended swinging actuator that form a subsequent parallel kinematic chain and it is equipped with ducted fan actuators. In spite of the complementary ducted fan actuators, the system is under-actuated. The method of computed torques is applied to control the motion of the robot.The under-actuated systems have less control inputs than degrees of freedom. We assume that the investigated under-actuated system has desired outputs of the same number as inputs. In spite of the fact that the inverse dynamical calculation leads to the solution of a system of differential–algebraic equations (DAE), the desired control inputs can be determined uniquely by the method of computed torques.We use natural (Cartesian) coordinates to describe the configuration of the robot, while a set of algebraic equations represents the geometric constraints. In this modeling approach the mathematical model of the dynamical system itself is also a DAE.The paper discusses the inverse dynamics problem of the complex hybrid robotic system. The results include the desired actuator forces as well as the nominal coordinates corresponding to the desired motion of the carried payload. The method of computed torque control with a PD controller is applied to under-actuated systems described by natural coordinates, while the inverse dynamics is solved via the backward Euler discretization of the DAE system for which a general formalism is proposed. The results are compared with the closed form results obtained by simplified models of the system. Numerical simulation and experiments demonstrate the applicability of the presented concepts.     

Wall-following of mobile robot based on fuzzy genetic algorithm to linear interpolating

Traditional fuzzy controller has some disadvantages, such as inferiorly adaptability due to the invariable membership function parameters and too many subjective factors. So in this paper, we firstly put forward a new method to fuzzy inference based on the idea of linear interpolating. This method overcomes the shortcoming of conventional fuzzy controller such as the character of multi-relay and the conflict of rule numbers and real-time. Then we use genetic algorithm to off-line optimize the membership function parameters of fuzzy controller, which is used in the controlling course of mobile robot following straight wall. The result shows the optimizing control strategy is more effective in the aspect of following precision than the traditional fuzzy controller.     

Robot motion classification from the standpoint of learning control

In robot learning control, the learning space for executing the general motions of multi-joint robot manipulators is very complicated. Thus, when the learning controllers are employed as major roles in motion governing, the motion variety requires them to consume excessive amount of memory. Therefore, in spite of their ability to generalize, the learning controllers are usually used as subordinates to conventional controllers or the learning process needs to be repeated each time a new trajectory is encountered. To simplify learning space complexity, we propose, from the standpoint of learning control, that robot motions be classified according to their similarities. The learning controller can then be designed to govern groups of robot motions with high degrees of similarity without consuming excessive memory resources. Motion classification based on using the PUMA 560 robot manipulator demonstrates the effectiveness of the proposed scheme.