Cooperative manipulation of a floating object by some space robots: application of a tracking control method using the transpose of the generalized Jacobian matrix

In future space missions, it is considered that many tasks will be achieved by cooperative motions of space robots. For free-floating space robots with manipulators, we have proposed a digital tracking control method using the transpose of the generalized Jacobian matrix (GJM). In this paper, the tracking control method using the transpose of the GJM is applied to cooperative manipulations of a floating object by space robots. Simulation results show the effectiveness of the control method. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

Cooperative manipulation of a floating object by some space robots with joint velocity controllers: application of a tracking control method using the transpose of the generalized Jacobian matrix

We have studied the cooperative manipulation of floating object by several space robots. For these cooperative motions, we have reported that a tracking control method using the transpose of the generalized Jacobian matrix (GJM) can be utilized for robots with joint torque controllers. For cooperative motions by some robots with joint velocity controllers, we proposed a tracking control method using the transpose of the GJM. Simulation results show the effectiveness of the proposed control method.     

Digital control of space robot manipulators with velocity type joint controller using transpose of generalized Jacobian matrix

For free floating space robots having manipulators, we have proposed a discrete-time tracking control method using the transpose of Generalized Jacobian Matrix (GJM). Control inputs of the control method are joint torques of the manipulator. In this paper, the control method is augmented for angular velocity inputs of the joints. Computer simulations have shown the effectiveness of the augmented method. This work was presented in part and awarded as Best Paper Award at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Fuzzy MTEJ controller with integrator for control of underactuated manipulators

This paper presents control of underactuated robot manipulators in task space utilizing novel algorithm called fuzzy modified transpose effective Jacobian (MTEJ) with integrator term which ensures tracking trajectory in task space with high-quality performance. Although non-model base MTEJ have been introduced before and evaluated for underactuated robots, this method has poor operation against non-linear factors in actuators and joints observed in practical tests like deadband, backlash or Coulomb damping. The contributions of this paper are in twofold. First, to introduce improved MTEJ algorithm with additional integrator term that is efficient to eliminate effects of mentioned factors or other source of steady state error (SSE). Second, using new fuzzy rules to manage its terms to stabilize system with better control properties, the controller is used to make the endeffector to both track a predefined trajectory or set on an exact point. Global stabilization of this control method is proved. Simulation and experimental results are offered which compared tracking performance of the improved fuzzy MTEJ with integrator to other methods for both tracking and point to point (P.T.P) control. Outcomes of these experiments reveal privileges of using fuzzy improved MTEJ in various areas like removing SSE with better control characteristics and low computational efforts.     

实时SAR成像系统矩阵转置方法研究与实现

合成孔径雷达（SAR）是一种高分辨率成像雷达,而矩阵转置是实时SAR成像信号处理中一个很重要的操作,矩阵转置的效率高低将直接决定整个SAR成像信号处理系统的性能。对于矩阵转置,可采用行进列出或列进行出、两页式或三页式转置等方法进行处理,但这些方法处理时间较长,转置效率较低。在现有矩阵转置方法的基础上,提出了一种新的矩阵转置方法。在实际硬件平台上利用提出的矩阵转置方法进行了实时SAR成像处理,所得结果的矩阵转置效率为78%,整个SAR成像处理时间为10秒。测试结果表明,该方法对解决矩阵转置问题是行之有效的。     

Smooth continuous transition between tasks on a kinematic control level: Obstacle avoidance as a control problem

Kinematically redundant robots allow simultaneous execution of several tasks with different priorities. Beside the main task, obstacle avoidance is one commonly used subtask. The ability to avoid obstacles is especially important when the robot is working in a human environment. In this paper, we propose a novel control method for kinematically redundant robots, where we focus on a smooth, continuous transition between different tasks. The method is based on a new and very simple null-space formulation. Sufficient conditions for the tasks design are given using the Lyapunov-based stability discussion. The effectiveness of the proposed control method is demonstrated by simulation and on a real robot. Pros and cons of the proposed method and the comparison with other control methods are also discussed.     

The leader-follower formation control of nonholonomic mobile robots

In this paper, the leader-waypoint-follower formation is constructed based on relative motion states of nonholonomic mobile robots. Since the robots’ velocities are constrained, we proposed a geometrical waypoint in cone method so that the follower robots move to their desired waypoints effectively. In order to form and maintain the formation of multi-robots, we combine stable tracking control method with receding horizon (RH) tracking control method. The stable tracking control method aims to make the robot’s state errors stable and the RH tracking control method guarantees that the convergence of the state errors tends toward zero efficiently. Based on the methods mentioned above, the mobile robots formation can be maintained in any trajectory such as a straight line, a circle or a sinusoid. The simulation results based on the proposed approaches show each follower robot can move to its waypoint efficiently. To validate the proposed methods, we do the experiments with nonholonomic robots using only limited on-board sensor information.     

用小波插值方法实现移动机器人的轨迹追踪控制

基于动力学模型方程，利用小波插值方法，对移动机器人的轨迹追踪控制问题，给出了一种新的方法．该方法在有限时间内，实现了转动速度、前进速度均不为零的期望轨迹追踪．它的突出特点是计算量小、方法简单，期望轨迹可为任意复杂的非线性曲线．在仿真实验里，取得了理想的效果．     

Adaptive-intelligent control of robots and technological equipment of intelligent manufacturing

Methods of adaptive-intelligent control of robots and technological equipment of intelligent manufacturing (IM) with incomplete information on the plant conditions are discussed. Flexible algorithms are proposed for programming and sensor correction of movements which provide adaption to obstacles and uncertainly factors. Robust and adaptive control algorithms which compensate the uncontrollable disturbances and ensure the wanted character of transient process are synthesized. The conversational means and computeraided design technology for multi-microprocessor adaptive control systems of robots and technological equipment are described. The results of using the proposed methods in adaptive-intelligent control systems of robots and flexible manufacturing systems (FMS) for powder metallurgy, laser processing are presented.     

Action control of soccer robots based on simulated human intelligence

A multi-modal action control approach is proposed for an autonomous soccer robot when the bottom hardware is unchangeable. Different from existing methods, the proposed control approach defines actions with the principle of ``perception-planning-action' inspired by human intelligence. Character extraction is used to divide the perception input into different modes. Different control modes are built by combining different control methods for the linear velocity and angular velocity. Based on production rules, the motion control is realized by connecting different perceptions to the corresponding control mode. Simulation and real experiments are conducted with the middle-sized robot Frontier-I, and the proposed method is compared with a proportional-integral-derivative (PID) control method to display its feasibility and performance. The results show that the multi-modal action control method can make robots react rapidly in a dynamic environment.     

A simple PID control for asymptotic visual regulation of robot manipulators

This paper addresses the asymptotic regulation problem of robot manipulators with a vision‐based feedback. A simple image‐based transpose Jacobian proportional‐integral‐derivative (PID) control is proposed. The closed‐loop system formed by the proposed PID control and robot system is shown to be asymptotically stable by using Lyapunov's direct method and LaSalle's invariance theorem. Advantages of the proposed control include the absence of dynamical model parameters in the control law formulation and the control gains are easily chosen according to simple inequalities including some well‐known bounds extracted from robot dynamics and kinematics. Simulations performed on a two degree‐of‐freedom manipulator are provided to illustrate the effectiveness of the proposed approach. Copyright © 2010 John Wiley & Sons, Ltd.     

含驱动器动力学的移动机器人编队自适应控制

针对含有驱动器及编队动力学的多非完整移动机器人编队控制问题,基于领航者-跟随者[l-ψ]控制结构,通过反步法设计了一种将运动学控制器与驱动器输入电压控制器相结合的新型控制策略。采用径向基神经网络（RBFNN）对跟随者及领航者动力学非线性不确定部分进行在线估计,并通过自适应鲁棒控制器对神经网络建模误差进行补偿。该方法不但解决了移动机器人编队控制的参数与非参数不确定性问题,同时也确保了机器人编队在期望队形下对指定轨迹的跟踪;基于Lyapunov方法的设计过程,保证了控制系统的稳定与收敛;仿真结果表明了该方法的有效性。     

Control of leader–follower formation and path planning of mobile robots using Asexual Reproduction Optimization (ARO)

This paper presents the optimal path of nonholonomic multi robots with coherent formation in a leader–follower structure in the presence of obstacles using Asexual Reproduction Optimization (ARO). The robots path planning based on potential field method are accomplished and a novel formation controller for mobile robots based on potential field method is proposed. The efficiency of the proposed method is verified through simulation and experimental studies by applying them to control the formation of four e-Pucks robots (low-cost mobile robot platform). Also the proposed method is compared with Simulated Annealing, Improved Harmony Search and Cuckoo Optimization Algorithm methods and the experimental results, higher performance and fast convergence time to the best solution of the ARO demonstrated that this optimization method is appropriate for real time control application.     

A generalized control framework of assistive controllers and its application to lower limb exoskeletons

Various control methods have been studied for the natural assistance of human motions by exoskeletal robots, i.e., wearable robots for assisting the human motions. For example, impedance control and compliance control are widely used for controlling interaction forces between a human and a robot. When an accurate measurement of the human muscular force is available (e.g., electromyography), a direct use of the estimated human joint torque is possible in the control of an assistive robot. The human motions in a daily living, however, are so complex that they are constituted by multiple phases, such as walking, sitting, and standing, where the walking can be further categorized into multiple sub-phases. Therefore, a single control method cannot be the best option for all the motion phases; a switch in the control algorithms may be necessary for assisting human movements in multiple motion phases. In this paper, a generalized control framework is proposed to incorporate the various assistive control methods in one general controller structure, which consists of Feedforward Disturbance Compensation Control, Reference Tracking Feedback Control, Reference Tracking Feedforward Control, Model-based Torque Control. The proposed control framework is designed taking into consideration of the linearity of each control algorithm, and thus it enables the continuous and smooth switching of assistive control algorithms, and makes it possible to analyze the stability of the overall control loop. The proposed method is implemented into a lower-limb exoskeleton robot and is verified by experimental results.     

一种多非完整移动机器人分布式编队控制方法

本文研究了多非完整移动机器人编队控制算法。在该算法中,参考轨迹被视为虚拟领导者,只有部分机器人可以接收到领导者信息,机器人之间只能进行局部信息交互。利用坐标变换将机器人系统的编队问题转化为变换后系统的一致性问题,在持续激励的条件下,设计了一种分布式控制算法,通过图论与Lyapunov 理论证明了该分布式控制算法可以使移动机器人队伍指数收敛于期望队形,并使队形的几何中心指数收敛到参考轨迹。最后,数值仿真验证了该控制算法的有效性。     

非完整移动机器人目标环绕动态反馈线性化控制

本文针对多个非完整移动机器人对静止或运动目标的环绕追踪问题进行研究.每个机器人仅通过自身和其相邻的机器人的位置与方向信息以及所追踪的目标的位置信息来协调其运动.首先,提出了一种基于动态反馈线性化方法的分布式控制策略,并引入一个控制机器人之间相对角间距的非线性函数,控制机器人间的相对角间距.使多个机器人能够以期望的与目标之间的相对距离、环绕速度和机器人之间的相对角间距对目标进行追踪.然后,利用Lyapunov工具对控制算法进行了渐近稳定性和收敛性分析.最后构建了多移动机器人实验平台,进行了数值仿真和实验验证,仿真和实验的运行结果表明了所提出算法的有效性.     

Region-based shape control for a swarm of robots

This paper presents a region-based shape controller for a swarm of robots. In this control method, the robots move as a group inside a desired region while maintaining a minimum distance among themselves. Various shapes of the desired region can be formed by choosing the appropriate objective functions. The robots in the group only need to communicate with their neighbors and not the entire community. The robots do not have specific identities or roles within the group. Therefore, the proposed method does not require specific orders or positions of the robots inside the region and yet different formations can be formed for a swarm of robots. A Lyapunov-like function is presented for convergence analysis of the multi-robot systems. Simulation results illustrate the performance of the proposed controller.     

A SVM controller for the stable walking of biped robots based on small sample sizes

Conventional machine learning methods such as neural network (NN) uses empirical risk minimization (ERM) based on infinite samples, which is disadvantageous to the gait learning control based on small sample sizes for biped robots walking in unstructured, uncertain and dynamic environments. Aiming at the stable walking control problem in the dynamic environments for biped robots, this paper puts forward a method of gait control based on support vector machines (SVM), which provides a solution for the learning control issue based on small sample sizes. The SVM is equipped with a mixed kernel function for the gait learning. Using ankle trajectory and hip trajectory as inputs, and the corresponding trunk trajectory as outputs, the SVM is trained based on small sample sizes to learn the dynamic kinematics relationships between the legs and the trunk of the biped robots. Robustness of the gait control is enhanced, which is propitious to realize the stable biped walking, and the proposed method shows superior performance when compared to SVM with radial basis function (RBF) kernels and polynomial kernels, respectively. Simulation results demonstrate the superiority of the proposed methods.     

一种小样本支持向量机控制器在两足机器人步态控制的研究

神经网络等传统的机器学习方法是基于样本数目无穷大的经验风险最小化原则,这对非确定环境下有限样本的步态学习控制非常不利．针对两足机器人面临的非确定环境适应性难题,提出了一种基于支持向量机（SVM）的两足机器人步态控制方法,解决了小样本条件下的步态学习控制问题．提出了一种基于混合核的步态回归方法,仿真研究表明了这种方法比全局核和局部核分别单独用于步态学习时有优越性．SVM以踝关节及髋关节的轨迹作为输入,相应的满足ZMP判据的上体轨迹作为输出,利用有限的理想步态样本对机器人上体轨迹与腿部轨迹之间的动态运动关系进行学习,然后将训练好的SVM置入机器人控制系统,从而增强了步态控制的鲁棒性,有利于实现两足机器人在非结构环境下的稳定步行．仿真结果表明了所提方法的优越性．     

基于关节驱动力矩的自由飞行空间机器人捕捉目标控制算法

本文研究了自由飞行空间机器人(FFSR)关节驱动力 矩的求解算法及在其基础上的捕捉目标控制算法．首先,建立了计算FFSR关节驱动力矩的求 解算法;其次结合基于广义雅可比矩阵的分解运动速度控制法提出了一种捕捉目标的力矩控 制算法;最后,计算机仿真验证了本文提出算法的有效性．