On the trajectory tracking control of industrial SCARA robotmanipulators

In this paper, the authors discuss, from an experimental point of view, the use of different control strategies for the trajectory tracking control of an industrial selective compliance assembly robot arm robot, which is one of the most employed manipulators in industrial environments, especially for assembly tasks. Specifically, they consider decentralized controllers such as proportional-integral-derivative-based and sliding-mode ones and model-based controllers such as the classical computed-torque one and a neural-network-based controller. A simple procedure for the estimation of the dynamic model of the manipulator is given. Experimental results provide a detailed framework about the cost/benefit ratio regarding the use of the different controllers, showing that the performance obtained with decentralized controllers may suffice in a large number of industrial applications, but in order to achieve low tracking errors also for high-speed trajectories, it might be convenient to adopt a neural-network-based control scheme, whose implementation is not particularly demanding     

Visual control of vehicles using two-view geometry

This paper addresses the problem of visual control for mobile robots with nonholonomic motion constraints. The vision system consists of a fixed camera mounted on the robot and no odometry or additional sensors are used. We consider the usual framework in which the target is defined by an image taken previously at the desired position. Then, the control law drives the robot from the initial position to the desired one by processing image information extracted from the current and target images. We present a new approach consisting in a switching control law based on the two-view geometry without scene constraints. Our main contribution is that two controllers are defined and combined in the switching control law. One is based on the epipolar geometry and the other on the homography model. Both models have well-known degenerate cases or particular situations in which the corresponding control fails when used alone. Nevertheless, the designed approach takes advantage of both models avoiding the drawbacks of each one and allowing a smooth motion of the robot. Experimental evaluation is presented to show the performance of the approach.     

An energy exchanging and dropping-based model-free output feedback crane control method

For the overhead crane control problem, velocity-related terms (corresponding to full-state feedback) are generally required in the designed control systems for damping injection to achieve (asymptotic) stability. However, it is known that velocity signals may be noisy or even unmeasurable. Also, most existing controllers require full or partial plant physical parameters like rope length or load mass. To resolve these issues, a model-free energy exchanging and dropping-based control law is proposed to achieve output (only position/swing-angle) feedback control for overhead cranes. We synthesize a total energy function, consisting of the (generalized) crane energy and the controller energy, to render it to achieve its (local) minimum at the desired equilibrium point. The proposed control law is dynamically generated by an artificial block-spring system, which exchanges energy with the crane dynamics and then drops the energy via an elegant dropping mechanism to gradually attenuate the total energy. The corresponding stability and convergence analysis is implemented using some Lyapunov-like analysis. Simulation and experimental results suggest the effectiveness and feasibility of the proposed method for crane control, in terms of rapid swing suppression, efficient trolley positioning, as well as increased robustness.     

结构光内参数和机器人手眼关系的同时标定

为实现对复杂工件的快速在线测量,构建了基于结构光的机器人视觉测量系统,提出一种该系统中结构光测头内参数和手眼关系同时标定的方法。标定时,在机器人工作空间内固定一个平面靶标作为参考物,精确控制机器人末端带动结构光测头做平移运动和变位姿运动,获取不同位姿下的靶标图像。通过平移运动,同时标定结构光测头内参数和手眼关系中的旋转矩阵;通过变位姿运动,同时标定摄像机内参数和手眼关系中的平移向量。考虑到旋转矩阵和平移向量分离标定会存在误差累积,采用非线性优化方法对手眼关系做进一步优化。对标准球及工件进行扫描测量实验,实验结果表明该标定方法简单高效,能保证测量系统具有较高的测量精度,适用于工业现场。     

Hybrid fault adaptive control of a wheeled mobile robot

A fault adaptive control methodology for mobile robots is presented. The robot is modeled as a continuous system with a supervisory controller. The physical processes of the robot are modeled using bond graphs, and this forms the basis of a combined qualitative reasoning and quantitative model-based estimation scheme for online fault detection and isolation during robot operation. A hierarchical-control accommodation framework is developed for the supervisory controller that determines a suitable control strategy to accommodate the isolated fault. It is shown that for small degradations in actuation effort, a robust controller achieves fault accommodation without significant loss of performance. However, for larger faults, the supervisor needs to switch among several controllers to maintain acceptable performance. The switching stability among a set of trajectory tracking controllers is presented. Simulation results verify the proposed fault adaptive control technique for a mobile robot.     

A stable self-organizing fuzzy controller for robotic motioncontrol

It is well known that robotic manipulators are highly nonlinear coupling dynamic systems. It is difficult to establish an appropriate mathematical model for the design of a model-based controller. Although fuzzy logic control has a model-free feature, it still needs time-consuming work for the rules bank and fuzzy parameters adjustment. In this paper, a stable self-organizing fuzzy controller (SOFC) is proposed to manipulate the motion trajectory of a 5-degrees-of-freedom robot. This approach has a learning ability for responding to the time-varying characteristic of a robot. Its control rules bank can be established and modified continuously by online learning with zero initial fuzzy rules. In addition, this control strategy has effectively improved the stability problem of a previous SOFC. The experimental results show that this intelligent controller has a stable learning ability and good motion control capability     

Adaptive sliding controller with self-tuning fuzzy compensation for vehicle suspension control

Since the hydraulic actuating suspension system has nonlinear and time-varying behavior, it is difficult to establish an accurate dynamic model for a model-based sliding mode control design. Here, a novel model-free adaptive sliding controller is proposed to suppress the position oscillation of the sprung mass in response to road surface variation. This control strategy employs the functional approximation technique to establish the unknown function for releasing the model-based requirement. In addition, a fuzzy scheme with online learning ability is introduced to compensate the functional approximation error for improving the control performance and reducing the implementation difficulty. The important advantages of this approach are to achieve the sliding mode controller design without the system dynamic model requirement and release the trial-and-error work of selecting approximation function. The update laws for the coefficients of the Fourier series functions and the fuzzy tuning parameters are derived from a Lyapunov function to guarantee the control system stability. The experimental results show that the proposed control scheme effectively suppresses the oscillation amplitude of the vehicle sprung mass corresponding to the road surface variation and external uncertainties, and the control performance is better than that of a traditional model-based sliding mode controller.     

基于动态预测目标轨迹和围捕点的多机器人围捕算法

为使围捕机器人快速地围捕移动目标,提出了一种基于动态预测目标轨迹和围捕点的多机器人围捕算法.随着目标的移动,动态更新采样点,用多项式拟合预测短期内目标可能到达的位置,并建立目标的安全域以禁止围捕机器人进入,从而避免目标主动逃逸.采用协商法为各机器人分配合适的期望围捕点,各机器人采用多侦查蚁协作算法迅速前往期望围捕点从而...     

Stabilization and path following of a single wheel robot

We have developed a single wheel, gyroscopically stabilized robot. This is a novel concept for a mobile robot that provides dynamic stability for rapid locomotion. The robot is a sharp-edged wheel actuated by a spinning flywheel for steering and a drive motor for propulsion. The spinning flywheel acts as a gyroscope to stabilize the robot and it can be steered by tilting. This robot is nonholonomic in nature, underactuated and inherently unstable in the lateral direction. In this paper, we first develop a three-dimensional (3-D) nonlinear dynamic model and investigate the dynamic characteristics of the robot. We conduct simulations and real-time experiments to verify the model. Both simulations and experiments show that the flywheel has a significant stabilizing effect on the robot. Then, we can decouple the longitudinal and lateral motions of the robot by linearization. We propose a linear state feedback to stabilize the robot at different lean angles, so as to control the steering velocity of the robot indirectly, because the robot steers only by leaning itself to a predefined angle. For the task of path following, we design a controller for tracking any desired straight line without falling. In the controller, we first design the linear and steering velocities for driving the robot along the desired straight line by controlling the path curvature. We then apply the linear state feedback to stabilize the robot at the predefined lean angle such that the resulting steering velocity of the robot converges to the given steering velocity. This work is a significant step toward fully autonomous control of such a dynamically stable but statically unstable system.     

Teleoperation of a robot manipulator using a vision-based human-robot interface

Remote teleoperation of a robot manipulator by a human operator is often necessary in unstructured dynamic environments when human presence at the robot site is undesirable. Mechanical and other contacting interfaces used in teleoperation require unnatural human motions for object manipulation tasks or they may hinder human motion. Previous vision-based approaches have used only a few degrees of freedom for hand motion and have required hand motions that are unnatural for object manipulation tasks. This paper presents a noncontacting vision-based method of robot teleoperation that allows a human operator to communicate simultaneous six-degree-of-freedom motion tasks to a robot manipulator by having the operator perform the three-dimensional human hand-arm motion that would naturally be used to complete an object manipulation task. A vision-based human-robot interface is used for communication of human motion to the robot and for feedback of the robot motion and environment to the human operator. Teleoperation under operator position control was performed with high accuracy in object placement on a target. Semi-autonomous traded and shared control using robot-vision guidance aided in achieving a more accurate positioning and orientation of the end-effector for object gripping tasks.     

Intelligent control of robotic manipulators: experimental study using neural networks

In recent years there has been increasing interest in universal model-free controllers. These controllers using neural networks learn about the systems they are controlling on-line, and thus automatically improve their performance. There has been a good deal of research on the use of neural networks for control, although most of the articles have been ad hoc discussions lacking theoretical proofs and repeatable design algorithms. In this paper experimental results on the control of robotic manipulator using neural networks have been provided and it has been demonstrated that neural networks do indeed fulfill the promise of providing model-free learning controllers for robotic systems and provide an excellent alternative for the control of robotic manipulators.     

Online statistical model recognition and State estimation for autonomous compliant motion

Autonomous execution of robot tasks requires the ability to deal online with uncertainties such as partially unknown environments, inaccurate models, and measurement noise. This is especially true for the execution of motions maintaining stiff contacts ("compliant motions"), as contact forces become very high even for small position errors. The autonomy during compliant motion tasks is based on i) a force controller, dealing with small misalignments and keeping the contact forces within safe limits, and ii) an estimator, which recognizes the model (e.g., the type of contact) and estimates the system state (e.g., the relative position of the contacting objects). This paper focuses on Bayesian model-based solutions to the model recognition problem. We discuss Bayesian hypothesis testing and practical approximations. Experimental results are provided for two autonomous-compliant motion tasks by applying consistency testing and likelihood ratio testing. The system state is estimated simultaneously with the model recognition. This estimation is performed by the Iterated Extended Kalman filter for (approximate) linear problems and by the nonminimal state Kalman filter for nonlinear problems.     

Multi-hierarchy interaction control of a redundant robot using impedance learning

The control of robots with a compliant joint motion is important for reducing collision forces and improving safety during human robot interactions. In this paper, a multi-hierarchy control framework is proposed for the redundant robot to enable the robot end-effector to physically interact with the unknown environment, while providing compliance to the joint space motion. To this end, an impedance learning method is designed to iteratively update the stiffness and damping parameters of the end-effector with desired performance. In addition, based on a null space projection technique, an extra low stiffness impedance controller is included to improve compliant joint motion behaviour when interaction forces are acted on the robot body. With an adaptive disturbance observer, the proposed controller can achieve satisfactory performance of the end-effector control even with the external disturbances in the joint space. Experimental studies on a 7 DOF Sawyer robot show that the learning framework can not only update the target impedance model according to a given cost function, but also enhance the task performance when interaction forces are applied on the robot body.     

An adaptive neurocontroller using RBFN for robot manipulators

In recent years, neural networks have fulfilled the promise of providing model-free learning controllers for nonlinear systems; however, it is very difficult to guarantee the stability and robustness of neural network control systems. This paper proposes an adaptive neurocontroller for robot manipulators based on the radial basis function network (RBFN). The RBFN is a branch of neural networks and is mathematically tractable. Therefore, we adopt the RBFN to approximate nonlinear robot dynamics. The RBFN generates control input signals based on the Lyapunov stability that is often used in the conventional control schemes. A saturation function is also chosen as an auxiliary controller to guarantee the stability and robustness of the control system under the external disturbances and modeling uncertainties.     

An output-based controller with two operational modes for a robotwith uncertain parameters in set-point regulation tasks

This paper presents a control approach for the set-point regulation task of a rigid robot with uncertain parameters. The controller strategy is based on two operational modes. During the first mode, the controller drives the robot toward a small neighborhood of the equilibrium point, while in the second mode, the robot converges exponentially to the final target. The proposed control scheme is associated with simple linear controllers, it applies position measurements only, and accounts for the system uncertainties and the unknown payload. Friction is included in the model. Simulation and experimental results are demonstrated     

Robot programming

The industrial robot's principal advantage over traditional automation is programmability. Robots can perform arbitrary sequences of pre-stored motions or of motions computed as functions of sensory input. This paper reviews requirements for and developments in robot programming systems. The key requirements for robot programming systems examined in the paper are in the areas of sensing, world modeling, motion specification, flow of control, and programming support. Existing and proposed robot programming systems fall into three broad categories: guiding systems in which the user leads a robot through the motions to be performed, robot-level programming systems in which the user writes a computer program specifying motion and sensing, and task-level programming systems in which the user specifies operations by their desired effect on objects. A representative sample of systems in each of these categories is surveyed in the paper.     

Task-specific optimal simultaneous kinematic, dynamic, and controldesign of high-performance robotic systems

A task-specific optimal simultaneous kinematic, dynamic and control design approach is proposed for high-performance computer-controlled machines, such as robots. This mechatronics design approach is based on the trajectory pattern method and a fundamentally new design philosophy that such machines, in general, and ultrahigh-performance machines, in particular, must only be designed to perform a class or classes of motions effectively. In the proposed approach, given the structure of the manipulator, its kinematic, dynamic, and control parameters are optimized simultaneously with the parameters that describe a selected trajectory pattern with which the desired class(es) of task(s) can best be performed. In one example, a weighted sum of the norms of the higher harmonics appearing in the actuating torques and the integral of the position and velocity tracking errors are used to form the optimality criterion. The selected optimality criterion should yield a system that is optimally designed to accurately follow the specified trajectory at high speed. Other objective functions can be readily formulated to synthesize systems for optimal performance. Based on the developed design methodology, a two-degrees-of-freedom robot manipulator with a closed-loop chain is optimally designed and constructed for point-to-point motions. The preliminary results of experiments indicate that the robot can, in fact, execute point-to-point motions rapidly and with minimal residual vibration. The potentials of the developed method and its implementation for generally defined motion patterns are discussed     

Generalized impedance control of robot for assembly tasks requiringcompliant manipulation

To perform assembly tasks requiring compliant manipulation, the robot must follow a motion trajectory and exert an appropriate 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 the motion errors and interaction force errors is introduced such that the contact force can be commanded and controlled. With generalized impedance control, the robot can behave with a desired dynamic characteristic when it interacts with the environment. To ensure the success of the assembly, a strategy during task planning which takes into consideration the interrelation between motion and force trajectories as well as contact compliance is introduced. The generalized impedance control method is applied to the prismatic joint of a selective compliance assembly robot arm (SCARA) robot for inserting a printed circuit board (PCB) into an edge connector socket. Depending on the progress of the parts joining operation, various amount of interaction forces are generated which have to be accommodated. It is demonstrated that an assembly strategy which consists of a sequence of carefully planned target impedance 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 well-established position control scheme as well as the original impedance control method     

基于底部排气弹的多重命中体制

希望常规高炮在拦阻射击时能够多重命中,而这种命中体制的本质是通过改变初速来达到控制弹头飞行时间,使其满足多重命中的条件,但是由于固定的、多级分号装药、次序发射,而不能在任选的瞬时实现同时命中,影响了它的适用性,因此提出一种基于底部排气弹的多重命中体制,并进行了理论分析,核心是通过控制底排延时点火时间,使弹头的飞行时间曲线满足多重命中条件,从而实现顺序发射的多发弹头同时命中目标.给出了问题求解的实现算法,此算法的核心是求解一个射角未知、终点受约束的两点边值问题,最终目的是获得同频射击条件下的底排延时点火时间.最后的仿真结果表明基于底排的多重命中体制具有可实现性,对发掘常规高炮的潜力有积极作用.