基于神经网络的机器人运动模型辨识及其实验研究

针对机器人楚模中不确定因素的影响，采用神经网络辨识机器人输入输出间的非线性关系，建立机器人的运动学模型，为了提高神经网络的辨识速度，基于Elman动态递归网络，通过增加网络输入输出的部分信息，提出一种新的动态神经网络结构——状态廷迟输入动态递归神经网络(SDIDRNN)，提高了网络的学习速度和稳态精度。以PowerCube^TM模块化机器人为研究对象，把根据机器人返回的关节位置信息和利用OPTOTRAK3020三维运动测量系统测得的机器人末端位置信患作为SDIDRNN的学习样本，对包含各种影响因素的机器人运动模型进行辨识，得到了满意的结果，说明了该神经网络的优越性。     

A parameter identification approach using optimal exciting trajectories for a class of industrial robots

In this paper, the problem of finding optimal exciting trajectories for parameter identification of industrial robots is investigated. A cost function of maximizing the minimum singular value of a recursive matrix is used in the optimization procedure. The optimal exciting trajectories obtained is insensitive with respect to the parameter perturbation. The identification accuracy and convergence speed or parameters is improved.     

Extended Kalman filtering and weighted least squares dynamic identification of robot

This paper presents an experimental comparison between the weighted least squares (WLS) estimation and the extended Kalman filtering (EKF) methods for robot dynamic identification. Comparative results and discussion are presented for a SCARA robot, depending on a priori knowledge and data filtering.     

The parameter identification of a population model of China

A mathematical model for predicting and controlling the future trends of the population of China is given. The model used is a discrete bilinear system with 101 dimensions. The model is decomposed, transformed and its parameters are estimated by nonlinear optimization and curve-fitting techniques. Results for both time-invariant and time-varying parameter estimation are obtained. Finally, a successful validation of the population prediction over the past 25 years is given, in which the predicting accuracy of the total population for each year is within about 1%. This is despite the fact that the available statistical data for these years, which include two censuses, are limited, incomplete and contain numerous contradictions.     

Trajectory tracking control of flexible-joint robots

Inverse dynamics control of flexible-joint robots is addressed. It is shown that, in a flexible-joint robot, the acceleration level inverse dynamic equations are singular because the control torques do not have an instantaneous effect on the end-effector accelerations due to the elastic media. Implicit numerical integration methods that account for the higher order derivative information are utilized for solving the singular set of differential equations. The trajectory tracking control law presented linearizes and decouples the system and yields an asymptotically stable fourth order error dynamics for each end-effector degree of freedom. A 3R spatial robot with all joints flexible is simulated to illustrate the performance of the proposed algorithm.     

Simultaneous dynamic optimization: A trajectory planning method for nonholonomic car-like robots

Trajectory planning in robotics refers to the process of finding a motion law that enables a robot to reach its terminal configuration, with some predefined requirements considered at the same time. This study focuses on planning the time-optimal trajectories for car-like robots. We formulate a dynamic optimization problem, where the kinematic principles are accurately described through differential equations and the constraints are strictly expressed using algebraic inequalities. The formulated dynamic optimization problem is then solved by an interior-point-method-based simultaneous approach. Compared with the prevailing methods in the field of trajectory planning, our proposed method can handle various user-specified requirements and different optimization objectives in a unified manner. Simulation results indicate that our proposal efficiently deals with different kinds of physical constraints, terminal conditions and collision-avoidance requirements that are imposed on the trajectory planning mission. Moreover, we utilize a Hamiltonian-based optimality index to evaluate how close an obtained solution is to being optimal.     

Pattern recognition: An alternative to parameter identification in adaptive control

Practical models used in identification of process control processes must be too simplistic to give precise control information. However, these models can be used for adaptation if they are continuously readapted. But the identification then lacks the precision which might justify the analytic elaboration. One alternative has been to use pattern recognition as a means for allowing a computer system to characterize transient response computing readapted parameters which cause the control behavior to approach a desired transient ‘shape’. The paper summarizes work using pattern features as a basis for practice and theory.     

基于运动学和动力学的关节空间轨迹规划

在机器人轨迹优化设计的研究中,轨迹规划是实现机器人高速、高精度运行的核心,合理的轨迹规划不仅要满足机器人的运动学要求,而且要满足机器人的动力学要求,在驱动电机不变的情况下,增加机器人的速度和载荷是轨迹规划的难点。利用先进的python软件对机器人的运行轨迹进行深入的分析,在关节空间内采用五次插值多项式进行运动轨迹拟合,并将动力学模型加入到轨迹规划中。生成的拟合曲线表明,机器人在关节空间和工作空间内的位移、速度、加速度、加加速度曲线连续可导,各时间段的峰值力矩、峰值功率趋于同一数值,能有效的提高运动部件的速度和寿命。     

Gradient dynamic optimization with Legendre chaos

The polynomial chaos approach for stochastic simulation is applied to trajectory optimization, by conceptually replacing random variables with free variables. Using the gradient method, we generate with low computational cost an accurate parametrization of optimal trajectories.     

A dynamic simulator for humanoid robots

Computer simulation is an essential step in the design and construction of various mechanical structures, including biped robots, because it enables rapid testing and virtual prototyping during the construction phase. Although many different simulators are available, this article gives an overview and a motivation for building a new dynamic multibody simulator. The simulator is especially adapted to humanoid robot Archie, developed at the IHRT Institute at the Technical University of Vienna. In addition, it is shown how the simulator can be used not only in the controller design, but also in the online control loop to extend the available sensors: a virtual sensors principle. This work was presented in part at the First European Workshop on Artificial Life and Robtics, Vienna, Austria, July 12–13, 2007     

Joint stiffness identification of six-revolute industrial serial robots

Although robots tend to be as competitive as CNC machines for some operations, they are not yet widely used for machining operations. This may be due to the lack of certain technical information that is required for satisfactory machining operation. For instance, it is very difficult to get information about the stiffness of industrial robots from robot manufacturers. As a consequence, this paper introduces a robust and fast procedure that can be used to identify the joint stiffness values of any six-revolute serial robot. This procedure aims to evaluate joint stiffness values considering both translational and rotational displacements of the robot end-effector for a given applied wrench (force and torque). In this paper, the links of the robot are assumed to be much stiffer than its actuated joints. The robustness of the identification method and the sensitivity of the results to measurement errors and the number of experimental tests are also analyzed. Finally, the actual Cartesian stiffness matrix of the robot is obtained from the joint stiffness values and can be used for motion planning and to optimize machining operations.     

Novel integrated optimization algorithm for trajectory planning of robot manipulators based on integrated evolutionary programming

Optimal trajectory plarmmg for robot manipulators plays an important role in implementing the high productivity for robots. The performance indexes used in optimal trajectory planning are classified into two roam categories:optimum traveling time and optimum mechanical energy of the actuators. The current trajectory planning algorithms are designed based on one of the above two performance indexes. So far, there have been few planning algorithms designed to satisfy two performance indexes simultaneously. On the other hand, some deficiencies arise in the existing integrated optimization algorithms of trajectory planning.In order to overcome those deficiencies, the integrated optimization algorithms of trajectory planning are presented based on the complete analysis for trajectory planning of robot manipulators. In the algorithm, two object functiom are designed based on the specific weight coefficient method and “ideal point” strategy. Moreover, based on the features of optimization problem, the intensified evolutionary programming is proposed to solve the corresponding optimization model. Especially, for the Stanford Robot, the high-quality solutions are found at a lower cost.     

Selection, identification and comparison of industrial robots using digraph and matrix methods

In the present work, a methodology based on digraph and matrix methods is developed for evaluation of alternative industrial robots. A robot selection index is proposed that evaluates and ranks robots for a given industrial application. The index is obtained from a robot selection attributes function, obtained from the robot selection attributes digraph. The digraph is developed considering robot selection attributes and their relative importance for the application considered. A step by step procedure for evaluation of robot selection index is suggested. Coefficients of similarity and dissimilarity and the identification sets are also proposed. These are obtained from the robot selection attributes function and are useful for easy storage and retrieval of the data. Two examples are included to illustrate the approach.     

Time-optimal and jerk-continuous trajectory planning for robot manipulators with kinematic constraints

In this paper a high smooth trajectory planning method is presented to improve the practical performance of tracking control for robot manipulators. The strategy is designed as a combination of the planning with multi-degree splines in Cartesian space and multi-degree B-splines in joint space. Following implementation, under the premise of precisely passing the via-points required, the cubic spline is used in Cartesian space planning to make either the velocities or the accelerations at the initial and ending moments controllable for the end effector. While the septuple B-spline is applied in joint space planning to make the velocities, accelerations and jerks bounded and continuous, with the initial and ending values of them configurable. In the meantime, minimum-time optimization problem is also discussed. Experimental results show that, the proposed approach is an effective solution to trajectory planning, with ensuring a both smooth and efficiency tracking performance with fluent movement for the robot manipulators.     

Direct identification of structural parameters from dynamic responses with neural networks

A novel neural network-based strategy is proposed and developed for the direct identification of structural parameters (stiffness and damping coefficients) from the time-domain dynamic responses of an object structure without any eigenvalue analysis and extraction and optimization process that is required in many identification algorithms for inverse problems. Two back-propagation neural networks are constructed to facilitate the process of parameter identifications. The first one, called emulator neural network, is to model the behavior of a reference structure that has the same overall dimension and topology as the object structure to be identified. After having been properly trained with the dynamic responses of the reference structure under a given dynamic excitation, the emulator neural network can be used as a nonparametric model of the reference structure to forecast its dynamic response with sufficient accuracy. However, when the parameters of the reference structure are modified to form a so-called associated structure, the dynamic responses forecast by the network will differ from the simulated responses of the associated structure. Their difference can be assessed with a proposed root mean square (RMS) difference vector for both velocity and displacement responses. With the associated structural parameters and their corresponding RMS difference vectors, another network, called parametric evaluation neural network, can be trained. In this study, several 5-story frames are considered as example object structures with simulated displacement and velocity time histories that mimic the measured dynamic responses in practice. The performance of the proposed strategy has been demonstrated quite satisfactorily; the error for the estimation of each stiffness or damping coefficient is less than 10% even in the presence of 7% noise. Numerical simulations show that the accuracy of the identified parameters can be significantly improved by injecting noise in the training patterns for the parametric evaluation neural network. The proposed strategy is extremely efficient in computation and thus has potential of becoming a practical tool for near real time monitoring of civil infrastructures.     

Eco-programming of industrial robots for sustainable manufacturing via dynamic time scaling of trajectories

Nowadays, Industrial Robots (IRs) have become widespread in many manufacturing industries. Medium and high payload IRs cover a significant percentage of the overall factory Energy Consumption (EC). This article focuses on the IRs eco-programming to minimize the EC of a robot, being energy efficiency one of the fundamental aims of sustainable manufacturing. By leveraging well-known trajectory scaling methods, this research develops a novel, versatile, fast, and efficient process to define the IR optimal velocity/acceleration profile in time, keeping the geometry of the trajectory fixed. A complete IR system model that founds application in various types of 6 degrees of freedom articulated manipulators has been developed by considering electrical motors, actuator drive systems, and controller cabinet losses. A new optimization technique based on Dynamic Time Scaling of trajectories is presented, and the obtained results are compared with other methods used in the scientific literature. When performing critical path analysis, the EC of the robot system is estimated to be cut down, being the robot motion time fixed, by about 13% through this novel approach. The model has been validated through commercial software, and the proposed optimization algorithm has been implemented in a user-friendly interface tool.     

An improved dynamic modeling of a multibody system with spherical joints

A dynamic modeling of multibody systems having spherical joints is reported in this work. In general, three intersecting orthogonal revolute joints are substituted for a spherical joint with vanishing lengths of intermediate links between the revolute joints. This procedure increases sizes of associated matrices in the equations of motion, thus increasing computational burden of an algorithm used for dynamic simulation and control. In the proposed methodology, Euler parameters, which are typically used for representation of a rigid-body orientation in three-dimensional Cartesian space, are employed to represent the orientation of a spherical joint that connects a link to its previous one providing three-degree-of-freedom motion capability. For the dynamic modeling, the concept of the Decoupled Natural Orthogonal Complement (DeNOC) matrices is utilized. It is shown in this work that the representation of spherical joints motion using Euler parameters avoids the unnecessary introduction of the intermediate links, thereby no increase in the sizes of the associated matrices with the dynamic equations of motion. To confirm the efficiency of the proposed representation, it is illustrated with the dynamic modeling of a spatial four-bar Revolute-Spherical–Spherical-Revolute (RSSR) mechanism, where the CPU time of the dynamic modeling based on proposed methodology is compared with that based on the revolute joints substitution. Finally, it is explained how a complex suspension and steering linkage can be modeled using the proposed concept of Euler parameters to represent a spherical joint.     

Joint acceleration feedback control for robots: analysis, sensing and experiments

Independent joint control for robots is enhanced to suppress the dynamic couplings by incorporating an acceleration feedback loop that is designed in terms of its stability and ability in resisting the dynamic coupling disturbances. The sensing and modeling of joint acceleration via linear accelerometers is dealt from the viewpoint of practical implementation. Extensive experiments are conducted on a three-link direct drive robot, to mainly investigate the ability of the independent joint controller with the acceleration loop in resisting the coupling torque, and demonstrating the enhanced trajectory tracking performance. Results are given against the ones obtained by conventional independent joint control without the acceleration feedback control.     

Dealing with internal and external perturbations on walking robots

Up to now, walking robots have been working outdoors under favorable conditions and using very large stability margins to cope with natural environments and intrinsic robot dynamics that can cause instability in these machines when they use statically-stable gaits. The result has been very slow robots prone to tumble down in the presence of perturbations. This paper proposes a novel gait-adaptation method based on the maximization of the Normalized Dynamic Energy Stability Margin. This method enables walking-machine gaits to adapt to internal (robot dynamics) and external (environmental) perturbations, including the slope of the terrain, by finding the gait parameters that maximize robot stability. The adaptation method is inspired in the natural gait adaptation carried out by humans and animals to balance external forces or the effect of sloping terrain. Experiments with the SILO4 quadruped robot are presented and show how robot stability is more robust when the proposed approach is used for different external forces and sloping terrains. Using the proposed gait-adaptation approach the robot is able to withstand external forces up to 58% the robot weight and 25-degree slopes.

An overview of standards development for robots and robotic systems

This paper is a summary of the standards development work which has been completed, is in process, or is under consideration in the area of robot and robotic systems standards including general areas such as environment, general requirements, data communications, general electrical and control requirements, control/machine interface, and safety. The intent of this paper is to show the interrelationship of various standards groups and organizations who are doing or considering doing work in this field and how the various standards projects or published standards are interrelated. Both national and international work is addressed. The paper also provides information on how the work in robots standards fits with other work being done in the area of industrial automation.