Singularity loci of planar parallel manipulators with revolute actuators

The determination of the singularity loci of planar parallel manipulators is addressed in this paper. The inverse kinematics of two kinds of planar parallel manipulators (a two-degree-of-freedom manipulator and a three-degree-of-freedom manipulator) are first computed and their velocity equations are then derived. At the same time, the branches of the manipulators are distinguished by the introduction of a branch index K_i. Using the velocity equations, the singularity analysis of the manipulators is completed and expressions which represent the singularity of the manipulators are obtained. A polynomial form of the singularity loci is also derived. For the first type of singularity of parallel manipulators, the singularity locus is obtained by finding the workspace limits of the manipulators. For the second type of singularity, the loci are obtained through the solution of nonlinear algebraic equations obtained from the velocity analysis. Finally, the graphical representation of the complete singularity loci of the manipulators is illustrated with examples. The algorithm introduced in this paper allows the determination of the singularity loci of planar parallel manipulators with revolute actuators, which has been elusive to previous approaches.     

A multi-level optimization methodology for determining the dextrous workspaces of planar parallel manipulators

A numerical multi-level optimization methodology is proposed for determining dextrous workspaces of 3-degree-of-freedom (3-dof) planar parallel manipulators, in which it is required that at any point within the workspace, the manipulator is able to assume any orientation in a specified range. The method starts by finding a single initial point on the boundary of the dextrous workspace. This first stage requires the successive solution of three separate optimization sub-problems, where the evaluation of the objective function for the second problem and the constraint functions in the third problem are determined by the solution of appropriate optimization problems at a lower level. Once the boundary point is identified, further successive points along the dextrous workspace boundary are traced by the application of the so-called chord method. In the latter procedure, the determination of each successive boundary point is also obtained via a constrained optimization problem, where the constraint functions are again evaluated via the solution of an optimization problem at a lower level. The proposed method is illustrated by its successful application to three different manipulator design geometries, and for various ranges of dexterity. An abbreviated version of this paper was presented at the 5th ASMO UK/ISSMO conference on Engineering Design Optimization, Stratford upon Avon, UK, July 12–13, 2004.     

Real-time inverse dynamics control of parallel manipulators using general-purpose multibody software

This work deals with the problem of computing the inverse dynamics of complex constrained mechanical systems for real-time control applications. The main goal is the control of robotic systems using model-based schemes in which the inverse model itself is obtained using a general purpose multibody software, exploiting the redundant coordinate formalism. The resulting control scheme is essentially equivalent to a classical computed torque control, commonly used in robotics applications. This work proposes to use modern general-purpose multibody software to compute the inverse dynamics of complex rigid mechanisms in an efficient way, so that it suits the requirements of realistic real-time applications as well. This task can be very difficult, since it involves a higher number of equations than the relative coordinates approach. The latter is believed to be less general, and may suffer from topology limitations. The use of specialized linear algebra solvers makes this kind of control algorithms usable in real-time for mechanism models of realistic complexity. Numerical results from the simulation of practical applications are presented, consisting in a “delta” robot and a bio-mimetic 11 degrees of freedom manipulator controlled using the same software and the same algorithm.     

Decrease of force capabilities as an index of upper limb fatigue

The aim of the study was to assess upper limb fatigue on the basis of the force change index (FCI), which expresses changes in developed force, and to demonstrate that this index differentiates muscle fatigue of the upper limb depending on external load. The study was performed on ten young men in 12 conditions of external load. Ten conditions characterized repetitive work in a two-period cycle, in which both or one of the periods were loaded, and two conditions characterized continuous work with constant load. The participants tried to maintain hand-grip force at an imposed level during a determined time in the standard upper limb posture. Changes in values of recorded force exerted during successive cycles were approximated by a regression function. In most cases there was a strong correlation between the measurement data and the logarithmic regression curve. However, several cases of lower external load showed absence of such correlation. In 75% of the cases, there were statistically significant differences between the values of FCI calculated for individual conditions of external load. That means that FCI not only expresses muscle fatigue quantitatively but also points to the differences in upper limb fatigue resulting from differences in the external load. The study results have shown that the developed index (FCI) can be applied for fatigue assessment and discrimination with a more sophisticated model of a repetitive task than just a simple two-period work and rest model.     

基于GPU的并行协同差分进化算法研究

针对大规模高维数复杂非线性函数优化的问题,提出一种新的基于GPU的协同差分进化算法。该方法将协同进化的思想引入启发式差分进化算法,随机分解大规模计算问题,利用GPU处理数据的并行性,同步计算分解后的子问题,加快算法的精度和收敛速度。实验对比结果表明,所提出的基于GPU的协同差分进化算法对大规模非线性函数优化具有更高的精度和效率。     

一种改进的多目标约束优化差分进化算法

提出一种新的多目标优化差分进化算法用于求解约束优化问题.该算法利用佳点集方法初始化个体以维持种群的多样性.将约束优化问题转化为两个目标的多目标优化问题.基于Pareto支配关系,将种群分为Pareto子集和Non-Pareto子集,结合差分进化算法两种不同变异策略的特点,对Non-Pareto子集和Pareto子集分别采用DE/best/1变异策略和DE/rand/1变异策略.数值实验结果表明该算法具有较好的寻优效果.     

Orthogonal parallel MCMC methods for sampling and optimization

Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In order to foster better exploration of the state space, specially in high-dimensional applications, several schemes employing multiple parallel MCMC chains have been recently introduced. In this work, we describe a novel parallel interacting MCMC scheme, called orthogonal MCMC (O-MCMC), where a set of “vertical” parallel MCMC chains share information using some “horizontal” MCMC techniques working on the entire population of current states. More specifically, the vertical chains are led by random-walk proposals, whereas the horizontal MCMC techniques employ independent proposals, thus allowing an efficient combination of global exploration and local approximation. The interaction is contained in these horizontal iterations. Within the analysis of different implementations of O-MCMC, novel schemes in order to reduce the overall computational cost of parallel Multiple Try Metropolis (MTM) chains are also presented. Furthermore, a modified version of O-MCMC for optimization is provided by considering parallel Simulated Annealing (SA) algorithms. Numerical results show the advantages of the proposed sampling scheme in terms of efficiency in the estimation, as well as robustness in terms of independence with respect to initial values and the choice of the parameters.     

Experimental kinematic calibration of parallel manipulators using a relative position error measurement system

Because of errors in the geometric parameters of parallel robots, it is necessary to calibrate them to improve the positioning accuracy for accurate task performance. Traditionally, to perform system calibration, one needs to measure a number of robot poses using an external measuring device. However, this process is often time-consuming, expensive and difficult for robot on-line calibration. In this paper, a methodical way of calibration of parallel robots is introduced. This method is performable only by measuring joint variable vector and positioning differences relative to a constant position in some sets of configurations that the desired positions in each set are fixed, but the moving platform orientations are different. In this method, measurements are relative, so it can be performed by using a simple measurement device. Simulations and experimental studies on a Hexaglide parallel robot built in the Sharif University of Technology reveal the convenience and effectiveness of the proposed robot calibration method for parallel robots.     

A hybrid differential evolution augmented Lagrangian method for constrained numerical and engineering optimization

We present a new hybrid method for solving constrained numerical and engineering optimization problems in this paper. The proposed hybrid method takes advantage of the differential evolution (DE) ability to find global optimum in problems with complex design spaces while directly enforcing feasibility of constraints using a modified augmented Lagrangian multiplier method. The basic steps of the proposed method are comprised of an outer iteration, in which the Lagrangian multipliers and various penalty parameters are updated using a first-order update scheme, and an inner iteration, in which a nonlinear optimization of the modified augmented Lagrangian function with simple bound constraints is implemented by a modified differential evolution algorithm. Experimental results based on several well-known constrained numerical and engineering optimization problems demonstrate that the proposed method shows better performance in comparison to the state-of-the-art algorithms.     

Self-adapting control parameters modified differential evolution for trajectory planning of manipulators

Control parameters of original differential evolution （DE） are kept fixed throughout the entire evolutionary process. However, it is not an easy task to properly set control parameters in DE for different optiinization problems. According to the relative position of two different individual vectors selected to generate a difference vector in the searching place, a self-adapting strategy for the scale factor F of the difference vector is proposed. In terms of the convergence status of the target vector in the current population, a self-adapting crossover probability constant CR strategy is proposed. Therefore, good target vectors have a lower CFI while worse target vectors have a large CFI. At the same time, the mutation operator is modified to improve the convergence speed. The performance of these proposed approaches are studied with the use of some benchmark problems and applied to the trajectory planning of a three-joint redundant manipulator. Finally, the experiment results show that the proposed approaches can greatly improve robustness and convergence speed.     

Robust scheme of global parallel force/position regulators for robot manipulators under environment uncertainty

A simple robust scheme of parallel force/position control is proposed in this paper to deal with two problems for non-planar constraint surface and nonlinear mechanical feature of environment： i） uncertainties in environment that are usually not available or difficult to be determined in most practical situations; ii） stability problem or/and integrator windup due to the integration of force error in the force dominance rule in parallel force/position control. It shows that this robust scheme is a good alternative for anti-windup. In the presence of environment uncertainties, global asymptotic stability of the resulting closed-loop system is guaranteed; it environment with complex characteristics. Finally, numerical robot manipulator. also shows robustness of the proposed controller to uncertain simulation verifies results via contact task of a two rigid-links     

多目标混沌差分进化算法

将差分进化算法用于多目标优化问题,提出了多目标混沌差分进化算法(CDEMO).该算法利用混沌序列初始化种群,并用混沌备用种群进行替换操作.该操作不仅起到了维持非劣最优解集均匀性的作用,而且增强了算法的搜索功能.对CDEMO的性能进行研究,数值实验结果表明了CDEMO的有效性.     

Intelligent control for avoiding the joint limits of redundant planar manipulators

This article presents an intelligent control system for a redundant manipulator to avoid physical limits such as joint angle limits and joint velocity limits. In this method, a back-propagation neural network (NN) is introduced for the kinematic inversion of the manipulator. Since this inverse kinematics has an infinite number of joint angle vectors, a fuzzy-neuro system is constructed to provide an approximate value for that vector. This vector is fed into the NN as a hint input vector in order to guide the output of the NN within the self-motion. Simulations and a comparative study are made based on a four-link redundant manipulator to prove the efficacy of the proposed control system.     

基于差分进化的粒子群算法的鼓式制动器多目标优化设计

为了提高鼓式制动器的设计水平,建立了一种以制动鼓体积最小和制动器温升最低为目标的鼓式制动器多目标优化模型,并使用一种基于差分进化的粒子群算法进行优化设计.为了进一步增加算法多样性,该算法将差分进化策略引入到多目标粒子群算法中;为了避免粒子飞行速度过快,陷入局部最优解,加入一种速度控制策略.通过无约束的ZDT系列和三个有约束的基准测试函数进行测试,并与其他四种算法进行对比实验,证明该算法在收敛性和多样性方面都占优,在工程实例的求解结果表明了算法的工程可行性.     

An operator allocation optimization model for balancing control of the hybrid assembly lines using Pareto utility discrete differential evolution algorithm

This paper investigates the operator allocation problems (OAP) with jobs sharing and operator revisiting for balance control of a complicated hybrid assembly line which appears in the apparel sewing manufacturing system. Multiple objectives and constraints for the problem are formulated. The utility function is employed to deal with the difficulty of combining several conflicting and incommensurable objectives into one overall measure. An optimization model combining the Pareto utility discrete differential evolution (PUDDE) algorithm and the embedded discrete event simulation (DES) model is proposed to solve the OAPs. The PUDDE algorithm is an improved discrete differential evolution approach used with the Pareto utility selection strategy, which extends the real-value differential evolution to handle the discrete-value vector by introducing two modified operators, namely the subtraction and addition operators. During the optimization process, the embedded DES model is used to evaluate the performance objectives by analyzing the dynamic behaviors of the hybrid assembly lines, which tackles the problem of having no closed-form mathematical expressions for the evaluation of performance objectives owing to the existence of jobs sharing and operator revisiting. Extensive experiments are conducted to validate the proposed optimization model. The experimental results demonstrate that the proposed PUDDE-based optimization model can effectively solve the OAPs for the hybrid assembly lines with the consideration of jobs sharing and operator revisiting. It was also found that the proposed PUDDE algorithm evidently outperforms the general differential evolution algorithm. Compared with the collected industrial results, the solution generated by the proposed optimization model has much better performance objectives for the hybrid assembly lines.     

Modified harmony search optimization for constrained design problems

Constrained optimization is a major real-world problem. Constrained optimization problems consist of an objective function subjected to both linear and nonlinear constraints. Here a constraint handling procedure based on the fitness priority-based ranking method (FPBRM) is proposed. It is embedded into a harmony search (HS) algorithm that allows it to satisfy constraints. The HS algorithm is conceptualized using the musical process of searching for a perfect state of harmony. Here, the original heuristic HS was improved by combining both improved and global-best methods along with the FPBRM. The resulting modified harmony search (MHS) was then compared with the original HS technique and other optimization methods for several test problems.     

基于粗糙集理论与差分进化的混合多目标优化算法

针对传统算法求解约束多目标优化所得近似解精度不高、分布性能不好的问题,提出一种基于粗糙集理论与差分进化的混合算法.首先利用多目标差分进化生成一个初始的近似 Pareto 前沿;然后利用粗糙集理论提高Pareto 前沿的分布质量.选取一组标准的多目标约束测试问题,采用混合算法与 NSGA-II 算法进行仿真求解,对比结果表明,所提出的算法在求解约束多目标优化问题时具有更好的近似解分布和更优越的近似解性能.     

混沌鲶鱼粒子群优化和差分进化混合算法

为了改善粒子群优化算法的性能,引入了"鲶鱼效应"思想,改造粒子群个体的进化策略,用混沌方法改良了种群搜索策略,把这两者结合起来,既提高种群的广度搜索能力,又提升深度搜索能力,跟差分进化算法进行混合,算法优势互补,形成一种新型的混合算法,更好地协调广度搜索和深度搜索之间的矛盾,提升算法性能。经过对三个标准函数的测试,仿真结果表明该算法在逃离局部陷阱能力和搜索精度均有显著提高。     

差异演化算法及其改进形式的综述

差异演化算法是一种基于群体差异的演化算法,群体中每个向量代表问题的一个候选解,该算法利用向量之间的差异扰动整个种群,求解问题的最优解.综述了差异演化算法的基本原理、常用测试函数和算法优缺点,讨论了多种改进手段,如:三角变异、混沌理论、逆向计算、均匀设计表等,并给出了未来可能的研究方向.     

一类求解方程全部根的改进差分进化算法

求解高次实复系数代数方程的根,提出了一种改进的差分进化算法,计算种群中每个个体的适应度并排序,利用二分之一规则选取个体,并引入自适应差分变异算子和进化策略重组算子.对5个高次代数方程求根问题进行了数值计算,结果表明,该算法能求解任意次数的实复系数代数方程的全部根,而且求解精度高,收敛速度快,是求解代数方程根的一种有效算法.