基于Kriging模型的并联机器人机构的多目标优化设计

为了提高并联机器人机构的性能特性,对机构参数进行优化设计分析,以达到性能指标最优的目的。先对应用于盾构拼装机的并联机构进行了运动学分析,利用封闭矢量法建立了逆解方程,并在此基础上求解了表征驱动关节输入和动平台输出映射关系的雅可比矩阵。引入衡量并联机构性能的全域运动灵巧度和全域承载能力指标,并以结构参数为设计变量,性能指标为优化目标,基于Krigring近似模型,利用多岛遗传算法对并联机构进行多目标优化,最终得到Pareto解集,并选取了机构参数的最优解。结果表明,基于Kriging模型的多目标优化对机构性能有明显的改善。     

基于高维多目标定向混合进化的可变形机器人优化设计

针对水路两栖模块化可变形机器人工作环境的复杂性和任务多变性,根据其结构和运动特性,提出一种基于高维多目标定向混合进化算法的机器人结构参数优化设计方法,使该型机器人的变形能力和两栖多种运动步态性能达到最优。通过一组方向矢量将搜索空间分解成多个固定寻优方向,并将机器人参数设计的该高维多目标优化问题转化成固定方向上的单目标优化问题;构建混合进化机制加强各方向上最优参数设计方案的搜索能力;以改进的交互式模糊支配和密度估计因子构造精英保留策略,提高设计方案集合的先进性和分布性。试验结果表明,高维多目标定向混合进化算法能够迅速、客观地选择合理的机器人结构参数,可以给设计人员提供更多的选择,为水路两栖可变形机器人的设计提供了一种简单、高效的新方法。     

非叠加型可变形两栖机器人水下推进方法

受自然界中变形虫和生物蛇启发,将可变形和链式构型特性引入两栖机器人。通过运动特性、水环境适应性和可变形能力扩展等综合分析,设计了没有添加额外水下推进机构的非叠加型两栖机器人链式可变形构型。该机器人在陆地环境具有较高机动性外,还具有履带划水和仿生划水组合的复合推进方式。提出基于理想推进器的履带划水推进模型;针对仿生划水时运动学与流体力的耦合关系,提出基于拉格朗日方程的运动学-动力学联合运动模型,并建立了转向模型,完成仿生推进方式的水中机动性能分析。通过仿真分析了履刺高度和分布等机构参数以及幅值、频率等运动参数对水下推进性能的影响。利用基于链式可变形构型研制的机器人样机Amoeba-II进行了水环境试验,验证该构型在水下推进中的有效性,并对履带划水和仿生划水的推进效率、稳定性进行对比。     

水陆两栖蛇形机器人的研制及其陆地和水下步态

针对沼泽、浅滩等复杂环境对蛇形机器人的环境适应需求,在广泛分析国内外水陆两栖蛇形机器人研究最新进展的基础上,研发一种新型水陆两栖蛇形机器人。该机器人由9个具有密封设计的万向运动单元组成,保证了样机在陆地和水中均能灵活运动。基于简化的蛇形曲线得到水陆两栖蛇形机器人的基本二维运动步态即蜿蜒运动。对两个垂直平面上,即水平面和竖直面上基本步态进行复合,由基于启发式思想的三维步态生成方法,得到包括侧向蜿蜒等运动的水陆两栖蛇形机器人的多种陆地步态和水下步态,其中S形翻滚运动和螺旋翻滚运动为蛇形机器人的两种新型步态。通过步态试验验证了水陆两栖蛇形机器人的陆地和水下运动能力。在试验过程中,对陆地和水下步态的性能做出分析,分析结果对水陆两栖蛇形机器人在陆地和水下运动的位置和姿态控制具有重要意义。     

遗传算法在六轮救援机器人越障规划中的应用

针对复杂的非结构地面环境特点,设计了一种机动性能好、越障性能强的六轮救援机器人。并且通过遗传算法使救援机器人在满足越障的几何约束条件下,以保障机器人运动稳定性为目标,求出了最优越障姿态参数。     

圆盘式磁流变制动器磁路多目标优化

为了提高磁流变制动器的制动性能,基于Herscher-Bulkley模型,以磁流变制动器的质量最小、输出制动力矩最大为优化目标,提出了一种基于有限元分析(FEA)和多目标遗传算法(MOGA)的联合优化设计方法。利用该方法获得了磁路结构的Pareto最优解,并采用组合赋权法对Pareto最优解进行选优,得到了制动器最优的磁路结构参数。仿真结果表明:所提出的磁流变制动器磁路多目标优化设计方法是正确有效的,能够获得更加紧凑的磁路结构,并提高磁流变制动器的制动力矩,可作为磁流变制动器设计的参考。     

基于多目标遗传算法的贝塞尔超声变幅杆优化设计方法研究

为设计满足蜂窝复合材料加工要求的高性能超声变幅杆,提出了一种基于多目标遗传算法的超声变幅杆优化设计方法。以变幅杆的结构参数为设计变量,以谐振频率和放大系数为优化设计目标,建立了贝塞尔超声变幅杆的数学优化模型。通过在遗传算法中调用ANSYS仿真软件,对变幅杆进行了建模和动力学分析,获得了计算目标函数所需的参数,采用多目标遗传算法求出了Pareto最优解集,在所求出的Pareto最优解集中选择了一组最符合设计要求的解作为超声变幅杆的设计参数。为验证设计的有效性,对所设计的变幅杆进行了性能测试并对蜂窝复合材料进行了试切实验。实验结果表明：通过该优化设计方法得到的变幅杆放大倍数为7.66,较优化设计前提高了29%,且工作频率更接近于设计频率。通过仿真分析和性能实验,验证了该方法的有效性和可靠性,试切实验结果表明所设计的变幅杆满足加工要求,工艺效果好。     

基于遗传算法的水空两栖机器人可变形翼剪叉机构优化设计

针对水空两栖机器人可变形翼的剪叉机构拟合问题，根据最优化思想，构建剪叉机构的几何模型，提出了一种优化算法以及优化流程框架。应用遗传算法对剪叉机构几何参数进行优化设计，探究剪叉单元数量与拟合误差值之间的关系，分析了优化结果对变形翼的影响，对水空两栖机器人可变形翼剪叉机构的设计有一定帮助。     

基于iSIGHT的异形专用塔机多目标动态优化设计

以异形专用塔机关键模态频率最大、动载系数最小、质量最轻为设计目标,构建i SIGHT与ANSYS多目标联合优化平台。通过在ANSYS中建立参数化有限元模型并进行结构灵敏度分析,确定响应值较大的参数为设计变量。运算过程采用非支配排序遗传算法(NSGA-II),最终得到Pareto最优解集。优化结果反应出了各优化目标之间的影响关系,为设计静动态性能皆优的起重机金属结构提供了参考。     

Pareto多目标遗传算法及其在机械健壮设计中的应用

在机械或结构的优化设计中 ,普遍存在约束的作用 ,且最优解往往位于可行域的边界上。由于外界环境的变化或人为因素造成设计变量扰动 ,可能使设计成为不可行。本文提出了一种的基于设计变量敏感性的健壮性设计方法 ,并提出了一种用 Pareto遗传算法来实施的带约束的多目标优化方法以求解健壮性问题。 Pareto遗传算法可得到 Pareto最优解集 ,从中可选出满足设计需要的解。本文提出的算法包括 5个基本算子 :选择、变异、交叉、小生境技术、Pareto集合过滤器。文中用算例说明该方法的应用     

Evolutionary bi-criteria optimum design of robots based on task specifications

The optimum robot structure design problem based on task specifications is an important one, since it has greater influence on manipulator workspace design, vibrations of the manipulator during operation, manipulator efficiency in the work environment and power consumption. In this paper, an optimization robot structure problem is formulated with the objective of determining the optimal geometric dimensions of the robot manipulators considering the task specifications (pick and place operation). The aim is to minimize torque required for motion and maximize manipulability measure of the robot subject to dynamic, kinematic, deflection and structural constraints with link physical characteristics (length and cross-sectional area parameters) as design variables. In this work, five different cross-sections (hollow circle, hollow square, hollow rectangle, C-channel and I-channel) have been experimented for the link. Three evolutionary optimization algorithms namely multi-objective genetic algorithm (MOGA), elitist nondominated sorting genetic algorithm (NSGA-II) and multi-objective differential evolution (MODE) are used for the optimum structural design of 2-link and 3-link planar robots. Two methods (normalized weighting objective functions and average fitness factor) are used to select the best optimal solution. Two multiobjective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the Pareto optimal fronts. Two more multiobjective performance measures namely optimiser overhead and algorithm effort, are used to find computational effort of optimization algorithm. The results obtained from various techniques are compared and analyzed.     

A new multi-objective ant colony algorithm for solving the disassembly line balancing problem

The disassembly line is the best choice for automated disassembly of disposal products. Therefore, disassembly line should be designed and balanced so that it can work as efficiently as possible. In this paper, a mathematical model for the multi-objective disassembly line balancing problem is formalized firstly. Then, a novel multi-objective ant colony optimization (MOACO) algorithm is proposed for solving this multi-objective optimization problem. Taking into account the problem constraints, a solution construction mechanism based on the method of tasks assignment is utilized in the algorithm. Additionally, niche technology is used to embed in the updating operation to search the Pareto optimal solutions. Moreover, in order to find the Pareto optimal set, the MOACO algorithm uses the concept of Pareto dominance to dynamically filter the obtained non-dominated solution set. To validate the performance of algorithm, the proposed algorithm is measured over published results obtained from single-objective optimization approaches and compared with multi-objective ACO algorithm based on uniform design. The experimental results show that the proposed MOACO is well suited to multi-objective optimization in disassembly line balancing.     

Multi-objective optimization and evaluation method of modular product configuration design scheme

Product configuration is one of the key technologies for mass customization. Traditional product configuration optimization targets are mostly single. In this paper, an approach based on multi-objective genetic optimization algorithm and fuzzy-based select mechanism is proposed to solve the multi-objective configuration optimization problem. Firstly, the multi-objective optimization mathematical model of product configuration is constructed, the objective functions are performance, cost, and time. Then, a method based on improved non-dominated sorting genetic algorithm (NSGA-II) is proposed to solve the configuration design optimization problem. As a result, the Pareto-optimal set is acquired by NSGA-II. Due to the imprecise nature of human decision, a fuzzy-based configuration scheme evaluation and select mechanism is proposed consequently, which helps extract the best compromise solution from the Pareto-optimal set. The proposed multi-objective genetic algorithm is compared with two other established multi-objective optimization algorithms, and the results reveal that the proposed genetic algorithm outperforms the others in terms of product configuration optimization problem. At last, an example of air compressor multi-objective configuration optimization is used to demonstrate the feasibility and validity of the proposed method.     

基于传感器的两栖机器人环境感知系统研究

通过对两栖机器人复杂作业环境及自身设计特点的分析,针对水面、陆地及水陆交叠的不同环境,分别为机器人设计了基于传感器的环境感知系统,并在此基础上,针对陆地环境将模糊控制算法引入两栖机器人的避障研究,设计了模糊控制器.仿真及实验研究的结果表明,该环境感知系统可以在不同的作业环境下及时准确地获取环境信息,同时模糊控制算法应用于陆地环境下的机器人局部路径规划后,取得了较优的无碰撞局部路径.     

Product configuration optimization using a multiobjective genetic algorithm

Product configuration is one of the key technologies in the environment of mass customization. Traditional product configuration technology focuses on constraints-based or knowledge-based application, which makes it very difficult to optimize design of product configuration. In this paper, an approach based on multiobjective genetic algorithm is proposed to solve the problem. Firstly, a configuration-oriented product model is discussed. A multiobjective optimization problem of product configuration according to the model is described and its mathematical formulation is designed. Secondly, a multiobjective genetic algorithm is designed for finding near Pareto or Pareto optimal set for the problem. A matrix method used to check constraint is proposed, and the coding and decoding representation of the solution are designed, then a new genetic evaluation and select mechanism is proposed. Finally, performance comparison of the proposed genetic algorithm with three other genetic algorithms is made. The result shows that the proposed genetic algorithm outperforms the other genetic algorithms in this problem.     

基于多属性决策的气动隐身多目标优化

针对多目标优化结果排序与选择的多属性决策(Multi-attribute decision making,MADM)问题,将多目标优化与MADM相结合,提出基于MADM的多目标优化方法,并将该方法应用于跨声速前掠翼(Forward-swept wing,FSW)气动隐身多目标优化中,优化结果提高了跨声速FSW的气动和隐身性能。采用类别形状函数变换法(Class-shape function transformation,CST)方法对翼型几何外形进行描述,实现FSW气动和隐身多学科优化设计模型的参数化描述。建立基于N-S方程的计算流体力学方法的FSW气动分析模型和基于矩量法的计算电磁学方法的FSW隐身分析模型。将Pareto多目标遗传算法得到的Pareto非劣解集构成MADM矩阵,采用基于模糊熵权的改进的逼近理想解的排序法(Modified technique for order preference by similarity to ideal solution,M-TOPSIS)方案评价方法进行Pareto非劣解排序,最终确定最佳的Pareto非劣解。研究结果验证了所提出方法的有效性,为多目标优化问题提供了一种新的解决途径。     

Optimal disc cutters plane layout design of the full-face rock tunnel boring machine (tbm) based on a multi-objective genetic algorithm

Improving of the quality of the disc cutters’ plane layout design of the full-face rock tunnel boring machine (TBM) is the most effective way to improve the global performance of a TBM. The plane layout design of disc cutters contains multiple complex engineering technical requirements and belongs to a multi-objective optimization problem with multiple nonlinear constraints. Based on analysis of the technical requirements of the plane layout problem, an optimizing mathematical model was built. To obtain a set of design schemes for engineers to choose from, a multi-objective genetic algorithm (MOGA) was applied to carry out the optimization of the mathematical model. A constraint-domination principle was utilized to handle the constraints, and a nondominated sorting method was adopted to obtain Pareto solutions. Simulation results showed that the proposed method was efficient and accurate in obtaining the Pareto layout solutions.     

Multi-objective dynamic optimal trajectory planning of robot manipulators in the presence of obstacles

In this paper, a real-world test problem is presented and made available for the use of evolutionary multi-objective community. The generation of manipulator trajectories by considering multiple objectives and obstacle avoidance is a non-trivial optimisation problem. In this paper two multi-objective evolutionary algorithms viz., elitist non-dominated sorting genetic algorithm (NSGA-II) and multi-objective differential evolution (MODE) algorithm are proposed to address this problem. Multiple criteria are optimised to two simultaneous objectives. Simulations results are presented for industrial robots with two degrees of freedom (Cartesian robot (PP) with two prismatic joints) and six degrees of freedom (PUMA 560 robot), by considering two objectives optimisation. Two methods (normalized weighting objective functions and average fuzzy membership function) are used to select the best optimal solution from Pareto optimal fronts. Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of Pareto optimal fronts. Two more multi-objective performance measures namely optimiser overhead and algorithm effort are used to find computational effort of NSGA-II and MODE algorithms. The Pareto optimal fronts and results obtained from various techniques are compared and analysed.     

Dynamic balancing of planar mechanisms using genetic algorithm

This paper presents an optimization technique to dynamically balance the planar mechanisms in which the shaking forces and shaking moments are minimized using the genetic algorithm (GA). A dynamically equivalent system of point-masses that represents each rigid link of a mechanism is developed to represent link’s inertial properties. The shaking force and shaking moment are then expressed in terms of the point-mass parameters which are taken as the design variables. These design variables are brought into the optimization scheme to reduce the shaking force and shaking moment. This formulates the objective function which optimizes the mass distribution of each link. First, the problem is formulated as a single objective optimization problem for which the genetic algorithm produces better results as compared to the conventional optimization algorithm. The same problem is then formulated as a multi-objective optimization problem and multiple optimal solutions are created as a Pareto front by using the genetic algorithm. The masses and inertias of the optimized links are computed from the optimized design variables. The effectiveness of the proposed methodology is shown by applying it to a standard problem of four-bar planar mechanism available in the literature.     

基于多目标遗传算法的直升机总体参数优化设计

应用多目标优化问题中Pareto最优解集的概念,提出了一种基于多目标遗传算法的直升机总体参数优化设计方法。算法引入了个体的序和密度的概念,改进了变异操作算子,使用精英策略,确保能够搜索到具有较高贴近性、均匀性和完整性的Pareto解集。以UH-1H直升机为优化算例的计算结果表明:多目标遗传算法适用于解决多目标优化问题,能够改善Pareto解的质量和均匀性分布。