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.     

Concurrent multi-objective tolerance allocation of mechanical assemblies considering alternative manufacturing process selection

Concurrent design of tolerances by considering both the manufacturing cost and quality loss of each component by alternate processes of the assemblies may ensure the manufacturability, reduce the manufacturing costs, decrease the number of fraction nonconforming (or defective rate), and shorten the production lead time. Most of the current tolerance design research does not consider the quality loss. In this paper, a novel multi-objective optimization method is proposed to enhance the operations of the non-traditional algorithms (Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO)) and systematically distribute the tolerances among various the components of mechanical assemblies. The problem has a multi-criterion character in which three objective functions, one constraint, and three variables are considered. The average fitness factor method and normalized weighted objective function method 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 optimizer overhead and algorithm effort are used to find the computational effort of NSGA-II and MOPSO algorithms. The Pareto-optimal fronts and results obtained from various techniques are compared and analysed. Both NSGA-II and MOPSO algorithms are best for this problem.     

Multi-objective optimization of drive gears for power split device using surrogate models

Power split device (PSD) is a key component in the energy coupling and decoupling of parallel-series hybrid electric vehicle. This paper proposes a multi-objective optimization method to achieve optimal balance solution among the volume, contact stress, and frictional energy dissipation of PSD drive gears, some of which are implicit with respect to design variables. To avoid the time-consuming problem of finite element analysis used to solve nonlinear responses, surrogate models are adopted to generate approximate expressions of design variables. Pareto-optimal solutions of PSD are obtained using multi-island genetic algorithm (MIGA), non-dominated sorting GA-II (NSGA-II), and multi-objective particle swarm optimization algorithm. The performances of PSD before and after optimization are compared. Results indicate that the proposed method is effective, and NSGA-II achieves higher optimizing efficiency in solving the multiobjective optimization problem of PSD than the other algorithms.     

行星齿轮减速器多目标优化设计

针对行星齿轮减速器优化设计的多参数、多目标、多约束的特点,建立了多目标优化数学模型,应用层次分析法计算各目标函数权重,通过采用遗传算法进行减速器参数多目标优化求解.实例计算表明.优化设计参数比原设计参数更加合理,说明遗传算法用于减速器优化设计是有效、可行的,对行星齿轮减速器的优化有一定的指导意义.     

基于多目标遗传算法的水陆两栖可变形机器人结构参数设计方法

水陆两栖可变形机器人是一种兼具变形能力与两栖环境适应能力的新型移动机器人。在其机构设计中,结构参数直接影响该机器人在任务环境中的各项机动性能。针对水陆两栖可变形机器人工作环境复杂性和任务多变性,提出一种基于多目标遗传算法的机器人结构参数设计方法,以得到该型机器人在两栖环境中的最优的综合性能。在水陆两栖可变形机器人陆地环境和水环境中运动学和动力学模型基础上,建立两栖环境中机器人的机动性能指标函数与结构参数的映射关系,并在此基础之上构建面向水陆两栖可变形机器人的结构参数设计的多目标优化问题。利用多目标遗传算法得到该多目标机构参数设计问题的Pareto最优解集,并且通过组合赋权方法确定各目标决策属性的权重,从Pareto最优解集中得到符合设计要求的水陆两栖可变形机器人的各项机构参数最优解,进而指导机器人最终结构参数设计。根据最终得到的结构参数研制出水陆两栖可变形机器人样机Amoeba-II,并在两栖环境下进行样机的各项性能试验,最终验证了基于多目标遗传算法的机器人结构参数设计方法的有效性以及在机器人设计中的适用性。     

基于黑板模型的排球机器人动作规划

基于黑板模型设计了排球机器人动作规划框架,并将动作规划中的各个子任务定义为智能体。由黑板激活相应的智能体进行子任务求解,从而完成排球机器人的击球动作规划问题。此外,考虑到排球任务的特点,以机械臂的可操作度作为评价函数,选取最优的机械臂构形。最后,采用MatlabSimulink对平面3连杆机械臂的击球问题进行了仿真研究,得到满足可操作度指标下的最优击球动作。该仿真的实现过程体现了基于黑板模型及多智能体技术的思想。     

Design of PID-type controllers using multiobjective genetic algorithms

The design of a PID controller is a multiobjective problem. A plant and a set of specifications to be satisfied are given. The designer has to adjust the parameters of the PID controller such that the feedback interconnection of the plant and the controller satisfies the specifications. These specifications are usually competitive and any acceptable solution requires a tradeoff among them. An approach for adjusting the parameters of a PID controller based on multiobjective optimization and genetic algorithms is presented in this paper. The MRCD (multiobjective robust control design) genetic algorithm has been employed. The approach can be easily generalized to design multivariable coupled and decentralized PID loops and has been successfully validated for a large number of experimental cases.     

基于GA-PSO的地砖铺设机器人作业点优化

针对建筑移动机器人路径规划中移动小车作业点问题,提出了以最大可操作度为优化指标,基于遗传粒子群混合算法搜寻移动小车作业点的方法.阐述了地砖铺设机器人系统的组成及灵巧度优化指标;给出了移动机械臂铺砖的一般步骤;结合遗传算法和粒子群算法的优点,以机械臂可操作度最大为原则进行优化,以快速准确地得到最优作业位姿点.利用MATL...     

Multi-objective Optimization of a Parallel Ankle Rehabilitation Robot Using Modified Differential Evolution Algorithm

Dimensional synthesis is one of the most difficult issues in the field of parallel robots with actuation redundancy. To deal with the optimal design of a redundantly actuated parallel robot used for an...     

Optimal design of a 3-leg 6-DOF parallel manipulator for a specific workspace

Researchers seldom study optimum design of a six-degree-of-freedom(DOF) parallel manipulator with three legs based upon the given workspace. An optimal design method of a novel three-leg six-DOF parallel manipulator(TLPM) is presented. The mechanical structure of this robot is introduced, with this structure the kinematic constrain equations is decoupled. Analytical solutions of the forward kinematics are worked out, one configuration of this robot, including position and orientation of the end-effector are graphically displayed. Then, on the basis of several extreme positions of the kinematic performances, the task workspace is given. An algorithm of optimal designing is introduced to find the smallest dimensional parameters of the proposed robot. Examples illustrate the design results, and a design stability index is introduced, which ensures that the robot remains a safe distance from the boundary of sits actual workspace. Finally, one prototype of the robot is developed based on this method. This method can easily find appropriate kinematic parameters that can size a robot having the smallest workspace enclosing a predefined task workspace. It improves the design efficiency, ensures that the robot has a small mechanical size possesses a large given workspace volume, and meets the lightweight design requirements.     

高重合度圆柱齿轮传动多目标优化设计

以重合度最大、体积最小、弯曲强度相等为目标函数,建立了圆柱齿轮传动多目标优化设计数学模型,采用带精英策略的快速非支配排序遗传算法(NSGA-Ⅱ)进行优化求解。对高速重载斜齿圆柱齿轮传动进行了高重合度优化设计,得到了Parteto最优解,并从中选择了一个优化方案与原始方案进行对比,结果显示高重合度圆柱齿轮传动的强度有明显提高,体积也有一定的减小。     

角度与运动约束下串联式机械臂工作空间求解与结构优化

深水作业机械臂通常采用串联式结构,机械臂每个关节的转动角度与长度会受到相应的限制,这些参数会直接影响到机械臂的运动轨迹规划和作业效率.机械臂的有效工作空间的求解是一个多目标多约束的优化问题.通过数学分析建立机械臂的运动学模型,分析影响有效工作空间的相关参数,利用图解法来分析机械臂工作空间的边界曲线,得到机械臂的有效工作...     

基于粒子群算法的永磁电机优化设计

将粒子群算法应用到永磁电机的多目标优化设计。利用Matlab脚本语言跨平台调用电磁场分析软件Ansoft Maxwell,建立永磁电机设计的参数化建模方式,实现多目标优化设计。实验表明脚本化建模及多目标优化的可行性,能直接应用到电机的设计制造中。     

Performance enhancement of axial fan blade through multi-objective optimization techniques

This paper presents an axial fan blade design optimization method incorporating a hybrid multi-objective evolutionary algorithm (hybrid MOEA). In flow analyses, Reynolds-averaged Navier-Stokes (RANS) equations were solved using the shear stress transport turbulence model. The numerical results for the axial and tangential velocities were validated by comparing them with experimental data. Six design variables relating to the blade lean angle and the blade profile were selected through Latin hypercube sampling of design of experiments (DOE) to generate design points within the selected design space. Two objective functions, namely, total efficiency and torque, were employed, and multi-objective optimization was carried out, to enhance the performance. A surrogate model, Response Surface Approximation (RSA), was constructed for each objective function based on the numerical solutions obtained at the specified design points. The Non-dominated Sorting of Genetic Algorithm (NSGA-II) with local search was used for multi-objective optimization. The Pareto-optimal solutions were obtained, and a trade-off analysis was performed between the two conflicting objectives in view of the design and flow constraints. It was observed that, by the process of multi-objective optimization, the total efficiency was enhanced and the torque reduced. The mechanisms of these performance improvements were elucidated by analysis of the Pareto-optimal solutions.     

Multi-objective optimization of an axial compressor blade

Numerical optimization with multiple objectives is carried out for design of an axial compressor blade. Two conflicting objectives, total pressure ratio and adiabatic efficiency, are optimized with three design variables concerning sweep, lean and skew of blade stacking line. Single objective optimizations have been also performed. At the data points generated by D-optimal design, the objectives are calculated by three-dimensional Reynolds-averaged Navier-Stokes analysis. A second-order polynomial based response surface model is generated, and the optimal point is searched by sequential quadratic programming method for single objective optimization. Elitist non-dominated sorting of genetic algorithm (NSGA-II) with ε-constraint local search strategy is used for multi-objective optimization. Both objective function values are found to be improved as compared to the reference one by multi-objective optimization. The flow analysis results show the mechanism for the improvement of blade performance.     

NEW MULTIOBJECTIVE SIMULATED ANNEALING ALGORITHM AND ITS APPLICATION TO TURBINE CASCADES’ DESIGN

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柔性关节机械臂轨迹规划及振动抑制研究

针对工业机器人多自由度复杂机械臂系统,对其建立多刚体运动学模型,仿真验证末端运动轨迹的真确性。在此基础上,对机械臂系统的末端关节进行柔性化处理,添加随机柔性扰动,得到刚柔耦合机械臂较为真实的末端轨迹曲线。提出了基于混沌粒子群优化算法(CPSO)的振动抑制方案,通过CPSO算法对机械臂末端轨迹的插值参数进行优化,定义了柔性末端的振动变形最小的目标函数,并给出了具体的求解步骤。数值仿真结果表明,在满足系统约束条件的情况下,机械臂运行平稳,不存在角速度突变的情况,相比于基本粒子群优化算法,CPSO算法保证了粒子群体的随机性,提高了群体的多样性,且收敛速度较快,不会陷入局部最优,在CPSO优化下的柔性末端轨迹振动明显减小,从而说明CPSO算法能够有效优化轨迹规划参数,减小机械臂柔性末端的振动变形。     

某火炮协调器多学科一体化设计优化

为了解决传统火炮优化设计中单学科建模分析的不足,采用多学科一体化设计优化方法对某火炮协调器系统进行了研究。通过对电气控制、液压、机械三个学科及学科间耦合关系的分析,建立了协调器结构控制一体化分析模型,结合多目标进化算法,对建立的一体化分析模型进行了设计优化研究。与原设计相比,优化后的设计使系统拥有更优的综合性能。     

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.     

A parallel computing application of the genetic algorithm for lubrication optimization

This study investigated the performance of parallel optimization by means of a genetic algorithm (GA) for lubrication analysis. An air-bearing design was used as the illustrated example and the parallel computation was conducted in a single system image (SSI) cluster, a system of loosely network-connected desktop computers. The main advantages of using GAs as optimization tools are for multi-objective optimization, and high probability of achieving global optimum in a complex problem. To prevent a premature convergence in the early stage of evolution for multi-objective optimization, the Pareto optimality was used as an effective criterion in offspring selections. Since the execution of the genetic algorithm (GA) in search of optimum is population-based, the computations can be performed in parallel. In the cases of uneven computational loads a simple dynamic load-balancing scheme is proposed for optimizing the parallel efficiency. It is demonstrated that the huge amount of computing demand of the GA for complex multi-objective optimization problems can be effectively dealt with by parallel computing in an SSI cluster.