Kinematic analysis of flexible bipedal robotic systems

In spite of its intrinsic complexities,the passive gait of bipedal robots on a sloping ramp is a subject of interest for numerous researchers.What distinguishes the present research from similar works is the consideration of flexibility in the constituent links of this type of robotic systems.This is not a far-fetched assumption because in the transient(impact)phase,due to the impulsive forces which are applied to the system,the likelihood of exciting the vibration modes increases considerably.Moreover,the human leg bones that are involved in walking are supported by viscoelastic muscles and ligaments.Therefore,for achieving more exact results,it is essential to model the robot links with viscoelastic properties.To this end,the Gibbs-Appell formulation and Newton's kinematic impact law are used to derive the most general form of the system's dynamic equations in the swing and transient phases of motion.The most important issue in the passive walking motion of bipedal robots is the determination of the initial robot configuration with which the system could accomplish a periodic and stable gait solely under the effect of gravitational force.The extremely unstable nature of the system studied in this paper and the vibrations caused by the impulsive forces induced by the impact of robot feet with the inclined surface are some of the very serious challenges encountered for achieving the above-mentioned goal.To overcome such challenges,an innovative method that uses a combination of the linearized equations of motion in the swing phase and the algebraic motion equations in the transition phase is presented in this paper to obtain an eigenvalue problem.By solving this problem,the suitable initial conditions that are necessary for the passive gait of this bipedal robot on a sloping surface are determined.The effects of the characteristic parameters of elastic links including the modulus of elasticity and the Kelvin-Voigt coefficient on the walking stability of this type of robotic systems are also studied.The findings of this parametric study reveal that the increase in the Kelvin-Voigt coefficient enhances the stability of the robotic system,while the increase in the modulus of elasticity has an opposite effect.

Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

FLEXIBLE JOB-SHOP SCHEDULING WITH FUZZY GOAL THROUGH IOCDGA

The fuzzy goal flexible job-shop scheduling problem （FGFJSP） is the extension of FJSP. Compared with the convention JSP, it can solve the fuzzy goal problem and meet suit requirements of the key job. The multi-object problem, such as the fuzzy cost, the fuzzy due-date, and the fuzzy makespan, etc, can be solved by FGFJSP. To optimize FGFJSP, an individual optimization and colony diversity genetic algorithm （IOCDGA） is presented to accelerate the convergence speed and to avoid the earliness. In IOCDGA, the colony average distance and the colony entropy are defined after the definition of the encoding model. The colony diversity is expressed by the colony average distance and the colony entropy. The crossover probability and the mutation probability are controlled by the colony diversity. The evolution emphasizes that sigle individual or a few individuals evolve into the best in IOCDGA, but not the all in classical GA. Computational results show that the algorithm is applicable and the number of iterations is less.

INTEGRATED OPERATOR GENETIC ALGORITHM FOR SOLVING MULTI-OBJECTIVE FLEXIBLE JOB-SHOP SCHEDULING

In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objective FJSP, the Grantt graph oriented string representation （GOSR） and the basic manipulation of the genetic algorithm operator are presented. An integrated operator genetic algorithm （IOGA） and its process are described. Comparison between computational results and the latest research shows that the proposed algorithm is effective in reducing the total workload of all machines, the makespan and the critical machine workload.

A Modi ed Iterated Greedy Algorithm for Flexible Job Shop Scheduling Problem

The flexible job shop scheduling problem(FJSP) is considered as an important problem in the modern manufacturing system. It is known to be an NP-hard problem. Most of the algorithms used in solving FJSP problem are categorized as metaheuristic methods. Some of these methods normally consume more CPU time and some other methods are more complicated which make them di cult to code and not easy to reproduce. This paper proposes a modified iterated greedy(IG) algorithm to deal with FJSP problem in order to provide a simpler metaheuristic, which is easier to code and to reproduce than some other much more complex methods. This is done by separating the classical IG into two phases. Each phase is used to solve a sub-problem of the FJSP: sequencing and routing sub-problems. A set of dispatching rules are employed in the proposed algorithm for the sequencing and machine selection in the construction phase of the solution. To evaluate the performance of proposed algorithm, some experiments including some famous FJSP benchmarks have been conducted. By compared with other algorithms, the experimental results show that the presented algorithm is competitive and able to find global optimum for most instances. The simplicity of the proposed IG provides an e ective method that is also easy to apply and consumes less CPU time in solving the FJSP problem.

Solving flexible job shop scheduling problem by a multi-swarm collaborative genetic algorithm

The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborative optimization algorithm is proposed for the FJSP.Multi-population structure is used to independently evolve two sub-problems of the FJSP in the MSCGA.Good operators are adopted and designed to ensure this algorithm to achieve a good performance.Some famous FJSP benchmarks are chosen to evaluate the effectiveness of the MSCGA.The adaptability and superiority of the proposed method are demonstrated by comparing with other reported algorithms.

Simultaneous scheduling of machines and automated guided vehicles in flexible manufacturing systems using genetic algorithms

The problem of simultaneous scheduling of machines and vehicles in flexible manufacturing system (FMS) was addressed.A spreadsheet based genetic algorithm (GA) approach was presented to solve the problem.A domain independent general purpose GA was used,which was an add-in to the spreadsheet software.An adaptation of the propritary GA software was demonstrated to the problem of minimizing the total completion time or makespan for simultaneous scheduling of machines and vehicles in flexible manufacturing systems.Computational results are presented for a benchmark with 82 test problems,which have been constructed by other researchers.The achieved results are comparable to the previous approaches.The proposed approach can be also applied to other problems or objective functions without changing the GA routine or the spreadsheet model.

APPLYING PARTICLE SWARM OPTIMIZATION TO JOB-SHOPSCHEDULING PROBLEM

A new heuristic algorithm is proposed for the problem of finding the minimummakespan in the job-shop scheduling problem. The new algorithm is based on the principles ofparticle swarm optimization (PSO). PSO employs a collaborative population-based search, which isinspired by the social behavior of bird flocking. It combines local search (by self experience) andglobal search (by neighboring experience), possessing high search efficiency. Simulated annealing(SA) employs certain probability to avoid becoming trapped in a local optimum and the search processcan be controlled by the cooling schedule. By reasonably combining these two different searchalgorithms, a general, fast and easily implemented hybrid optimization algorithm, named HPSO, isdeveloped. The effectiveness and efficiency of the proposed PSO-based algorithm are demonstrated byapplying it to some benchmark job-shop scheduling problems and comparing results with otheralgorithms in literature. Comparing results indicate that PSO-based algorithm is a viable andeffective approach for the job-shop scheduling problem.

Solving Job-Shop Scheduling Problem Based on Improved Adaptive Particle Swarm Optimization Algorithm

An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.

Finite Element Method Applied to the Eigenvalue Analysis of Flexible Rotors Supported by Journal Bearings

This work deals with a finite element procedure developed to perform the eigenvalue analysis of damped gyroscopic systems, represented by flexible rotors supported on fluid film journal bearings. The rotor finite element model is based on the Timoshenko beam theory, accounting for the shaft rotary inertia and gyroscopic moments. The governing equations for the hydrodynamic journal bearing are obtained through the Galerkin weighted residual method applied to the classical Reynolds equation. A perturbation scheme on the fluid film governing equation permits to obtain the zero-th and first order lubrication equations for the bearings, which allow the computation of the dynamic force coefficients associated with the bearing stiffness and damping. The rotor-bearing system equation, which consists of a case of damped gyroscopic equation, is rewritten on state form to compute the complex eigenvalues. The natural frequencies at several operating conditions are obtained and compared to the technical literature data. The influence of the effective damping on the eigenvalue real part sign is analyzed for some examples of rotor-bearing systems, showing how the stability conditions can be predicted by the eigenvalue analysis. The procedure implemented in this work can provide useful guidelines and technical data about the selection of the more appropriate set of bearing parameters for rotating machines operating at stringent conditions.

A Grafted Genetic Algorithm for the Job-Shop Scheduling Problem

The standard genetic algorithm has limitations of a low convergence rate and premature convergence in solving the job-shop scheduling problem.To overcome these limitations,this paper presents a new improved hybrid genetic algorithm on the basis of the idea of graft in botany.Through the introduction of a grafted population and crossover probability matrix,this algorithm accelerates the convergence rate greatly and also increases the ability to fight premature convergence.Finally,the approach is tested on a set of standard instances taken from the literature and compared with other approaches.The computation results validate the effectiveness of the proposed algorithm.

DYNAMICS OF A ROTATING FLEXIBLE MANIPULATOR WITH TIP LOAD

The dynamics of a flexible manipulator is investigated in this paper. From the point of view of dynamic balance, the motion equations of a rotating beam with tip load are established by us ing Hamilton' s principle. By taking into account the effects of dynamic stiffening and dynamic softening, the stability of the system is proved by employing Lyapunov' s approach. Furthermore, the method of power series is proposed to find the exact solution of the eigenvalue problem The effects of rotating speed and tip load on the vibration behavior of the flexible manipulator are shown in numerical results.

考虑模糊质检时间的柔性作业车间动态调度问题

为解决更符合现实情形的模糊质检时间柔性作业车间动态调度问题,以最小化完工时间为目标,立足紧急插单、机器在空载运行时发生故障和机器在加工工件时发生故障的三种故障情形,建立了带模糊质检时间的机器故障、紧急插单重调度模型。设计了基于元胞自动机邻域搜索和随机重启爬坡算法的改进遗传算法求解模型,即针对车间调度问题中存在的订单排序和机器选择双决策问题特征,设计包含工序码和机器码的双层编码方案,并基于遗传算法思想对工序码和机器码设计相应的交叉、变异等遗传操作。同时,将遗传操作应用于基于元胞自动机的邻域搜索算法框架中以增强算法全局搜索能力,整合基于关键工序的随机重启爬坡算法以提高算法局部开发能力。实验选取10个柔性车间调度算例验证了所提算法的有效性,同时,测试1个模糊质检时间柔性车间调度算例验证了模型的有效性。另外,实验也测试了不同故障场景,得出该动态调度方法优于实际场景中常使用的“工件后移”调度策略。

混合遗传变邻域搜索算法求解柔性车间调度问题

针对考虑生产成本的柔性作业车间调度问题(flow job shop scheduling problem, FJSP),以完工时间与加工成本为优化指标,提出一种求解FJSP的混合遗传变邻域搜索算法。根据个体适应度对种群分割,结合自适应交叉概率改进子代种群产生方式;设计两种邻域结构增强算法的局部搜索能力;提出一种基于动态交叉变异概率的优化算法流程提高求解效率。运用提出的算法求解基准实例与实际问题测试,验证了算法的有效性。

海上可移动式风机装备研制过程中的关键技术问题

可移动式风机是一种既能利用海上风资源进行发电储能,又具备一定自航能力的海上能源装备。与固定式海上风机以及系泊式漂浮风机相比,可移动式风机的有效作业水深更大,可以开发深远海蕴含的大部分风能,同时还可以为远海生产生活设施进行供电。本文基于国内外相关最新研究资料,结合作者自身的理解和探索,从基础选型、概念可行性论证、一体化动力分析技术、作业决策技术、安全性评估、水池模型试验技术、建造及安装技术等多个方面,对研制可移动式风机过程中需要解决的关键技术问题进行探讨。

可弯曲支气管镜在儿童心血管疾病相关气道问题中的应用

心血管疾病(CVD)患儿常伴随呼吸道症状,其原因多样,主要分为CVD对气道的外部压迫和CVD伴发气道发育畸形。可弯曲支气管镜在诊断和治疗CVD相关气道问题方面发挥越来越重要的作用,尤其在围手术期管理和伴发气道狭窄的介入治疗中。随着小儿心胸外科技术和儿童呼吸内镜介入治疗技术的快速发展,可弯曲支气管镜将在改善CVD患儿预后方面发挥更大的作用。本文探讨可弯曲支气管镜在儿童CVD相关气道问题中的应用价值,包括诊断气管-支气管树异常、清除黏液栓、止血、介入治疗气道狭窄,以及评估和管理心胸外科围手术期气道问题等方面。

基于改进灰狼算法的柔性作业车间动态节能分批调度问题

针对柔性作业车间动态节能分批调度问题,建立了以优化车间生产成本、机床能耗成本和动静态调度方案差异成本为目标的数学模型,接着针对实际生产过程中的动态扰动设计了动态调度方案,针对动态扰动导致工件工艺路线变化的问题设计了重组批技术,针对动态调度方案的求解则设计了改进灰狼算法,采用层级位置更新方法进行狼群的位置更新,提高算法的搜索效率,最后通过标准算例库验证了算法求解柔性作业车间动态节能分批调度问题的有效性与优越性。

基于粒子群优化算法的柔性生产线排产方法

大力推进信息化和工业化的融合,要在航空航天装备上实现重点领域的技术突破。在未来十年,我国对飞机的需求将会持续加大,这就要求飞机零件的制造效率要高、响应速度要快。但实际生产中,对生产线排产算法的研究与应用还比较少,这就导致实际生产不能发挥出生产线的最大效能。针对上述问题,对实际柔性生产线进行了研究,建立符合现场生产实际的排产调度模型,采用优化的粒子群算法对模型进行求解,得到满足实际排产要求的最优解,使其发挥最大生产效能。

父母养育心理灵活性与幼儿问题行为的关系--基于心理安全感和教养效能感的链式中介作用

为了探究父母养育心理灵活性与幼儿问题行为的关系,以及幼儿心理安全感和父母教养效能感在其中的作用,采用父母养育心理灵活性量表、学前儿童困难与长处问卷、幼儿心理安全感量表和幼儿父母教养效能感量表对重庆市355名幼儿进行问卷调查。结果显示,幼儿问题行为在父母学历和家庭结构中差异显著,幼儿心理安全感在家庭结构中差异显著和父母教养效能感在填表人上差异显著;父母养育心理灵活性、幼儿问题行为、幼儿心理安全感和父母教养效能感两两显著相关;父母养育心理灵活性能显著负向预测幼儿问题行为;幼儿心理安全感、父母教养效能感在父母养育心理灵活性对幼儿问题行为的影响中起链式中介作用。具体而言,父母养育心理灵活性通过三条路径影响幼儿问题行为、幼儿心理安全感和父母教养效能感:幼儿心理安全感的中介作用、父母教养效能感的中介作用以及两者的链式中介作用。因此,提高父母养育心理灵活性、激发父母教养效能感和重视幼儿心理安全感发展可以减少幼儿问题行为的产生。

改进萤火虫算法求解多目标柔性作业车间调度

针对多目标柔性作业车间调度问题,以最小化最大完工时间、最小化机器总负荷、最小化机器最大负荷为目标,提出一种改进邻域结构的离散萤火虫算法。首先,采用多种策略相结合的方式初始化种群,提高算法初始解质量以及种群多样性;其次,通过改进关键路径的邻域结构并设计离散萤火虫算法的位置更新公式,以增强算法全局搜索和局部搜索能力;最后,将该算法应用于标准数据集,并将求解结果与其他算法进行对比,验证了所提算法的有效性。