基于MATLAB的机械臂运动学分析和轨迹规划

目的 研究包装机械臂运动学和轨迹规划,以实现机器臂的平稳运行和精确控制。方法 以六自由度包装机械臂模型为研究对象,采用改进的D–H参数法建立坐标系,推导运动学方程,运用MATLAB搭建机械臂模型并进行正逆运动学仿真,以验证模型的正确性。其次,采用五次多项式插值算法对机械臂关节空间进行轨迹规划。最后,针对包装作业中待包装产品的定点抓取搬运任务进行轨迹规划和仿真。结果 搭建的机械臂模型正确,五次多项式插值法轨迹优化效果好,可保证各关节角度、速度和加速度曲线光滑连续;计算出抓取搬运任务中各关节的关节量,并得到了末端执行器运动轨迹曲线。结论 机械臂能以预期的运动精度完成抓取搬运的动作,为进一步探究机械臂的运动控制和实际应用奠定了基础。     

镍阳极板铜耳线搬运机械臂轨迹规划仿真研究

镍阳极板铜耳线的搬运依靠的是一种六自由度机械臂来完成,而六自由度机械臂在完成镍阳极板铜耳线搬运和插装的过程中存在着运动轨迹偏差大和不平稳的问题,本文将以六自由度机械臂为研究对象,运用D-H法建立运动学模型,参照此应用环境下的要求确定机械臂的约束要求,对机械臂的运动过程进行分析.采用五次多项式插值法对六自由度机械臂的运动...     

网络远程控制中机械臂的连续轨迹控制

探讨了点位控制在二自由度机械臂网络远程控制中的问题,将连续轨迹控制应用到基于关节坐标空间的机器人控制系统中,分析了在远程操控环境中实现连续轨迹控制所需要满足的条件,给出了最佳合成速度的求法,并进行了实际系统的远程控制实验,在期望时间内实现了连续、平滑的运动效果,证实了在网络远程系统的控制中采用连续轨迹控制能够获得更高的精度。     

基于凸规划的机械臂轨迹规划方法

研究了快速求解具有时间约束的机械臂轨迹规划问题,提出了一种基于凸规划的轨迹规划方法。该方法针对机械臂轨迹规划中动力学约束非线性强、时间约束不易处理的问题,首先通过变量替换,将非线性约束转化为线性约束,然后添加新的约束,将原始非凸优化问题转化为凸规划问题,在此基础上,将其写作二阶锥规划（SOCP）形式,使用SeDuMi等优化工具包近似实时求解。该方法具有以下优点：计算高效,凸规划问题能够在多项式时间内得到求解;算法全局稳定,能收敛到全局最优解,不需要提供优化初值;可扩展性强,工业机器人的多种约束以及性能指标如加速度平滑约束、功率等均可扩充。仿真实验表明,与现有方法相比,该方法能够有效提高轨迹规划的效率,机器人的轨迹规划可以近似实时求解。     

机械基础结构多目标拓扑优化设计方法

机械的基础结构在保证具有足够的刚度、强度和稳定性的条件下,经济性也必须要好,因此机械基础结构常采用内部布置有加筋板的箱体结构。以某机械基础结构为例,分别用基于经验设计的内部筋板布置方法和多目标拓扑优化方法进行优化设计,得到了2 种设计方案;比较了2 种方案的动静态力学性能。结果表明,多目标拓扑优化设计的基础结构比一般经验设计的结构,刚度有所提高,而结构质量减小11. 21%,一阶固有频率提高25. 07%。     

基于自适应神经网络的机械臂滑模轨迹跟踪控制

针对动态建模误差和不确定性扰动对机械臂末端高精度轨迹跟踪控制的不利影响,提出了一种新型的基于自适应神经网络的机械臂滑模控制策略。该控制策略可分为三部分：自适应神经网络补偿项、切换控制项和等效控制项。自适应神经网络的引入,避免了建模误差和外界未知扰动对机械臂系统的影响,提高了轨迹跟踪精度;切换控制项可使机械臂系统性能在迅速趋近滑模面的同时以很小的速率趋近平衡点,既能保证系统稳定,又能避免系统过于抖振;等效控制项用于对机械臂动力学模型的重力项和哥氏力项进行补偿,实现对模型的线性化处理,保证了系统的控制精度。最后,通过构造Lyapunov函数验证了所设计控制系统的稳定性,并在MATLAB/Simulink环境下和机器人系统工具箱中开展仿真实验和对比实验。结果表明,所提出的控制算法能够在保持机械臂稳定性的同时实现高精度的轨迹跟踪,验证了该控制算法的有效性和优越性。自适应神经网络滑模控制算法可为提高机械臂末端轨迹跟踪精度提供一种解决方案。     

柔性机械臂的轨迹跟踪与振动模糊控制

由于柔性机械臂的动态模型较为复杂,传统的基于模型的控制策略一般难以处理.基于PD型模糊控制器,提出一种基于新型的模糊控制的柔性机械臂轨迹跟踪与振动控制策略.首先推导用于柔性机械臂角控制的PD型并联模糊控制器,随后将其改进,提出了一种非并联型模糊控制器,并结合输入整形策略,推导了一种新型的柔性机械臂的混合模糊控制器.在此基础上,构建了柔性机械臂实验平台,开展实时控制实验,给出了相关的时域和频域控制效果.相关的实验结果表明,所提出的新型模糊控制器能够较好地完成柔性机械臂的轨迹跟踪任务,并显著降低柔性机械臂的振动.     

碳纤维复合材料控制臂多尺度多目标优化

为实现悬架控制臂轻量化设计,采用碳纤维复合材料(CFRP)对钢制控制臂进行材料替换,并利用多层次优化方法对CFRP控制臂进行铺层优化。基于多尺度分析方法,考虑碳纤维复合材料细观结构参数及铺层角度对控制臂宏观性能的影响,选取细观尺度下纤维束截面高度、宽度、纱线曲折度、单位长度经纬纱数量及四个经典铺层角度作为设计变量,以控制臂的质量最小、一阶模态频率最高为优化目标,以刚度和强度为约束条件,结合响应面近似模型和NSGA-Ⅱ优化算法对碳纤维复合材料控制臂进行多尺度多目标优化,得到Pareto解集。优化结果表明,在刚度满足使用性能要求下,CFRP控制臂的质量较钢制控制臂下降了65%,且强度、振动特性等性能得到大幅提升,轻量化效果显著,可为碳纤维复合材料结构件的轻量化设计提供参考。     

刚柔性耦合机械臂轨迹跟踪与振动抑制

讨论刚柔性耦合机械臂轨迹跟踪控制和振动抑制问题.以双连杆刚柔性机械臂为例,建立了双连杆刚柔性机械臂的非线性动力学方程,运用基于模型的非线性解耦反馈控制方法,使方程中关节角变量和弹性变量部分解耦.利用机械臂逆动力学方法和线性二次型(LQ)最优控制方法讨论刚柔性耦合机械臂的轨迹跟踪控制问题和消除残余振动的控制问题.通过数值仿真计算,表明文中方法的有效性.     

一种工业机器人多目标轨迹优化算法

为解决工业机器人工作效率低、能耗损失严重和关节冲击磨损较大的问题,提出了一种基于布谷鸟搜索（cuckoo search,CS）算法和非支配排序遗传算法-Ⅱ(non-dominated sorting genetic algorithm-Ⅱ,NSGA-Ⅱ)的混合算法（简称为CSNSGA-Ⅱ）,用于机器人的轨迹优化。采用5次非均匀有理B样条（non-uniform rational B-splines,NURBS）曲线作为工业机器人的轨迹规划曲线,同时以运动时间、能耗和冲击磨损为优化目标构建相应的多目标轨迹优化模型,并在速度、加速度和加加速度的约束下采用CSNSGA-Ⅱ进行轨迹优化。CSNSGA-Ⅱ以Tent混沌映射初始化时间序列,采用不可行度算法将解分为可行解与不可行解,并利用改进的CS算法对不可行解进行处理。利用MATLAB软件对6R勃朗特机器人进行建模仿真,并对得到的非支配解集和归一化加权迭代最优值进行对比分析。仿真结果表明,相比于NSGA-Ⅱ、多目标粒子群优化（multi-objective particle swarm optimization,MOPSO）算法,所提出的CSNS...     

An improved version of the multiple trajectory search for real value multi-objective optimization problems

Multi-objective optimization is widely used in science, engineering and business. In this article, an improved version of the multiple trajectory search (MTS) called MTS2 is presented and successfully applied to real-value multi-objective optimization problems. In the first step, MTS2 generates M initial solutions distributed over the solution space. These solutions are called seeds. Some seeds with good objective values are selected as foreground seeds. Then, MTS2 chooses a suitable region search method for each foreground seed according to the landscape of the neighbourhood of the seed. During the search, MTS2 focuses its search on some promising areas specified by the foreground seeds. The performance of MTS2 was examined by applying it to solve the benchmark problems provided by the Competition of Performance Assessment of Constrained/Bound Constrained Multi-Objective Optimization Algorithms held at the 2009 IEEE Congress on Evolutionary Computation.     

A novel six degrees-of-freedom parallel manipulator for aircraft fuselage assemble and its trajectory planning

Parallel manipulators have been successfully used for pose adjustment. However, aircraft fuselages are heavy and have complex shapes, so the existing parallel manipulators are not suitable for aircraft fuselages. For the first time, this paper presents a novel six degrees of freedom parallel manipulator for aircraft fuselages. Compared with other parallel manipulators, the presented parallel manipulator is suitable for large round parts. The Jacobi matrix of the presented parallel manipulator is derived, which is expressed by roll, pitch, and yaw. Using the derived Jacobi matrix, inverse kinematics of the presented parallel manipulator is investigated systemically. Combining Newton’s second law with Euler equations, dynamic equations of the manipulator are derived. Using the derived dynamic equations, inverse dynamics of the manipulator are also investigated systemically. For improving safety and efficiency of fuselage pose adjustment, a new trajectory planning algorithm is proposed which is based on the minimum mean force. Simulation experiment results demonstrated the ability of the trajectory planning algorithm to achieve stable movement comparable to the time-optimal trajectory planning algorithm. At the conclusion of this paper, practical applications of the presented parallel manipulator are shown.     

牵引式下肢康复机器人机构参数优化及轨迹规划

为满足下肢运动功能障碍患者在不同阶段的康复训练需求,针对现有下肢康复机器人训练方式单一的问题,提出了一种可实现卧姿、坐姿训练模式的牵引式下肢康复机器人。首先,根据人体下肢运动机理和仿生原理,设计了一种五自由度混联机构构型。然后,建立了机器人的运动学模型,分别计算了其运动学正、逆解。接着,以人体下肢末端与机器人末端的工作空间重合度为目标函数,采用遗传算法对机器人的机构参数进行了优化,并求得人机系统矢状面内人体下肢的有效工作空间比为0.71。最后,规划了CPM（continuous passive motion,连续被动运动）、圆周运动和螺旋运动等3种康复训练运动轨迹,并根据优化后的机构参数搭建了机器人样机,通过运动捕捉实验验证了机器人结构设计与优化结果的合理性以及轨迹规划的正确性,表明该机器人能够满足下肢运动功能障碍患者的康复需求。     

A multi-objective optimisation model to integrating flexible process planning and scheduling based on hybrid multi-objective simulated annealing

Process planning and production scheduling play important roles in manufacturing systems. In this paper we present a mixed integer linear programming (MILP) scheduling model, that is to say a slot-based multi-objective multi-product, that readily accounts for sequence-dependent preparation times (transition and set up times or machine changeover time). The proposed scheduling model becomes computationally expensive to solve for long time horizons. The aim is to find a set of high-quality trade-off solutions. This is a combinatorial optimisation problem with substantially large solution space, suggesting that it is highly difficult to find the best solutions with the exact search method. To account for this, the hybrid multi-objective simulated annealing algorithm (MOHSA) is proposed by fully utilising the capability of the exploration search and fast convergence. Two numerical experiments have been performed to demonstrate the effectiveness and robustness of the proposed algorithm.     

Fuzzy multi-objective optimisation for master planning in a ceramic supply chain

In this paper, we consider the master planning problem for a centralised replenishment, production and distribution ceramic tile supply chain. A fuzzy multi-objective linear programming (FMOLP) approach is presented which considers the maximisation of the fuzzy gross margin, the minimisation of the fuzzy idle time and the minimisation of the fuzzy backorder quantities. By using an interactive solution methodology to convert this FMOLP model into an auxiliary crisp single-objective linear model, a preferred compromise solution is obtained. For illustration purposes, an example based on modifications of real-world industrial problems is used.     

机械手Pick-and-Place运动规划

针对机械手拾放操作(Pick and PlaceOperations ,P&PO)的避障及平滑轨迹要求,提出了一种摆线形式的机械手关节空间的运动轨迹规划方法。该方法只需要求解初始点和结束点的逆运动学方程,能够满足位置、速度、加速度的约束条件,可以在笛卡儿空间产生平滑的运动轨迹。利用一种简单有效的算法寻找到了一个位于初始点与结束点之间的第三点,修正后的摆线运动轨迹能够使机械手末端执行器通过该点以避开障碍。将该运动规划方法用于三自由度微操作机械手的P&PO作业中,试验结果验证了该方法的有效性。     

Evolutionary multi-objective concurrent maximisation of process tolerances

Concurrent tolerancing which simultaneously optimises process tolerance based on constraints of both dimensional and geometrical tolerances (DGTs), and process accuracy with multi-objective functions is tedious to solve by a conventional optimisation technique like a linear programming approach. Concurrent tolerancing becomes an optimisation problem to determine optimum allotment of the process tolerances under the design function constraints. Optimum solution for this advanced tolerance design problem is difficult to obtain using traditional optimisation techniques. The proposed algorithms (elitist non-dominated sorting genetic algorithm (NSGA-II) and multi-objective differential evolution (MODE)) significantly outperform the previous algorithms for obtaining the optimum solution. The average fitness factor method and the normalised weighting 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 the Pareto optimal fronts. Two more multi-objective performance measures namely optimiser overhead and algorithm effort are used to find the computational effort of the NSGA-II and MODE algorithms. Comparison of the results establishes that the proposed algorithms are superior to the algorithms in the literature.     

An improved multi-objective genetic algorithm for heterogeneous coverage RFID network planning

Recent research has demonstrated the potential benefits of radio frequency identification (RFID) technology in the supply chain and production management via its item-level visibility. However, the RFID coverage performance is largely impacted by the surrounding environment and potential collisions between the RFID devices. Thus, through RFID network planning (RNP) to achieve the desired coverage within the budget becomes a key factor for success. In this study, we establish a novel and generic multi-objective RNP model by simultaneously optimising two conflicted objectives with satisfying the heterogeneous coverage requirements. Then, we design an improved multi-objective genetic algorithm (IMOGA) integrating a divide-and-conquer greedy heuristic algorithm to solve the model. We further construct a number of computational cases abstracted from an automobile mixed-model assembly line to illustrate how the proposed model and algorithm are applied in a real RNP application. The results show that the proposed IMOGA achieves highly competitive solutions compared with Pareto optimal solutions and the solutions given by four recently developed well-known multi-objective evolutionary and swarm-based optimisers (SPEA2, NSGA-II, MOPSO and MOPS²O) in terms of solution quality and computational robustness.     

Hybrid evolutionary multi-objective optimization and analysis of machining operations

Evolutionary multi-objective optimization (EMO) has received significant attention in recent studies in engineering design and analysis due to its flexibility, wide-spread applicability and ability to find multiple trade-off solutions. Optimal machining parameter determination is an important matter for ensuring an efficient working of a machining process. In this article, the use of an EMO algorithm and a suitable local search procedure to optimize the machining parameters (cutting speed, feed and depth of cut) in turning operations is described. Thereafter, the efficiency of the proposed methodology is demonstrated through two case studies – one having two objectives and the other having three objectives. Then, EMO solutions are modified using a local search procedure to achieve a better convergence property. It has been demonstrated here that a proposed heuristics-based local search procedure in which the problem-specific heuristics are derived from an innovization study performed on the EMO solutions is a computationally faster approach than the original EMO procedure. The methodology adopted in this article can be used in other machining tasks or in other engineering design activities.