通用液压机械手设计 -圆柱坐标型

喜欢这套资料就充值下载吧。资源目录里展示的都可在线预览哦。下载后都有，请放心下载，文件全都包含在内，图纸为CAD格式可编辑，有疑问咨询QQ:414951605 或 1304139763p 论文查重15%，见格式中有说明 毕业设计(论文) 任 务 书 课题名称 通用液压机械手设计 指导教师 学院 机械与电气工程学院 专业 机械设计制造及其自动化 班级 学生姓名 学号 开题日期 一、 主要任务与目标： 1.1主要任务 认真学习《机器人》和《液压与气压传动》方面有关的书籍资料，尤其是有关机械人或机械手的结构图册，进一步熟悉机械人的基本理论和基本结构。 机械手的运动速度快，要求质量轻、转动惯量小，配置合理，运动平稳，定位精度和重复定位精度高。通过调研和分析确定本设计要求的LR20通用液压机械手设计的工作原理和传动方案。完成机械手的结构设计，绘制装配图和全部零件图。总图量达到3张A0。 按照毕业设计要求完成，认真阅读15篇以上有关《机器人》、《液压与气压传动》方面最新论文、经典教材和专著，为机器人设计作好充分准备，并结合设计实际完成开题报告、文献综述、外文翻译、设计计算书的撰写。 1.2目标 机械手能够代替工人长期连续高效地完成设定动作，在自动化装置中应用广泛，也是企业解决用工荒的重要方法，可见机械手的应用维护将越普遍。 通过本课题的训练，初步掌握新产品设计的工作流程。本设计课题旨在提高机械产品的系统设计和结构设计能力，有利于在生产企业从事新产品开发设计和装备设计能力，同时为从事企业生产过程中的设备管理和技术管理工作奠定良好的基础。 二、主要内容与基本要求： 2.1技术参数： 　工件：棒料，重量20公斤，最大长度300mm, 直径100－120mm 　工作区域：取物高度不小于600-1500mm，作业半径500-1500mm；回转角度270?， 工作速度：移动速度3m/s，定位精度±0.3mm；转动速度45?/s。 2.2主要内容 设计计算书（论文）10000字以上，按学校格式要求打印、装订。内容结合设计项目撰写。包括：前言、系统方案的分析与确定、结构参数的设计计算、运动分析与计算、结构强度设计计算及校核、精度分析、结束语等。 根据设计参数确定结构，选择合适的执行机构、驱动方式及机械传动部件。气动元器件可选择合适的标准器件，必要时进行自行设计，重点是整体结构。 装配图用A0绘制，总图为可画3张A0。。 开题报告、文献综述、外文翻译等，符合各项规定要求。 2.3基本要求 图样全部用计算机绘制，符合最新制图标准；投影正确，表达完整，布局合理。 设计要满足功能，注重性能、结构和装配工艺性，外观造型力求简洁明快；实用可靠。 计算书理论分析完整清楚；设计推导简明扼要；计算正确可靠。避免冗长，杜绝抄袭。 开题报告、文献综述、外文翻译等要紧扣设计主题，认真分析、归纳、加工、提炼，切不可照搬。 三、计划进度： 　　　2013.09.16~2013.10.15 接受任务，熟悉设计内容，搜集相关资料； 　　　2013.10.15~2014.01.10 完成设计图样和说明书初稿； 　　　2014.01.11~2014.04.20　修改图样、说明书，完成二稿； 　　　2013.10.15~2014.04.20　完成开题报告，文献综述和翻译； 　　　2014.04.21~2014.05.10　修改、检查全部资料，打印、上交资料； 2014.05.11~2011.05.20 准备论文答辩。 四、主要参考文献： [1]张福学编．机器人技术及其应用[M].．北京：电子工业出版社，2000 [2]朱世福，王宣银.机器人技术及其应用[M]..杭州：浙江大学出版社，2005 [3]《工业机械手图册》编写组.工业机械手图册[M]..北京：机械工业出版社，1978 [4]路甬祥.液压气动技术手册[M].北京：机械工业出版社，2004.5 [5]徐灏.机械设计手册[M]. 北京：机械工业出版社，2003.1 指导教师 2013年10月11日 系 主 任 2013年10月11日 机器人和电脑一体机制造 关于生成工业机器人机械手的运动 K. Kaltsoukalas, S. Makris, G. Chryssolouris 佩特雷大学实验室制造系统和自动化,希腊 论文信息 文章历史： 关键词： 2013年10月17日收到初稿 路径规划 2014年9月19日收到修改稿 工业机器人运动 2014年10月8日通过审核 网格搜索 2014年10月29日网上资源共享 摘要 在这个研究中，提出了一个智能搜索算法定义所需的位置和工业机器人机械手的定位效应路径的想法。这个算法通过选择和评估机器人的配置逐步达到所需的配置。构造网格的机器人替代配置使用一组参数,减少了搜索空间并减少计算时间。对于可选择性的评价，使用多个标准，用以满足不同的要求。替代的配置重点是给机器人的关节，主要影响末端执行器的位置。网格的分辨率和尺寸参数的设置在期望输出的基础上，高分辨率通过对目标位置只提供一些中间点用于平滑的路径和一个粗略的估计。规划的路径是一系列的机器人配置。这种方法为一个没有经验的程序员自动生成机器人路径提供了方便，这能达到预期的标准而不必记录中间点到目标位置。 2014 Elsevier有限公司保留所有权利 介绍 近年来，由于能适应不同的市场需求和产品结构的变化，对柔性制造系统的需求日益增加。动态生产环境要求越来越多的装配制造资源重新配置。自动装配系统，如机器人。它们的灵活性通常被制约因为高的编程工作要求机器人轨迹调整去适应不同的装配单元布局。经验丰富的机器人程序员不得不花大量时间采用常规的编程方法优化每一个具体的应用机器人的路径。一种广泛应用的方法是演示编程，通过顺序移动机器人的每个位置并记录中间点的位置来示教。通过机器人控制器的连接记录点产生机器人的最后路径，路径考虑到机器人的动力学约束并通过所有的约束点。机器人的最终轨迹高度依赖记录点和程序员各自的编程经验。机器人自动路径规划提出了一个问题，在过去的几十年里如何实现移动机器人从初始到最终的位置进行的研究，主要集中在路径规划避障。 一种运动规划技术是通过采样配置空间来构建近似的模型。在过去的几年里，已经有很多为基于采样的运动规划算法进行改善的工作在进行了。在不同的类别中分类所有的规划者很难定义一个单一的标准。经典的分离是基于路线图规划者和逻辑树规划者之间。概率路线图路径规划中引入一个计算机器人无碰撞路径的新方法。这个方法分两个阶段进行：学习和查询。在学习阶段，一个概率图是通过生成机器人的随机免费配置和使用一个简单的运动规划连接它们建设，也被称为当地的规划师。不同的方法已被用来解决各种各样的问题，为了可变形物体的运动规划而提出了构建和查询路线图两种不同的方法。还提出了另一个变形的技术可以应用到生成的路径中。介绍了障碍物的概率图法，在生成节点的几种策略进行了阐述并且提出了复杂三维工作区多级连接策略。通过扩展规划无碰撞运动触点配置的空间概率图范例 ，随机规划被描述为在任意两个多面体固体之间的CF兼容的运动规划。这种方法的关键是随机产生CF兼容的配置的新的采样策略 。 引入了快速扩展随机树的概念，基本的想法是，初始样品（起始组态）是树的根和新产生的样本，然后连接到树中已经存在的样品。两个快速扩展随机树（RRTS）扎根在开始和在目标配置中。树的每一个探索周围的空间，也提出了对彼此通过一个简单的贪婪算法的使用。虽然它最初被计划设计为人手臂的运动动作（建模为一个自由度的运动链），在无碰撞把握和操作任务的自动图形动画中，该算法已被应用于各种路径规划问题。逻辑树规划者们已证明是处理实时规划和重新规划问题的一个很好的框架。为了修复快速扩展随机树进行更改时配置空间，一个重新规划算法被提了出来。不是放弃当前RRT，该算法有效地只消除了新无效部分并保持休息。在工业环境中移动机器人的动态避障已经开始研究调查。然而，工业机器人通常被编程以执行预定义的路径。机器人编程的方法主要有两大类：在线编程和离线编程。 为了用户的互动转化为简单任务提出了一个在线路径规划支持系统产生可接受的轨迹，适用于工业机器人的编程的问题。建议得出了一种新的方法，即机器人编程使用增强现实环境。为现场的机器人编程方法所需提供灵活性和适应性以应对不同环境。路径规划方法包括生成路径的束搜索算法。有相似的研究表明，用户能够执行的操作，即通过点的选择和修改，为了实现一个光滑的无碰撞路径。一个机器人运动规划的方法，是根据预先计算的全局配置空间（C-）的连通性提出的。运动规划，包括离线阶段和在线阶段和无碰撞的路径将通过一个多分辨率策略下使用A*算法在C-空间中搜索。 在这个研究中，提出了一种智能搜索算法去定义工业机器人机械手端部执行器所需的位置和方向的路径。可供选择的配置的最大数量被一步步选择和评价，直到所需的配置是接近预定的误差范围内为止。替代的配置是一个聪明的方式产生，重视主要影响机器人的空间位置的关节角度。在配置空间上，有一个人工推导机器人的替代组态网格。一套巧妙的参数用于减少搜索空间，提高算法的性能。对于替代品的评价，使用多个标准，可以提高算法拓展的灵活性，这是为了满足不同的要求，即满足最短路径的要求。 On generating the motion of industrial robot manipulatorsK. Kaltsoukalas, S. Makris, G. Chryssolourisn,1Laboratory for Manufacturing Systems and Automation, University of Patras, Greecea r t i c l e i n f oArticle history:Received 17 October 2013Received in revised form19 September 2014Accepted 8 October 2014Available online 29 October 2014Keywords:Path planningIndustrial robot motionGrid searcha b s t r a c tIn this study, an intelligent search algorithm is proposed to define the path that leads to the desiredposition and orientation of an industrial robots manipulator end effector. The search algorithm graduallyapproaches the desired configuration by selecting and evaluating a number of alternative robotsconfigurations. A grid of the robots alternative configurations is constructed using a set of parameterswhich are reducing the search space to minimize the computational time. In the evaluation of thealternatives, multiple criteria are used in order for the different requirements to be fulfilled. Thealternative configurations are generated with emphasis being given to the robots joints that mainlyaffect the position of the end effector. Grid resolution and size parameters are set on the basis of thedesired output. High resolution is used for a smooth path and lower for a rough estimation, by providingonly a number of the intermediate points to the goal position. The path derived is a series of robotconfigurations. This method provides an inexperienced robot programmer with flexibility to generateautomatically a robotic path that would fulfill the desired criteria without having to record intermediatepoints to the goal position.& 2014 Elsevier Ltd. All rights reserved.1. IntroductionIn the recent years, there is an increasing need for flexiblemanufacturing systems, capable of adapting to different marketdemands and product-mix changes 1. The dynamic environmentin production requires an increasing number of reconfigurationson assembly manufacturing resources 3. In automated assemblysystems such as robots, the flexibility is normally restricted due tothe high programming effort required in order for robot trajec-tories to adjust to different assembly cell layouts. Experiencedrobot programmers have to spend considerable time in order tooptimize the robotic paths for each specific application by usingconventional programming methods. A method that is widelyused is programming by demonstration, where the intermediatepoints to the goal position are recorded by sequentially movingthe robot to each position using the teach pendant. The robotsfinal path is generated by connecting the recorded points via arobot controller, which tries to pass through all the points bytaking into consideration the dynamic constraints of the robot. Therobots final trajectory is highly dependent on the points recordedand the experience of the respective programmer, who has carriedthis out. Automatic path planning for robotics poses the questionas to how a robot can move from its initial to the final position andhas been investigated during the last decades mainly focusing onpath planning for collision avoidance.One of the techniques for motion planning is the construction ofapproximate models by sampling their configuration space. Over thelast few years, there has been a lot of work carried out for theimprovement of sampling based motion planning algorithms. It ishard to define a single criterion that can classify all planners indistinct categories. The classical separation is between roadmap-based planners and tree-based planners 4.The probabilistic road-map path planning was introduced in 5 as a new method ofcomputing collision-free paths for robots. The method proceeds intwo phases: those of learning and query. In the learning phase, aprobabilistic roadmap is constructed by generating the robotsrandom free configurations and connecting them using a simplemotion planner, also known as a local planner. Different approacheshave been used to address a variety of problems. In 6, two differentmethods for constructing and querying roadmaps are suggested forthe motion planning of deformable objects. Another two deforma-tion techniques that can be applied to the resulting path are alsopresented. The obstacle probabilistic roadmap method is introducedinto 7, where several strategies for node generation are describedand multi-stage connection strategies are proposed for cluttered3-dimensional workspaces. In 8, a randomized planner is describedfor planning CF-compliant motion between two arbitrary polyhedralsolids, by extending the probabilistic roadmap paradigm for plan-ning collision-free motion to the space of contact configurations. Thekey to this approach is a novel sampling strategy of generatingrandom CF-compliant configurations.Contents lists available at ScienceDirectjournal homepage: and Computer-Integrated Manufacturinghttp:/dx.doi.org/10.1016/j.rcim.2014.10.0020736-5845/& 2014 Elsevier Ltd. All rights reserved.nCorresponding author.E-mail address: xrisollms.mech.upatras.gr (G. Chryssolouris).1Tel.: 30 2610 997262.Robotics and Computer-Integrated Manufacturing 32 (2015) 6571The concept of Rapidly-exploring the Random Tree is introducedin 9. The basic idea is that an initial sample (the starting config-uration) is the root of the tree and newly produced samples are thenconnected to the samples already existing in the tree. In 10, twoRapidly-exploring Random trees (RRTs) were rooted at the start andduring the goal configurations. Each one of the trees explores thespace around it and also advances towards each other through theuse of a simple greedy heuristics. Although it was originallydesigned that motions be planned for a human arm (modeled as a7-DOF kinematic chain), in the automatic graphic animation ofcollision-free grasping and manipulation tasks, the algorithm hasbeen applied to a variety of path planning problems. Tree-basedplanners have proven to be a good framework for dealing with real-time planning and re-planning problems. In 11, a re-planningalgorithm is presented for repairing Rapidly-exploring RandomTrees when changes are made to the configuration space. Insteadof abandoning the current RRT, the algorithm efficiently removesonly the newly-invalid parts and maintains the rest. Dynamicobstacle avoidance has been investigated for the mobile robotsfound in industrial environments in 12. However, industrialmanipulators are typically programmed to execute predefined paths.The two main categories of robotic programming methods are thoseof online programming and offline programming.In 13, an online path planning and programming supportsystem is proposed for the transformation of the users interactioninto a simplified task that generates acceptable trajectories,applicable to industrial robot programming. In 14, a novelapproach to robot programming using an Augmented Realityenvironment was proposed, offering flexibility and adaptabilityto different environments when an on-site robot programmingapproach was desired. The path planning methodology included abeam search algorithm to generate paths. In 15, there is a similarstudy, where the user is able to perform operations, namely via-points selection and modification, in order for a smooth andcollision-free path to be achieved. An on-line robot motionplanning approach that is based upon pre-computing the globalconfiguration space (C-space) connectivity is proposed. In 16, themotion planner consists of an off-line stage and an on-line stageand the collision-free path is searched in this C-space by using theA*algorithm under a multi-resolution strategy.In this study, an intelligent search algorithm is proposed todefine an industrial robot manipulators path that leads to thedesired position and orientation of the end effector. A maximumnumber of alternative configurations are selected and evaluated ineach step until the desired configuration is approached within apredefined error. The alternative configurations are generated in aclever way giving emphasis to the joint angles that mainly affectthe robots position in the workspace. In the configuration space,there is a grid constructed to derive the robots alternative config-urations. A set of clever parameters are used to reduce the searchspace and increase the performance of the algorithm. In theevaluation of the alternatives, multiple criteria that would enhancethe algorithms flexibility to extend are used, in order for thedifferent requirements, namely the shortest path, to be fulfilled.2. ApproachFor an industrial robot manipulator (usually six degrees offreedom), the path planning problem is described via threehierarchical levels as shown in Fig. 1. For a given starting and goalposition, the requested paths include the robots intermediateconfigurations, where each configuration is a set of six jointparameters.2.1. Grid search of the alternative configurationFor an industrial robot manipulator with n degrees of freedom(n-DoF), the alternative configurations are defined from a set of njoint angles. If the possible values of each joint angle are equal to2k1, with resolution d(Fig. 2), the number of alternativeconfigurations is given by the following equation:Number of alternative configurations N 2k1n1For each joint angle that can be incremented, a dnresolutionhas to be selected.The number of alternative configurations increases for a robotwith higher degrees of freedom and a larger grid (k) size. For thisreason, the following parameters are used for the reduction of thealternative configurations, where a multi-criteria evaluation willbe carried out as follows:?Decision Horizon (DH): This parameter is taking values from oneto n (DoF of the robot). Starting from the base of the robot, DHFig. 1. Hierarchical levels for path planning problem (6 DOFs robot).K. Kaltsoukalas et al. / Robotics and Computer-Integrated Manufacturing 32 (2015) 657166parameter defines the degrees of freedom which are taken inconsideration while constructing the grid of the alternativeconfigurations. For joint angles, in the decision horizon, a grid iscreated as shown in Fig. 2. For the remaining joints, outside thedecision horizon, only a number of samples are randomly takenin order to have complete alternative robot configurations. Therobots joints are separated into those that mainly affect therobots movement in the workspace (position of the end effector)and those that mainly affect the orientation of the end effector.When only the target position has to be reached and theorientation of the end effector is ignored this parameter couldbe reduced for better performance and less computational time.?Maximum number of alternatives (MNA): A maximum number ofalternatives from the grid in the decision horizon are randomlyselected for evaluation. If MNA4N then automatically theparameter MNAN.?Sample Rate (SR): A sample rate is defined as the number ofsamples taken from the joints, outside the decision horizon, inorder to form the robots complete alternative configurations.When the orientation of the end effector is considered, SRparameter should be increased in order to generate morealternative configurations which affect the orientation of theend effector.For an industrial manipulator with 6 DOF (n6, Fig. 3), even fork1 and d101 for each degree of freedom, the number ofalternative configurations is given by Eq. (1): (Figs. 46)N 36 729 Alternative neighbor configurations of robotBy setting DH3, only the first three degrees of freedom aretaken into consideration whilst the number of the alternativeconfiguration on the grid drops down toNDH 3 33 27 Alternative configurations for DH 3The maximum number of alternatives in the decision horizon isdefined as follows:MNArNThe probability of getting the alternative configuration closer tothe desired position is given by the following equation:pDH;MNA MNAN2From 1 and 2pDH; MNA MNA2k1DH3Therefore, in the example with the 6 DOFs robot where, thenumber of the alternative configurations was found to be N27(for DH3)If MNA20, the probability of getting the alternative config-uration that is closer to the desired position is given by Eq.(2)Fig. 2. Available joint angles for each degree of freedom in the DH.Fig. 3. COMAU Smart5 Six, 6 DOF, Industrial Manipulator.Fig. 4. Alternative configurations using MNA3, DH3 and SR2 parameters for 6 DOFs.K. Kaltsoukalas et al. / Robotics and Computer-Integrated Manufacturing 32 (2015) 657167Probability to get the best alternative configuration in DH,P(DH3, MNA20) 20=27 74%Consequently, for exhaustive search in DH (P1), MNAN27Giving sample rate (SR)2 for each alternative in the decisionhorizon, two samples are taken from the rest of the joints; thus,the number of complete alternative configurations becomesN completeMNA 27; SR 2 MNAnSR 27 ? 2 54 complete alternativesIn general, the number of complete alternative configurationsfor the predefined MNA and SR parameters is given by thefollowing equation:Number of complete alternative configurationsMNA;SR;Ncomplete MNAnSR4The proposed algorithm does not have to search the entire work-space of the robot. During each iteration, only a maximum numberof neighbor configurations are evaluated. Calculation time for acomplete target path depends on the distance of the starting pointto the target. Calculation time also increases when more inter-mediate points are requested for a smoother path that betterfulfills the desired criteria.2.2. Evaluation of the alternative configurationsMultiple criteria are used for the evaluation of the alternativeconfigurations. A decision matrix is built as shown in the followingtable. In the context of this study, two criteria have been taken intoconsideration, those of the distance due to translation and the distancedue to rotation from the target position and the robots orientation.Despite the fact that the proposed algorithm could also be used justfor the definition of the joint parameters for a given position andorientation of the robots end effector (inverse kinematics), the mainpurpose of this study is to plan the robots path, which better fulfillsthe multiple criteria defined by the user. The search algorithm is easilyextensible for more criteria. (Tables 1 and 2)The utility for each of the alternatives is calculated as theweighted sum of the distance due to translation and to orientation.Ui WtjjXi?XjjWrfqi;q5where Xi?X, is the Euclidean distance of the end effector from thetarget position and fqi;qtarget is the distance due to rotation(orientation of the target configuration).The weight factors Wtand Wrare selected from the user inorder to give emphasis to the desired criterion. If the user is onlyinterested in the position of the end effector, the factors Wt1 andWr0 should be used.The metric of the distance between rotations is the Norm of theDifference of Quaternions, described in detail in 17.fqi;qtarget min fjjqi?qtargetjj;jjqiqtargetjjg6where, J J denotes the Euclidean norm (or 2-norm) and q theorientation of the end effector, expressed in quaternions. Themetric gives values in the range 0;ffiffiffi2p?.The alternative configuration with the smaller utility function isselected at each decision point.Path search algorithmInput: Target position (X Y Z), target orientation (Euler anglesZYZ”), DH, MNA, SR, (k, d: grid size & resolution)Output: Target configuration (123 n) & the sequenceof the intermediate configurations (path)1. The Grid parameters k & dare defined.2. The DH is defined. DH1/number of the robots DOF.3. The Grid is constructed for DH. Alternatives are generated.4. The MNA is selected in order to enable a configuration near thetarget.5. The SR is defined. Random samples are taken from the jointsafter the DH.6. A decision matrix is built; MNAnSR complete alternatives areevaluated. The alternative configuration that provides thesmaller value of the utility function is selected.7. The resolution and the size of the grid are redefined.8. Steps 17 are repeated until there is an alternative configura-tion that provides the target position and target orientationwithin the pre-defined distance error.2.3. Industrial manipulator motion generationThe proposed algorithm calculates the robots sequential,intermediate configurations in order to approach the target posi-tion while fulfilling the predefined criteria for the path. Everyconfiguration of the robot is within its joint limits. The robotcontroller uses the derived path in order to generate the motion ofthe industrial manipulator, taking into consideration the dynamicconstraints of the robot.3. ImplementationThe proposed algorithm has been implemented in Matlab withthe use of the Robotics Toolbox 18. The flowchart of thealgorithm is presented in the following figure.Fig. 5. Industrial robot motion generation.Table 1Evaluation of the alternatives according to the distance criteria.AlternativeConfigurationsNormalized criteriaUtility valueDistance dueto translationDistance dueto rotationUi W1Ci1 W2Ci2(where W1and W2the criteria weights)Alternative 1C11C12U1Alternative 2C21C22U2Alternative 3C31C32U3AlternativemMNAnSRCm1Cm2UmK. Kaltsoukalas et al. / Robotics and Computer-Integrated Manufacturing 32 (2015) 6571684. ResultsIn Figs. 7 and 8, it is observed that the grid size and resolutionparameters (k, d) have a great influence on the smoothness ofthe path towards the desired position. Lower values of theseparameters lead to better paths, however, the computational timeis increased.4.1. Search algorithm parameters correlationIn order for the correlation among the search parameters MNA,DH and SR to be examined, a set of experiments was designedusing the Taguchi method with the objective of process timeminimization. The initial values of the grid parameters wereselected to be k5 and d0.1 rad (E61).4.1.1. Taguchi design of experimentsThe effect of the search parameters DH, MNA, and SR will beexamined so as for the process time required for finding the pathto be minimized to the target position. Four levels are selected foreach parameter. The proposed set of experiments, according to theTaguchi method, is given in L16 table.L16 table:Fig. 7. Grid resolution effect on the on the path (a) d0.01 rad and (b) d0.1 rad.Fig. 6. Flowchart of the proposed algorithm.Table 2Set of experiments for 4 levels of the parameters DH, MNA, and SR.Exp. no.DHMNASRTime (Sec)122510.60225020.57327531.124210041.82532540.72635030.91737520.918310011.17942520.551045010.911147542.1612410031.601352531.291455042.841557510.4816510022.01K. Kaltsoukalas et al. / Robotics and Computer-Integrated Manufacturing 32 (2015) 6571694.1.1.1. Analysis of means (ANOM)?From Figs. 9 and 10, it is observed that the target position of theend effector is better approached for DH3 (first three degreesof freedom of the robot). The higher values of MNA and SR aresufficient only when the orientation is taken into consideration.In order for both the target position and orientation of the endeffector to be approached, the best results (lowest computingtime) are given for DH3, MNA25 and SR2.?The interaction among the parameters DH, MNA and SR andtheir effect on the computing time is presented in Fig. 11. It isconfirmed that for lower DH values sufficient SR has to beconsider whilst for higher DH values the SR value should beminimum for less computing time.Fig. 8. Grid size effect on the path (a) path generated for k1 and (b) path generated for k5.Fig. 9. DH, MNA and SR vs. processing time (target position).Fig. 10. DH, MNA and SR vs. processing time (target position and orientation).Fig. 11. Interaction of DH with SR (target position).K. Kaltsoukalas et al. / Robotics and Computer-Integrated Manufacturing 32 (2015) 6571705. ConclusionsIn this study, an intelligent search algorithm is proposed todefine the path that leads to the desired position and orientation ofthe end effector of an industrial robot manipulator. The gridparameters as well as the search algorithm parameters DH, MNA,SR are proven to be drastically reducing the processing