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塔里木大学毕业论文（设计）中期检查记录表2016年 4 月 18 日学生姓名杨海斌班级机械16-1课题名称 自走连续振动式红枣收获机课题完成进度（学生自述） 第 1 周 查阅相关文献，为撰写开题报告做准备工作。 第 2-3 周 确定设计方案，粗化结构草图，完成开题报告。 第 4-5 周 根据工作要求，查阅相关手册书籍，选择、设计计算同时校核各零部件。 第6-10周 利用三维设计软件完成各零部件的三维实体建模。基本完成三维图的绘制，已开始二维装配图的绘制。存在的问题及整改措施（学生自述） 建立的三维模型需要细节的修改和补充，没有完成规定的任务进程。需要加快任务进度，尽快完成二维图的绘制。指导教师意见（课题进展情况、优缺点、整改措施等）指导教师签名 年 月 日学院意见负责人签名 年 月 日塔里木大学毕业论文（设计）任务书学院机械电气化工程学院班级机械16-1学生姓名杨海斌学号8011212125课题名称 自走连续振动式红枣收获机起止时间2015年11月27日2016年5月28日（共 14 周）指导教师郭文松职称课题内容： 设计要求一种自走连续振动式红枣收获机包括装有行心轮的机架以及分机，还有钳式振动头。其特征是:在机架上放置一个盛果箱，在盛果箱的前端上部安装一根吸气管，所说的风机安装在吸气管上，吸气管的自由端口连接着一个红枣捡拾吸头，在盛果箱后部上方的轻杂物沉降室的下端连接这一个过滤网兜，在盛果箱中部顶端设计一个竖直向下延伸的挡枣部，挡枣部的长度与盛果筐的宽度相同。在挡枣部下方的盛果箱内放置一个盛果筐，盛果筐的前端顶部与盛果箱的前臂之间设计一个倾斜的红枣下滑板。为了实用的方便在风机和红枣捡拾头直接设计一根波纹管。而且盛果筐四周壁上均设置着网孔，挡枣部为平直的挡板，所说的红枣下滑板为栅栏板上的缝隙宽度必须比红枣的直径小。 拟定工作进度（以周为单位） 第12周 查阅相关文献，撰写开题报告。 第34周 根据国内外现有的红枣收获机确定棒杆式红枣收获装置的总体设计方案，绘制总体结构简图。 第56周 根据工作要求，计算并查阅相关手册，选择和设计各零部件。 第79周 运用AutoCAD软件，绘制二维零件图和装配图。 第1011周 运用三维设计软件完成整机各零部件的三维建模并进行运动仿真。 第12周 从工艺性能，经济性能，实用性能等方面对产品进行综合评价、校核、修正。 第13周 完成设计说明书。 第14周 整理材料，准备答辩主要参考文献1成大先.机械设计手册 单行本 液压传动.化学工业出版社，2004: 311-317.2西北工业大学机械原理及机械零件教研室.机械设计.第九版.高等教育出版社，2014:72-78.3朱家诚.机械设计课程设计.合肥工业大学出版社，2005:43-201.4甘永立.几何量公差与检测.上海科学科技出版社，2008:25-45.5许福玲.液压与气压传动.机械工业出版社，2007:3-5.6王乃康，矛也冰，赵平.现代园林机械.中国林业出版社，2000:147-152.7大连理工大学工程图学教研室.机械制图.高等教育出版社，2007:1-346.8成大先.机械设计手册 单行本 机械传动.化学工业出版社，2004:102-210.任务下达人（签字） 郭文松 2015年 11 月 20 日任务接受人意见 无意见任务接受人签名 杨海斌 2015年 11 月 20 日注：1、此任务书由指导教师填写，任务下达人为指导教师。2、此任务书须在学生毕业实践环节开始前一周下达给学生本人。3、此任务书一式三份，一份留学院存档，一份学生本人留存，一份指导教师留存。塔里木大学毕业论文（设计）开题报告课题名称 自走连续振动式红枣收获机 学生姓名 杨海斌 学 号 8011212125 所属学院 机电院 专 业 机械设计制造及其自动化 班 级 16-1 指导教师 郭文松 起止时间 2015-11-27 机械电气化工程学院教务办制填 表 说 明一、学生撰写开题报告应包含的内容：1、本课题来源及研究的目的和意义；2、本课题所涉及的问题在国内（外）研究现状及分析；3、对课题所涉及的任务要求及实现预期目标的可行性分析；4、本课题需要重点研究的、关键的问题及解决的思路；5、完成本课题所必须的工作条件及解决的办法；6、完成本课题的工作方案及进度计划；7、主要参考文献（不少于7篇）。二、本报告必须由承担毕业论文（设计）课题任务的学生在接到“毕业论文（设计）任务书”的两周内独立撰写完成，并交指导教师审阅。三、开题报告要求手写体，字数在3000字以上，由学生在本报告册内填写，页面不够可自行添加A4纸张。四、每个毕业论文（设计）课题须提交开题报告一式三份，一份学生本人留存，一份指导教师存阅，一份学生所在学院存档，备检备查。开题报告正文1、 本课题来源及研究的目的和意义： 该课题是指导教师拟定，学生本人选定的。该课题研究的目的是为了解放生产力，发展生产力，从而使生产力适应生产关系、经济基础适应上层建筑，加快我国的社会主义建设贡献一份力量！该课题研究的意义主要是培养学生的动手和动脑能力，使理论知识与实践相结合，从而获得直接经验，为踏入社会做好准备。 枣树在我国的分布很广，一般来讲，小气候冬季最低气温不低于-32摄氏度，就可以栽培植枣。枣树在我国大面积经济栽培主要在山东、河北、山西、陕西五省的黄河流域，近年来安徽、甘肃、湖南、湖北发展很快。红枣为温带作物，适应性强，营养丰富，富含铁元素和维生素。红枣素有“铁杆庄稼”之称，具有耐旱、耐涝的特性，是发展节水型林果业的首选良种。由于经济的发展，特色农业的建立，红枣种植成为现代农业发展的一条新的产业项目，红枣产业已呈现出区域化布局、规模化发展、多种栽培模式尽显其效的新模式。各地根据自己的情况确立了不同的枣树种植面积，为农民增加了经济收入。随着红枣的种植面积的增加，红枣的机械化作业在红枣栽培中的重要性逐渐凸显。依据最近几年新疆林果业发展的态势分布。新疆果树种植每年以10%的速度递增，由于林果业的快速发展，各地正形成了较大的种植规模，每年收获季节需要投入大量的劳动力来完成水果采收。可以预见到，再过35年，新种植的果树进去盛果期后，水果采收作业将会出现因劳动力短缺，采收不及时，而直接影响果品质量和造成大量损失的问题。这是因为，水果采摘是一项劳动投入量很大的作业，有些水果因成熟期不一致，需要多次采摘才能完成收获;而有些作为鲜食或作为加工用途的果品，因市场对于果实外观要求较高，不能有碰伤、刮伤、压裂等机械损伤，采收这些水果时必须小心翼翼;另外，水果收获是在离地面35米高的空中作业，以上原因决定了水果采摘是一项费时、费工、费力的作业。人工采收水果的速度缓慢，大面积发展水果种植时，必须要依靠机械化来提高采摘效率。据有关资料介绍，有些鲜食水果的采收用工量较大，占水果生产总量用工的50%以上，导致特色果品的生产成本过大，不能满足向果品加工企业提供数量充足、质量优越、价格相对低廉的原料，这样极不利于企业直接参与市场竞争。可移式红枣收获机的研究，就是针对红枣的采摘时的要求进行设计的采摘机械，红枣的机械化收获对提高收获效率，降低收获作业成本，做到适时收获，减少收获过程中造成的机损失，保证红枣质量，促进枣业生产的规范化、标准化具有重要现实意义。2、 本课题所涉及的问题在国内外研究现状及分析： 由于机械振动式红枣收获机具有结构简单，操作方便，作业可靠，适用范围广和通用性好等特点，因此具有广阔的推广应用前景。近几年来，由于新疆特色红枣种植模式的调整，矮化值方式将成为部分林果的主要种植方式，因此机械振动式红枣收获机将成为今后机械采收的一个新的有待解决的问题和关键影响因素的基础研究。 国外对水果机械化收获技术的研究较多，机械采摘在美国、西班牙、俄罗斯、意大利、英国、德国、丹麦、匈牙利等国家的果园应用较为普遍。目前，机械采摘量较大的作物有苹果、葡萄、甜橙、桃、李、杏、樱桃、油橄榄、核桃、扁桃等。他们采用的机械收获方法主要有:震摇法、梳刷法、撞击法、水力法、半机械化采收等方法。但是，针对红枣收获的采摘机械比较少。据了解，美国的坚果收获已全部实现机械化，美国的葡萄、柑桔类水果的机械化收获问题也解决的较好。意大利生产一种鲜食水果收获机，专门用于苹果、梨、杏、李子等鲜食水果的操作，水果收获的效率可大大提高。同时也能避免和减少水果在收获过程中的机械损伤。除了收获机械之外，还需要引进适于机械化收获的品种和果园修剪等管理技术。例如，在法国和意大利，为实现水果作业机械化，把葡萄树普遍栽成扁平形，并花了很大的力量栽培修剪。栽果树时，树与树排行成列，既有较好的光照与通风，又便于拖拉机进去行间松土、施肥、喷药和采摘。法国的勃拉特研所据此设计制造了一种高架式葡萄收获机，成功地解决了酿酒用葡萄的收获问题。国外有很多发展水果机械化收获的经验，值得我们学习和研究。 总之，国外的水果收获机械研究主要在鲜食水果的收获中，他们是着眼于市场针对性的研制各种收获机械。他们不光强调机械一定要适应当地农园艺的要求，而且从生物学角度、农园艺角度加大科研力度，并开发利于机械化作业的新品种、新农园艺等，为机械化作业创造条件。这样就提高了水果的机械化采收作业率。我们国家虽然红枣的种类很多，种植面积大，但是对于机械化采摘红枣的技术还是比较少，机械化作业水平很低。近年来，作为鲜食上市的水果收获仍没有完全实现机械化。这是因为长在果树上的果实的生长形态不适于机械化采摘，而市场对于商品果外观要求又较高，不能有碰伤、擦伤等机械损伤的缘故。虽然这种水果收获机需要人工辅助摘果，但摘下后水果的输送、装箱等过程全部是机械化操作，水果收获的效率可大大提高，同时也能避免和减少水果再收获过程中的机械损伤。根据全国各地调查资料显示，目前我国的水果机械还是只停留在节水灌溉、滴灌、施肥一体化，包装保鲜等有限的几个工序上，在清洗、分级中偶有使用，而水果采摘机械还是很少。我国的水果采摘机械种类很少，大型的机械化设备使用率很低，只有少量的半自动机械在使用。比如，可移动水果采摘梯、可伸缩式高枝采果器这样的改进型机械。这些机械虽然在某种意义上使生产效率提升，提高了水果的采摘质量。但是对于大面积的果树收获还是不能满足要求，像红枣这样的果实数量多，结果时，红枣分布在果树的各个部位，所以如果要提高生产率就需要机械化程度比较高的水果采摘机械来完成。 目前，我国红枣收获主要靠人工手摘和借助简单工具辅助，如云梯和采果刀等红枣，劳动强度较大，用工量很多。采收整体技术水平较低，在操作上都为手动控制。采收机械的研究在我国仍处于起步阶段，尚未见成熟先进的实用机具报道。当前,机械采收的方法主要有振摇法和梳刷法。其中,振摇法是应用最多的一种方法, 适用于采收大多数干果、坚果和部分鲜食水果；而梳刷法适用于采收草莓、葡萄等浆果。新疆兵团应根据林果生产实际,从易于实现机械采收的红枣、核桃等特色干果的收获入手, 研发基于振摇原理的干果收获机具。新疆农垦科学院农机研究所已首次从意大利引进了一台干果收获机成套设备, 并于2007年9 月在新疆哈密农十三师火箭农场进行了红枣、核桃机械采收的适应性试验, 效果较好。自2008年开始，新疆农垦科学院机械装备研究所经过反复研究，终于成功研制出了“4YS-24红枣收获机”。该红枣收获机每小时可采摘50棵枣树，采净率达到91.5% ，工效提高了10倍左右，而人工采摘红枣每小时只能采摘5棵枣树。总的来说,新疆兵团以及国内林果业机械化发展步伐缓慢,林果业生产过程机械化作业大部分尚处于空白阶段。3、 对课题所涉及的任务要求及实现预期目标的可行性分析： 设计要求一种自走连续振动式红枣收获机包括装有行心轮的机架以及分机，还有钳式振动头。其特征是:在机架上放置一个盛果箱，在盛果箱的前端上部安装一根吸气管，所说的风机安装在吸气管上，吸气管的自由端口连接着一个红枣捡拾吸头，在盛果箱后部上方的轻杂物沉降室的下端连接这一个过滤网兜，在盛果箱中部顶端设计一个竖直向下延伸的挡枣部，挡枣部的长度与盛果筐的宽度相同。在挡枣部下方的盛果箱内放置一个盛果筐，盛果筐的前端顶部与盛果箱的前臂之间设计一个倾斜的红枣下滑板。为了实用的方便在风机和红枣捡拾头直接设计一根波纹管。而且盛果筐四周壁上均设置着网孔，挡枣部为平直的挡板，所说的红枣下滑板为栅栏板上的缝隙宽度必须比红枣的直径小。 （1）选择合适配套动力，设计工作装置、收集除杂装置和行走装置。（2）运用AutoCAD绘制二维装配图和零件图。（3）利用Solidworks绘制三维图。4、 本课题需要重点研究的、关键的问题及解决的思路： 本设计需要重点研究的是红枣采摘机械的基本形式、传动机构、采摘的类型。需要充分发挥机械的特性，最大限度的利用机械结构对红枣进行采摘并且保证收获的红枣质量和采净率。机械化收获是整个生产环节里的重要部分，所以研究红枣采摘机的技术具有很重要的意义。 机械振动式红枣收获机工作原理是基于机械振动的果实脱落原理，主要过程是通过操作液压控制阀控制五个液压油缸动作。首先，将果树振摇装置的钳式振动头夹持树干；然后，由拖拉机输出轴PTO传递动力经由液压控制回路到钳式振动头振动；振动头产生的机械振动传递给果树；果枝在接受了外加的强迫振动后，也以一定的频率和振幅振动，从而就使果枝上的果实以某种形式的振动而加速运动；加速运动的物体要受到惯性力的作用，当惯性力大于果实与果枝的结合力时，果实就会掉落。实验结果表明：树干的振动是一种无限多自由度的悬臂梁横向振动，其一阶基频的计算机值和实验值相差在10%左右。 5、 完成本课题所必须的工作条件及解决的办法： 塔里木大学位于南疆中心位置，区域优势明显。新疆生产建设兵团红枣种植面积有90万亩，为设计开展提供了便利的外部条件。塔里木大学有良好的硬件与软件设施，这些都为课题的实施提供了保障。 机械振动式红枣收获机适用于树冠较大，树干直径较粗且操作空间较大的稀疏果园，而且要求枣树长得笔直，不能有太大的弯曲度和根部不能有太多的分叉。设计要注意的是：采摘的方式；采摘后果实如何利于采集；保证对果实的损伤不影响其质量；使用简便的采摘方式；减少对果实的打击；提高采集装置承接的效率；传动装置要配套和采摘机械相互协调。经过查询资料和实际的测量，确定收获机所具备的基本条件是：工作宽度小，易移动，采摘过程对果实的损伤小。经研究采用负压捡拾，正压传送，中间挡枣部前设计成弧形进一步降低红枣与挡枣部进行碰撞时出现的碰撞摩擦力，减少红枣表皮的损伤。红枣挡部也可以设计成一块平直的挡板，可以减少制作的成本。6、 完成本课题的工作方案及进度计划： 第12周 查阅相关文献，撰写开题报告。第34周 根据国内外现有的红枣收获机确定棒杆式红枣收获装置的总体设计方案，绘制总体结构简图。第56周 根据工作要求，计算并查阅相关手册，选择和设计各零部件。第79周 运用AutoCAD软件，绘制二维零件图和装配图。第1011周 运用三维设计软件完成整机各零部件的三维建模并进行运动仿真。第12周 从工艺性能，经济性能，实用性能等方面对产品进行综合评价、校核、修正。第13周 完成设计说明书。第14周 整理材料，准备答辩 工作方案为首先根据工况选择原动力部分即发动机的型号，再通过实地测量枣树之间的行距和间距初步确定机器外廓尺寸，然后根据工作条件设计各部分零、部件，如收获机机架的设计、底板的设计、挡枣部的设计，链式输送带的设计、钳式振动头的设计等等。7、 主要参考文献：(1)机械设计课程设计手册（第4版）吴宗泽、罗圣国等著 高等教育出版社(2)机械设计（第七版）濮良贵、纪名刚等著 高等教育出版社(3)机械原理（第七版）陈作模、葛文杰等著 高等教育出版社(4) 机械设计手册（第二版）上册.化学工业出版社，1983 (5)机械设计手册（第二版）中册.化学工业出版社，1983 （6）机械设计图册化学工业出版社，2002 （7）农业机械学(第二版)农业出版社，1994 学生签名 杨海斌 2015 年 11 月 29 日指导教师审阅意见指导教师签名 年 月 日挡枣板直径400毫米，中心孔10毫米，厚度10毫米，30个扇形；敲击棒直径900毫米，中心孔30毫米，厚度10毫米，12个杆；敲击棒中心杆外直径47毫米，内直径20毫米，长度1700毫米；机架长4000毫米（1300，1200，1500），外宽1300毫米，内宽1100毫米，外高1800毫米，内高1700毫米；轮胎直径1000毫米，宽300毫米；带板外长4000，宽500，高100，内长3900，宽450，高100，板上孔深50，直径20，平带长3450，宽450，厚5,；传送带长1700，宽150，厚5，带挡板长100，宽150，厚5；挡枣板中心轴长100，直径20；驾驶室支撑杆外径100，长1200；机箱长1000，宽1000；果树采摘机器人及控制系统研制摘要机器设备组成的操纵者,效应器和基于图像的视觉伺服控制系统是为了采摘苹果而开发的。5自由度的机械手PRRRP结构几何优化提供准线性行为和简化控制策略。匙形效应器与气动驱动爪为了满足采摘苹果的要求。苹果采摘机器人自主完成收集任务使用一个应用模块。通过使用支持向量机与径向基函数,果实识别算法开发了检测并自动查找苹果在树上。控制系统,包括工业电脑和交流伺服驱动程序,进行了机械手和效应器融合、将苹果摘下来的。原型机器人装置的有效性经实验室测试和现场实验的领域。机器人采摘苹果的成功率是77%,平均采摘时间大约是15秒/苹果。1 序言在中国,农村经济的快速发展和水果种植领域结构的不断调整,如苹果、柑橘和梨自1993年以来达到8 - 9百万公顷,占四分之一世界上的水果种植总面积。然而,水果收集任务,以50%-70%/小时,仍然依靠体力劳动。所以收获将自动化,因为农业人口在中国逐渐减少。此外,由于果树高,采摘工作已经将使用梯子,这使得手工收割十分危险和低效。因此,未来需要对苹果采摘进入机械化和自动化。机械采摘实验在一些地区已经进行了假设模拟收获,但开发这一战略还没有广泛。有选择性的采摘,这是必要的,需要复杂的机器人技术。总之,有必要设计一个与人类感知能力相似的智能机器人。以下这个为实例,这台机器需要检测水果、计算水果的位置,然后选择不破坏果皮或果树，从而进行采摘。研究水果收获机器人发生在1980年代。河村建夫、Namikawa Fujiura,Ura所言(1984)首次开发了一个果园机器人。后来, Rabatel,Pellenc、Journeau Aldon(1987)开发了一个机器人。从那时起,他们关于这方面的知识进行了开创性的研究。此外,一些相关的研究农业机器人在温室进行了。例如,番茄收获,黄瓜收获,樱桃收获,草莓收获。然而,大多数水果收获机器人的文献目前没有进入生产或销售。相反,他们仍然处于研究发展阶段。为此,支持进一步的研究和开发是很重要的,以提高性能和减少这些机器人的初始安装费用。基于上述概念,本研究打算开发和评估竞争低价设备自动收获,即,一个苹果采摘机器人。首先,一个完整的机器人包括组件描述的操纵者,效应器和基于图像的视觉伺服控制的系统描述。其次,机械手的几何优化以获得准线性行为并简化描述的控制策略。第三,气动驱动的结构爪设计来满足采摘苹果的要求。基于这个设计,采摘机器人的自动执行部分使用一个应用模块来检测和采集任务找到苹果在树上,控制系统进行机械手和效应器方法来摘苹果。研究采摘机器人,来有效的验证实验室测试和现场实验测试的领域执行。实验结果是本文的重要贡献。本文组织如下:例如在第二节中,机械手,效应器和基于图像的视觉伺服控制系统。在第三节中讨论实验结果显示的可行性机器人系统的提出。最后,在第四节中总结对机器人未来研究的建议。2 材料和方法21 苹果采摘机器人的机械结构苹果采摘机器人的原型主要考虑的是模型设计效率和成本效益。它主要由自主车,5自由度机械手,效应器,传感器、视觉系统和控制系统组成。自主研发水果采摘的机械结构如图1所示。1.移动车辆 2.水果篮子 3.活动带 4.末端效应器 5.采集单元6. 电动绞盘棒 7.运动小手臂 8.大臂 9.运动大手臂 10.活动的腰部11.腰部 12. 升降平台 13.中心控制系统图1 苹果采摘机器人的原理图2.1.1 自主移动车辆车辆的移动方式为履带式移动。气动泵供应电力，电子硬件数据采集和控制,机械手的效应器切水果。全球定位系统(GPS)技术用于自治导航的移动车辆,其典型的速度1.5m/s。2.1.2 机械手与其他结构相比 ,联合结构是有效的三维位置和方向空间。采摘机器人是一个随机的操作空间分布,可能存在很多障碍机器人。联合操纵与自由的多具有任意曲线拟合功能。因此很容易躲开障碍,操作时相应的关节效应器到达指定的位置。因此,采摘机器人与五自由度机械手 (PRRRP)结构安装在自主移动车辆设计。第一个自由度是用于提出整个机械手。Xo、Xc、Yc为摄像机坐标系的轴。L1,L2,L3为机器人的腰,大臂和手臂。q1、q2为第三关节的腰和手臂。水平垂直。U0为平面坐标轴,中心坐标xg,yg，投影中心用来协调目标,按照目标苹果的图像特性的差异来进行辨别，要采摘的目标就会出现在显示器上。主要的直臂k1,k2武器的控制参数Dd，根据运动度的像素单元就会调整一个合适的角度。交流模拟信号A / D、数字CCD电荷装置，D /数字模拟直流电流。要求自由度GPS全球定位系统目标的的颜色,强度、饱和IBVS基于图像的视觉伺服，PBVS定位视觉伺服PRRRP棱镜的转动 ，从而进行判断。中间三个自由度的旋转,其中,第二个驱动臂设计旋转腰部,第三和第四的转动轴移动终端上下操作符。这个自由度允许效应器朝任意方向移动。最后,手臂的灵活用于伸长, 机器人控制命令会根据实测值的误差达到目标位置,从而实现苹果的采摘。上面的讨论表明,五自由度机械手的设计应足以执行苹果采摘的操作。机械手的机械结构如图2所示。机械手的升降是通过升降平台来进行的,能够应付特殊情况下苹果的采摘。旋转接头和灵活的关节是由伺服电机驱动的。机器人机械手的运动参数和机械结构如表一所示。 图2 机械手的照片2.1.3 实测值的误差效应器机制是由生物学特性来操作目标对象的。苹果采摘机器人的操作对象主要是像苹果一样的球形。匙形效应器(图3所示)根据球面生物学特性而设计的水果,通过切断茎来实现的。图3 末端效应器的图片效应器包含以下部分:钳子用于掌握水果，电动切割装置将苹果从枝桠上切断。效应器的打开和关闭是由一些适合快速行动的气动设备,快速响应特征效应器的开关控制。转移模式实现能量传递的动力是使用压缩气体压力传输。苹果根茎是安装在夹具机制的电动刀切断的。苹果被抓住时,电源通过直流电使得爪刀旋转,切断效应器感应到的目标。2.2 传感器非结构性和不确定的操作特性，个体差异和随机的操作对象的不同,使得苹果采摘机器人应该足够应对复杂的环境。在水果夹紧过程中,水果的生物学特性,包括果皮的薄和脆弱需要效应器的高度掌控。它要求传感器来控制高精度的把握力。此外,手臂的旋转,目标的位置和准确捕获还需要经过传感器来检测和定位。此外,为了避免损坏设备,导致受伤,未能确定水果、避免手臂的碰撞也需要传感器有效的感知操作环境。2.2.1 效应器上的传感器传感器的上的效应器,包括摄像头传感器、位置传感器、碰撞传感器和压力传感器。如图4所示。视觉传感器,它使用高分辨率的电荷耦合装置(CCD)摄像机，采集系统通过串行总线(USB)接口的视频窗口捕获技术形成图像, 在完成图像采集、水果搜索和认可扮演一个重要角色。获得广泛应用。视觉传感器的位置是在一个眼手并用的模式。在图4中可以看出的两对红外双光电位置传感器光电电池。此外, 传感器的开关位置通常是用来限制安装在手臂上的电切刀。手臂开始减速时,效应器通过视觉传感器上传来的采摘对象的图像。手臂停了下来,钳子夹水果当蓄电池的两双都是模糊的。在这一点上,压力和碰撞传感器采用力敏感电阻。当压力爪感到一定的压力传感器,电动切割器旋转和切断花梗。开关位置传感器便会操作电切刀停止工作。碰撞传感器用于采摘过程中躲避障碍物。模拟信号来自力敏感电阻和红外线光电管，在工业计算机与数据采集模块通常是不相容的。因此,他们之间需要调制传输之前数据采集模拟信号。图5显示了传感器信号调制电路。图4 末端传感器的布局 图5 传感器信号的调制电路2.2.2 传感器为避免操作的碰撞控制旋转关节的角度和位置用来控制关节的灵活度。霍尔传感器安装在腰的转动关节,主要负责小手臂关节的两端。在工作环境中,小手臂的运动空间大。其中位置传感器，压力传感器，图像传感器，碰撞传感器图，效应器的传感器。图5 为信号调制电路。2.3 视觉系统苹果采摘机器人视觉系统的关键成分是公认的苹果图像处理方法。它影响机器人的可靠性和直接决定的能力,快速、准确进行水果识别。然而,在早期的研究中,存在一些尚未解决的问题，低准确率和时间消耗等在一定程度上限制了苹果采摘机器人在自然环境中的实时和多任务处理能力。为了克服这些缺点,运用识别视觉系统组成的彩色CCD相机自动获取原始的苹果图像和一个工业计算机处理图像识别定位水果。因为富士苹果在中国是最受欢迎的,我们研究集中这种多样性。识别和定位过程如下。首先,由于自然环境的形象采集设备使用,原始的未经加工的苹果图像不可避免的包含噪声,影响其质量。一个向量中值滤波器应用于图像增强预处理。它不仅可以有效地去除噪声突显出苹果果实的前景,也能保持良好的图像边缘。其次,大多数的苹果采摘在自然图像条件下通常包括树枝和树叶让问题变得更加复杂。只通过传统的形象分割算法,很难达到预期的效果。基于颜色直方图色调的统计,强度饱和模型,图像分割算法用来开发双阈值和区域增长，从复杂的背景识别苹果果实。色度组件是适用于提取轻色调的苹果,这避免了不同的照明水平对图像的影响。这种算法简单,所需的处理时间简短。苹果提取通过不同的特征来确定空间位置,并提供手臂相应的运动参数。对于颜色特征提取,色度组件色调和饱和度,通常与颜色特征提取识别不同。然而,在我们的研究中,苹果果实、树枝和叶有特定形状,及其不同的形状是巨大的。因此,苹果的形状特性对于对象识别是非常重要。形状特征的选择规则基于旋转不变性和规模。考虑到苹果果实图像的特点,圆形的方差,方差椭圆,圆周边比率和正方形区域是用来描述苹果的形状特征轮廓。这四个特征向量与形状特征提取。对应的特征值的计算后,他们作为每个样本的特征向量,用于训练和分类。最后,基于支持向量的分类算法并建立了辨识苹果方法。仿真和实验表明, 基于苹果的颜色特征和形状特征，支持向量机方法和径向基函数(RBF)的内核是对于苹果被发现最好的认可。Research PaperDesign and control of an apple harvesting robotZhao De-An, Lv Jidong, Ji Wei*, Zhang Ying, Chen YuSchool of Electrical and Information Engineering, Jiangsu University, XueFu Road No.301, Zhenjiang, Jiangsu Province 212013, PR Chinaa r t i c l e i n f oArticle history:Received 9 February 2011Received in revised form4 July 2011Accepted 17 July 2011Published online 6 August 2011A robotic device consisting of a manipulator, end-effector and image-based vision servocontrol system was developed for harvesting apple. The manipulator with 5 DOF PRRRPstructure was geometrically optimised to provide quasi-linear behaviour and to simplify thecontrol strategy. The spoon-shaped end-effector with the pneumatic actuated gripper wasdesigned to satisfy the requirements for harvesting apple. The harvesting robot autono-mouslyperformeditsharvestingtaskusingavision-basedmodule.Byusingasupportvectormachine with radial basis function, the fruit recognition algorithm was developed to detectand locate the apple in the trees automatically. The control system, including industrialcomputer and AC servo driver, conducted the manipulator and the end-effector as itapproached and picked the apples. The effectiveness of the prototype robot device wasconfirmed by laboratory tests and field experiments in an open field. The success rate ofappleharvestingwas77%,andtheaverageharvestingtimewasapproximately15sperapple.Crown Copyright 2011 Published by Elsevier Ltd on behalf of IAgrE. All rights reserved.1.IntroductionIn China, with the rapid development of the rural economyand the continuous adjustment of planting structures, fruitcultivation areas, such as apple, citrus and pear, havereached 8-9 million ha since 1993, accounting for one-quarterof the total fruit cultivation area in the world. However, fruitharvesting tasks, which take 50%e70% of the total workinghours, still depend on manual labour (Xu & Zhang, 2004.Harvesting is expected to be automated because the farmingpopulation is gradually decreasing in China. In addition,since the fruit trees are tall, harvesting work has to be con-ducted using step ladders, which makes manual harvestingdangerous and inefficient. Therefore, there is a strong desireto mechanise and automate harvesting. Mechanical har-vestingexperimentshavebeenperformedontheassumptionof once-over harvesting in some areas, but exploitation ofthis strategy is not yet widespread (Hancock, 1999). Selectiveharvesting, which is commonly used, requires sophisticatedrobotic technology. In short, it is necessary to design anintelligent robot with human-like perceptive capabilities. Forinstance, the machine needs to detect fruit, calculate theposition of the fruit and then pick it without damaging thepericarp or the fruit tree.Research on fruit harvesting robots took place in the 1980s.Kawamura, Namikawa, Fujiura, and Ura (1984) first developeda fruit-harvesting robot for orchards. Later, Grand, Rabatel,Pellenc, Journeau, and Aldon (1987), developed an apple-harvesting robot. Since then, their pioneering studies werefollowed by many research papers covering several aspects(e.g., ;Edan, Rogozin, Flash, & Miles, 2000; Foglia & Reina, 2006;Hwang&Kim,2003;Kondo&Ting,1998;Muscato,Prestifilippo, Abbate, & Ivan, 2005; Sakai, Osuka, Maekawa, &Umeda, 2007, 2008; Sarig, 1993; Van Henten, Hemming, VanTuijl, Kornet, Meuleman, 2002). In addition, several relevantstudiesonagriculturalrobotsingreenhouseshavebeencarried* Corresponding author. Tel.: 86 511 82028322; fax: 86 511 82028322.E-mail address: (J. Wei).Available at journal homepage: /locate/issn/15375110biosystems engineering 110 (2011) 112e1221537-5110/$ e see front matter Crown Copyright 2011 Published by Elsevier Ltd on behalf of IAgrE. All rights reserved.doi:10.1016/j.biosystemseng.2011.07.005out; for instance, tomato harvesting (Monta et al., 1998),cucumber harvesting (Van Henten, Van Tuijl, Hemming,Kornet,Bontsema&VanOs,2003),cherryharvesting(Tanigaki, Fujiura, Akase, & Imagawa, 2008), strawberry har-vesting (Hayashi et al., 2010). However, most of the fruit har-vesting robots discussed in the literature are not currentlymanufacturedorsold.Instead,theyremainintheresearchanddevelopment stages. To this end, it is important to supportfurtherresearch anddevelopment toimprove the performanceand reduce the initial set-up costs of these robots.Based on the concepts above, this study intends to developand evaluate a competitive low price device for automaticharvesting, i.e., an apple-harvesting robot. Firstly, a detaileddescription on the components of the robot including themanipulator,theend-effectorandtheimage-basedvisionservocontrol system is described. Secondly, the geometrically opti-misation of the manipulator to gain a quasi-linear behaviourand simplify the control strategy is described. Thirdly, the end-effectorwiththepneumaticactuatedgripperdesignedtosatisfythe requirements for harvesting apple is described. Based onthis design, the harvesting robot autonomously performs itsharvesting task using a vision-based module to detect andlocate the apple in the trees, and control system conducts themanipulator and the end-effector to approach and pick apple.To verify the validity of the developed harvesting robot, thelaboratory tests and field experiments in an open field wereperformed. The experimental results are the important contri-bution of this paper.The paper is organised as follows: in section 2 the maincomponents of the robot are presented in detail, i.e., themanipulator, the end-effector and the image-based visionservo control system, respectively; in section 3 the experi-mental results are discussed to show the feasibility of therobot system proposed; finally, in section 4 conclusions aredrawn and suggestions for future research are made.2.Material and methods2.1.Mechanical structure of apple harvesting robotA prototype model of the apple harvesting robot is designedfor both efficiency and cost effectiveness. It mainly consists ofan autonomous vehicle, a 5 degree of freedom (DOF) manip-ulator, an end-effector, the sensors, the vision system andcontrol system. The mechanical structure of fruit harvestingrobot self-developed in this paper is shown in Fig. .The autonomous mobile vehicleA crawler type mobile platform was selected as the mobilevehicle. It carried the power supplies, pneumatic pump,electronic hardware for data acquisition and control, and themanipulator with the end-effector for cutting the fruit. Globalposition system (GPS) technology was used for autonomousnavigation of the mobile vehicle, whose typical speed was1.5 ms?.The manipulatorCompared with other structures, as described in Sakai,Michihisa, Osuka, and Umeda (2008), joint structure is effec-tive for any position and orientation in three-dimensionalspace. The operation of a harvesting robot is a random largespace distribution, where a lot of obstacles may exist aroundthe robot. A joint manipulator with multi-degrees of freedomhas an arbitrary curve fitting function. It is therefore easy toavoid obstacles by operating the corresponding joints whenthe end-effector reaches the object position. Therefore,aharvestingrobotmanipulatorwith5DOFprismatic-revolute-revolute-revolute-prismatic (PRRRP) structure to bemounted on autonomous mobile vehicle was designed. Thefirst DOF was used for uplifting the whole manipulator. TheNomenclatureSymbolsCR, CRU, CU, CLU, CLAvoidance sensors numberXc, Yc, ZcThe camera coordinates axesXo, Yo, ZoRobot coordinates axesL1, L2, L3Lengths of waist, major arm and minor armq1;q2;q3Joint angles of waist, major arm and minor arm.u, vImage plane coordinates horizontal and verticalaxesuo, voImage centre coordinatexg, ygProjection centre coordinate of target fruitex, eyThe difference of target fruit image featurebetween xg, ygand uo, voM ? NImage plane pixels of video camerajexmaxj;jeymaxj Maximum of ex and eyDq1; Dq2; Dq3Joint deviationangles of waist, major armandminor armk1, k2Control parameters of armsDd The angle to adjust for the movement of a pixel withunit of degree per pixel.AbbreviationsACAlternating CurrentA/DAnalog, DigitalCCDCharge Coupled DevicesD/ADigital, AnalogDCDirect Current.DOFDegree of FreedomGPSGlobal Position SystemHISHue, Intensity, SaturationIBVSImage-Based Vision ServoPBVSPosition-Based Vision ServoPRRRPPrismatic Revolute Revolute Revolute PrismaticRBFRadial Basis FunctionRSTRotation Scale, TranslationSVMSupport Vector MachineUSBUniversal Serial BusVFWVideo for Windowsbiosystems engineering 110 (2011) 112e122113middle three DOF were for rotation, among which, the seconddriving arm was designed to rotate around the waist, and thethird and fourth ones were rotation axes to move the terminaloperator up and down. This DOF allowed the end-effector tomove towards an arbitrary direction in the work space. Thefifth, and last, DOF was flexible and used for elongation, whichmade the end-effector reach the target location according tothe robot control commands, thus achieving the harvesting offruit (Zhao, Zhao, & Ji, 2009; Zhao, Zhao, & Shen, 2009). Thediscussion above shows that 5 DOF manipulator designedshould be sufficient to perform the harvest operation. Themechanical structure of the manipulator is shown in Fig. 2.The lifting of manipulator was performed by the pump-driven lifting platform, which was able to cope with thespecial circumstances of tall fruit crops. The rotary joints andflexible joints were driven by servo motors. Motion parame-ters of the robot manipulator mechanical structure are shownin Table .The end-effectorThe mechanism of end-effector is determined by operationand biological characteristics of the target object. The opera-tion objects of harvesting robot are mainly spherical fruit suchas apple. A spoon-shaped end-effector (shown in Fig. 3) isdesigned according to biological characteristics of sphericalfruit, which are picked by means of cutting off the stalk.The end-effector contained the following parts: a gripper tograsp the fruit and an electric cutting device to separate thefruit from the stalk. The opening and closing of end-effectorgripper was determined by some pneumatic devices, whosequick action, fast response characteristics were suitable forthe switching control of the end-effector. Pressure trans-mission was a transferring mode using compressed gaspressure to achieve energy transference. The apple stalk wassevered by an electric cutter installed in the side of grippermechanism. When the fruit was grasped, the direct current(DC) motors transmited power by flexible wire to drive thecutter rotating around the gripper,cutting off the stalk in frontof end-effector at any position.Fig. 1 e Schematic diagram of the fruit harvesting robot.Fig. 2 e Photograph of the manipulator.biosystems engineering 110 (2011) 112e1221142.2.The sensorsThe non-structural and uncertain features of the operatingenvironment, and the individual differences and randomnature of the operating objects, determines that fruit har-vesting robots should have intelligent sensibility to theircomplex environment (Edan et al., 2000; Zhao, Zhao, & Ji, 2009;Zhao, Zhao, & Shen, 2009). During the process of clamping thefruit, the biological characteristics of fruit including its thinand fragile pericarp put a high demand on grasping force ofend-effector (Monta, 1998). It required sensors to control thegrasping force accurately. In addition, the rotation of arm, itstraveling position and accurate capture also required thesensors to detect and locate fruit (Jiang, Cai, & Liu, 2005; Qiao,Wu, & Zhu, 1999). Furthermore, in order to avoid damagingequipment, causing injury and failing to pick fruit, collisionavoidance of the arm also needs sensors to perceive theoperating environment effectively.2.2.1.The sensors on end-effectorThe layout of sensors on end-effector, which includes a visionsensor, a position sensor, a collision sensor and a pressuresensor, is shown in Fig. 4. The vision sensor, which uses high-pixel colour charge coupled devices (CCD) video camera withuniversal serial bus (USB) interface and the video for windows(VFW) capture technology to form image acquisition system,plays a key role in completing image acquisition, fruit searchand recognition. To obtain a wide visible-field and not influ-encedby end-effector, the position of the vision sensoris in aneye-in-hand mode. In Fig. 4, it can be seen that there is thephotoelectricpositionsensorwithtwo pairsofinfrareddoublephotoelectric cells. In addition, the switch position sensorwhich was usually used to limit for electric cutting knife wasalso mounted on the position sensor. The arm began decel-eration when the end-effector moved towards the target fruitguided by the vision sensor and the first pair of photodiodeswas obscured by the fruit in the holder. The arm stopped andthe gripper clamped fruit when the two pairs of photocellswere obscured. At this point, both the pressure and collisionsensors adoptedforce sensitive resistance. When the pressuresensor on the gripper felt a certain pressure, the electric cutterrotated and cuts off pedicel. The cutter stopped working whenthe switch position sensor operated. The collision sensor wasused for obstacle avoidance during the process of harvesting.Analogue signals derived from the force sensitive resistanceand infrared photoelectric tubes are usually incompatiblewith the data acquisition module inside industrial computer.Therefore, they require modulation before transmission to thedata acquisition module. Fig. 5 shows the sensors signalmodulation circuit.2.2.2.The Sensor on manipulator for collision avoidanceControl of the angle of the rotating joints and position controlof the flexible joints was fulfilled using 8 Hall sensors,installed on the rotation joints of waist, the major arm, theminor arm and both ends of flexible joints. In the workingenvironment, the movement space of minor arm was wide;Fig. 3 e Photograph of the end-effector.PositionSensorPressureSensorVisionSensorCollisionSensorFig. 4 e Layout of sensors on end-effector.Fig. 5 e Sensors signal modulation circuit.Table 1 e Motion parameters of manipulator mechanicalstructure.Joint MotionparametersLift platform0 me0.8 mRotation joint of waist?180?e180?Rotation joint of major arm?80?e80?Rotation joint of minor arm?80?e80?Flexible joint0 me0.8 mbiosystems engineering 110 (2011) 112e122115and the probability of collision with obstacles was high.Therefore, the collision sensor was fixed in the minor arm todetect obstacles. Five groups of micro switches were fixed ondifferent positions in the minor arm to obtain real-timeinformation from obstacles. Noting that software program-ming processes signals conveniently, the five groups ofavoidance sensors were designated CR、CRU、CU、CLU、CLin accordance with their position. The distribution of theminor arm collision avoidance sensors is shown in Fig. 6.2.3.The vision systemsFor the vision system of the apple harvesting robot, the keyingredient was the image processing method that recognisedand located the fruit. It affects the robots dependability andalso determines its ability to directly, quickly and accuratelyrecognise in the fruit real time (Bulanon, Kataoka, & Okamoto,2004). However, in the earlier research (Bulanon, Kataoka, &Ota, 2002; Liu, Zhang, & Yang, 2008; Plebe & Grasso, 2001;Zhao, Yang, & Liu, 2004), there exist some unsolved issuessuch as low accuracy rate and time consumption, which tosome extent restricted the real-time and multitasking abilityof the apple harvesting robot in the natural environment.To overcome these shortcomings, a real-time automaticrecognition vision system consisting of a colour CCD cameraforcapturingoriginalappleimagesandanindustrialcomputer for processing images to recognise and locate thefruit was developed. Since the Fuji apples are the mostpopular in China, our research focused on this variety.The recognition and location procedure is as follows.Firstly, due to the natural environment and the imageacquisition device used, the original unprocessed apple imageinevitably includes noise that influences its quality. A vectormedianfilter wasappliedtoimageenhancementpre-processing. It can not only remove noise effectively andhighlights the apple fruit in foreground, but it also maintainsgood image edges.Secondly, most apple images acquired in the naturalconditionsusuallyincludebranchesandleaveswhichcomplicate matters. By using only a conventional imagesegmentation algorithm, it was difficult to achieve anticipatedeffect. Based on hue histogram statistics from the hue,intensity and saturation (HIS) model, the double thresholdand region growing method was employed to develop animage segmentation algorithm for identifying apple fruitfrom complex background. The chromaticity component isirrelevant when lightness is extracted and this avoided theinfluence of different illumination levels on the images. Thealgorithm was simple, and required little processing time.The apple features were extracted to determine the spatiallocation, and provide corresponding motion parameters forarm. For colour feature extraction, the chroma componentshue and saturation, are usually extracted as colour featuresfor recognition. However, in our study, apple fruit, branchesand leaves havespecific shapes, and their differences in shapeare large. Therefore, the shape feature is important in appleobject recognition. The selected rule of shape features wasbased on invariance in rotation, scale and translation (RST).Taking account of characteristics of apple fruit images,circular variance, variance ellipse, tightness, ratio betweenperimeter and square area were used to describe the outlineshape features of apple. These four feature vectors wereextracted as shape features. After the calculation of the cor-responding eigenvalues, they were used as feature vectors ofeach sample and used for training and classification.Finally, a new classification algorithm based on supportvector machine was constructed to recognise the apple fruit.Simulation and experiment shows that the support vectormachine (SVM) method with radial basis function (RBF) kernelfunction based on both colour features and shape featureswas found to be the best for apple recognition. Details of thealgorithm can be found in Wang, Zhao, Ji, Tu, and Zhang(2009).2.4.The control systemThe hardware structure is shown in Fig. 7. At the centre of thecontrol system was the host computer, which integrates thecontrol interface and all of software modules to control thewhole system. The sensor signal acquisition system andimage acquisition system constituted the input section whichwas used to collect external environment information for theFig. 6 e Layout of sensors on minor arm.Servodrivers14Incrementalphotoelectricencoder14Cutter of theend-ffectorUSB interfacePosition limitedsensorDrive motorfor cutterAirpumpCollision sensorInfrared sensorCCD Vision sensorElectricvalveSignalmodulationcircuitGripper of theend-ffectorAC Servomotors andload joints14Data acquisition moduleHost control computerRS232/RS422convertersFig. 7 e Hardware structure of apple harvesting robotcontrol system.biosystems engineering 110 (2011) 112e122116harvesting robot. The output section included a servo drivenmotor, air pump and end-effector.2.4.1.Host computerA Kintek KP-6420i (Kintek Electronics Co., Ltd., Miaoli Hsien,Taiwan, China) industrial computer with Intel Pentium41.7 GHz processor and 512 M memory was selected as the hostcontrol computer, which was responsible for collecting wholesensor signals, processing images online, calculating theinverse kinematics of manipulator and completing the controlalgorithm. The host computer transmitted instructions to thealternating current (AC) servo driver through a serial port tocontrol the joint motors of waist and arms. HighTek HK-5108(Shenzhen FangXingLiuTong Industrial Co., Ltd., Shenzhen,China) RS-232/RS-422 converters were chosen for serialcommunicationfunctions.Adataacquisitionmoduleinstalled inside host computer was responsible for datacollection from all the sensors except for the vision sensorandoutput control of the electrical cutter. A KPCI-847H (BeijingKeRuiXingYe Technology Co., Ltd., Beijing,China)module with16 channels A/D and D/A converter was used.Fig. 8 e Communication link diagram of the host computer and servo drivers.Fig. 9 e Geometrical relations of manipulator joints.Fig. 10 e Perspective projection diagram of fruit in 3-Dspace.biosystems engineering 110 (2011) 112e1221172.4.2.Servo DriveMechanismThe purpose of motion control was to guarantee that the fruitharvesting robot achieved movement with an arbitrary angleto grab the object fruit accurately and rapidly. Therefore thekey issue was to control the drive motors of each joint. Theclosed-loop servo system used to control rotary joints andflexible joints, consisted of Delta ASDA-AB (Delta Electronics,Inc, Taipei, Taiwan, China) series servo drivers, ECMA (DeltaElectronics, Inc, Taipei, Taiwan, China) series AC servo motorsand incremental photoelectrical encoders, which included theplanetary PH (Hubei Planetary Gearboxes Co., Ltd., Wuhai,China) series reduction gear and the Danaher IDC EC2(DanaherMontion Co.,Ltd., Petaluma, Cal., USA)series preciselinear electric actuators. To improve the safety of operation,an electromagnetic brake was fitted to each motor. The ASDA-AB series servo driver not only has three control modesincluding position control, speed control and torque control,but it also included serial communication functions for RS-485, RS-232 and RS-422. Considering the practical applica-tions of the system, the rotation joints for the waist, majorarm and minor arm employed a position control mode, whilstthe flexible joint of the electric pusher employed a speedcontrol mode. The communication between host computerand servo drivers of all the joint motors employed the RS-422mode. Therefore, the frame computer not only set theparameters and adjusted the gains, but it also monitored theoperating state of the servo driver and alarm conditions. Thecommunication link diagram for the host computer and servodrivers is shown in Fig. .Manipulator control strategyThefruitharvestingrobothadanintegratedsystem,comprisingenvironmentperception,dynamicdecision-making and behaviour control. Motion control was the mostbasic and important ingredient. The robot vision servo controlincluded two methods, a position-based vision servo (PBVS)and image-based vision servo (IBVS) (Lippiello, Siciliano, &Villani, 2007; Mariottini, Oriolo & Prattichizzo, 2007). IBVSwas usually used to control the manipulator according toimage features and separate vision reconstruction problemsfrom robot control. This method simplifies robot control andavoids targets outside the camera visible-field. Thus, it isa commonly usedas a methodforrobot control. Tothis end, inour harvesting robot manipulator control system, the IBVScontrol method was employed to achieve location and thepicking motion for the target fruit.The structural diagram for fruit harvesting robot manipu-lator is shown in Fig. 2, where the installed CCD camera is inan eye-in-hand mode. At the start of the picking process, the(u0, v0)uvexey(xg, yg)OFig. 11 e Location diagram of target fruit in image plane.Fig. 12 e Flowchart of small step transformation algorithm.biosystems engineering 110 (2011) 112e122118flexible joint contracted in the minor arm during the processof searching for target fruit. Therefore, the harvesting robotmanipulator can be regarded as a three-joint robot manipu-lator, and the relationship between camera coordinate systemand robot coordinate system can be obtained according togeometrical relation shown in Fig. 9. The camera coordinatesaxes (Xc, Yc, Zc) parallel to corresponding axes in robot coor-dinates (Xo, Yo, Zo). L1, L2, L3are the lengths of the waist, majorarm and minor arm respectively, and q1;q2;q3are the jointangles of the second, third and fourth DOF.Apples with radius of 40 mm (average radius of the apples)were considered as research objectives. Their projection wasa circle on the image captured by video camera. Perspectiveprojection of a fruit in 3-D space is shown in Fig. 10, andformed in the video camera. Feature information of targetfruit in image plane is shown in Fig. 11. For a two-dimensionalimage captured by a video camera, the origin is a point in theupper right corner. Symbols of u and v denote horizontal andvertical axes respectively. The image feature of target fruit ischaracterize as ex and ey, which are the errors betweenprojection centre coordinate (xg, yg) and image centre coordi-nate (uo, vo). During joint control of harvesting robot manip-ulator, image feature of ex varies along with the change ofwaist joint angle q1, and image feature of ey varies along withthe change of major arm joint angles q2and minor arm jointangles q3.It can be seen that the manipulator with 5 DOF PRRRPmechanical structure was geometrically optimised to simplifythe control strategy, and the control algorithm designed toavoid complicated jacobian operations. At the same time, thevision systems software gave only planar information of thetarget fruit in our robotic system. The distance informationbetween target fruit and camera was unknown. Hence themanipulator jacobiancould not bedirectlyused in our system.The process of picking target fruit can be presented asfollows. Firstly, each module of harvesting robot was ini-tialised, and the manipulator made to approach the fruit treesat a proper location. Then the video obtained image infor-mation of target fruit, and the recognition and location wereobtained by image processing software such that the centroidcoordinate xg, ygof target in image and the errors ex and eyobtained by comparison with the image centre coordinate uoand vo.Secondly, the robot was controlled to move with small stepaccording to the calculated deviations ex and ey, and eventu-ally it drove them to be zero. Assuming that image planepixels of video camera are M ? N, then jexmaxj M=2 andjeymaxj N=2. The flowchart of the small step transformationalgorithm is shown in Fig. 12. When the deviations of thesmall step movements of the waist, major arm and minor armwere zero, then the centroid of target fruit was coincidentwith image centre. During the process of eliminating devia-tions ex and ey, each joint angle was required to move. Thiswas calculated according to Eq. (1)Dq1 ex ? DdDq2 k1? ey ? DdDq3 k2? ey ? Dd(1)where Dq1; Dq2; Dq3are joint angles of waist, major arm andminor arm respectively; k1,k2are the control parameters ofarms; Dd is the angle to be adjusted for the movement ofa pixel with unit of degree per pixel.Then, the host computer sent instructions to the flexiblejoint to spread.Afterthe objectfruit enteredinto thegripper ofend-effector, the flexible joint stopped spreading. The gripperwasthenclosedandtheelectricalcuttercutofftheapplestalk.Finally, the flexible joint backed to its initial position.Thereafter, the gripper was opened and fruit slid along theflexible tube into the basket.To achieve continuous picking the above steps wererepeated.2.4.4.System software designA WindowsXPsystemwas employed as anoperatingplatformfor its good stability and security. Visual C 6.0 was selectedas programming development tool for the host computer. Inthe system, multiply tasks needed to be processed simulta-neously. Noting that a single-thread might lead to datacommunication jams and not guarantee real-time control,a multi-threading event-driven approach was adopted for theprogram control system software. The main thread wasresponsible for the management of visualisation controlinterface, system initialisation; the sub-thread was respon-sible for communication and synchronisation. The sub-threadFig. 13 e Main program flowchart of robot harvesting task.biosystems engineering 110 (2011) 112e122119system involved video capture, motion control, elongationtest of flexible joint and extraction test of prism sub-threads.The main program flowchart for the fruit harvesting robot isshown in Fig. 13.3.Experiment resultsIn this section, the results of a feasibility study of the systemperformed through laboratory tests along with field validationarepresented.Thelaboratoryexperiments wereperformedonthe prototype operating in simulation working conditions.This stage was helpful to set up and optimise the componentsof our system. Finally, the performance of the harvestingrobot was verified in field tests.3.1.Laboratory tests3.1.1.Recognition and Location experimentFor the control system of the fruit harvesting robot, live videowindows on the control softwareinterfacewas usedto displaythe real-time process of picking. Target recognition windowsshowed the accuracy of target recognition, where red “”implied image centre and blue “” implied the centroid of theobject fruit. The position of object fruit could be easily shownin the images. In the target location windows, the track thecentroid of target fruit with regard to image centre during thelocation process was marked with a blue line.Recognition and location test results of object fruit can beseen in Fig. 14. It is obvious that in the figure, accuraterecognition and smooth location track made following fruitgrabbing possible, which verified that the designed robot hasgood tracking performance to meet the requirements ofaccurate real-time recognition and location.During the process of picking operations, video imagesignals needed to be acquired dynamically and continuously,and handled frame by frame. In the video, the size of oneframe of dynamic images was 320 ? 240 pixels. The recogni-tion time for 100 continuous and dynamic images is shown inTable 2.From Table 2, the average recognition time of 100 frameimages was 352 ms. From these results, it was concluded thatthe developed recognition algorithm met the requirements ofreal-timeoperationandthatthesystemcouldbeusedtoguidea robot manipulator as it approached an apple in real-time.3.1.2.Harvesting experimentsA photograph of the fruit harvesting robot operating duringlaboratory simulation harvesting tests is shown in Fig. 15.Under laboratory conditions, apples with radius about 40 mm,were hung on fresh branches in different directions. Theperiod of image acquisition was 100 ms. 100 picking tests werecarried out in 10 different positions.The test results were as follows: successful picking occa-sions 86, and failed occasions 14. Therefore the success ratewas 86%. Without regard to the set-up time, the average timeof picking one apple is 14.3 s. This was high enough to meetTable 2 e Dynamic images recognition time.Image frame12345678910111213141516171819202122232425Recognition time(ms) 235 235 315 390 315 310 390 390 310 390 390 310 390 310 235 315 390 390 310 390 390 390 315 390 395Image frame26272829303132333435363738394041424344454647484950Recognition time(ms) 310 390 390 310 315 390 390 315 310 390 390 310 390 390 310 390 390 310 315 390 315 310 390 390 390Image frame51525354555657585960616263646566676869707172737475Recognition time(ms) 315 390 395 310 390 390 310 315 390 390 315 310 390 390 310 390 390 310 390 390 310 315 390 395 310Image frame767778798081828384858687888990919293949596979899 100Recognition time(ms) 315 390 310 315 390 395 310 390 390 310 390 390 310 315 390 390 310 315 390 390 315 390 390 315 390Fig. 14 e Recognition and location results in laboratory tests.biosystems engineering 110 (2011) 112e122120requirements of continuous harvesting operations. The mainreasons for failure could be attributed to the experimentalenvironment, where the soft foliage and apple vibrationduring operation resulted in a decrease in precision posi-tioning. In addition, occasionally the cutting knife failed to cutthe apple stalk.3.2.Field testsTo further verify the reliability and adaptability of harvestingrobot system, field tests were carried out in the BeijingChangping orchard during October 2009.The recognition result in the orchard is shown in Fig. 16.There 7 apples were well recognised, which indicated that therecognition algorithm could identify apples efficiently. Whereapples are behind branches and leaves and apples cover eachother, the apples cannot be picked directly. Those withoutlabel “” in Fig. 16, would be recognised after picking a certainnumber of apples.In practice, once an image such as that in Fig. 16 wasacquired, the vision system of robot located and picked theapple which had the minimum distance from the imagecentre of the visible-field of the camera. The vision system ofrobot located the next target fruit. Continuously pickingexperiments (shown in Fig. 17) were carried out in an orchardwith a complex environment. In 10 min, 39 apples were rec-ognised, of which 30 apples were picked and put into thecontainer successfully. Six apples failed to be picked sincetheir image was blocked by branches. Three were picked butfell down to the ground due to their small size and the grippernot clamping tightly. After calculation, the mean recognitiontime for picking was 15.4 s and the picking success rate was77%, which indicates that the prototype machine and controlsystem could be used to carry out the picking operationoutdoors.4.Conclusions and future researchA self-developed fruit harvesting robot and its control systemwas developed. The main components of the robot, i.e., themanipulator, the end-effector and the image-based visionservo control system, have been described in detail. Themanipulator was geometrically optimised to gain a quasi-linear behaviour and simplify the control strategy, and theend-effectorwiththepneumaticactuatedgripperwasdesigned to satisfy the requirements for the harvesting ofapples. The harvesting robot autonomously performed itsharvesting task using a vision-based module to detect andlocalise the apple in the trees, and control system regulatedthe manipulator and the end-effector to approach and pickthe apples. The validity of systems was confirmed by per-forming laboratory tests and field experiments in an openfield.Future research needs to be focused on the following threeaspects for practicability and the commercialisation of therobot: (1) optimisation of the existing software programs andalgorithmstoreducecomputation.Atthesametime,improving the speed and accuracy of picking for the blockedor swinging fruits thereby increasing the practicallity of therobot; (2) considering the complexity and unknown nature ofworking environment, the further research should be focusedon real-time obstacle avoidance, improving picking successrate and harvesting efficiency; (3) improving the mechanicalFig. 16 e Apple recognition results in an orchard.Fig. 17 e Harvesting experiments in an orchard.Fig. 15 e Harvesting experiments in laboratory tests.biosystems engineering 110 (2011) 112e122121structure of the robot to enhance the configuration. Forexample, by replacing the manipulator and end-effector withdifferent systems or using different freedom degrees to pickfruit with different shapes and sizes, to achieve all-purposesof the robot. This would increase its versatility, lower itsoverall cost, and promote its commercialisation.AcknowledgementsThis work was supported in part by Research Fund for theDoctoral Program of Higher Education of China under Grant20093227120013,inpartbyNationalHighTechnologyResearch and Development Program of China under Grant2006AA10Z254, in part by a project funded by the PriorityAcademic Program Development of Jiangsu Higher EducationInstitutions, and in part by Innovate Foundation for GraduateStudent of JiangSu Province under Grant CXZZ11_0573.r e f e r e n c e sBulanon, D. M.,