外文翻译--轮式移动机器人的导航与控制
机械外文翻译外文文献英文文献机械臂动力学与控制的研究
外文出处：Ellekilde, L. -., & Christensen, H. I. (2009). Control of mobile manipulator using the dynamical systems approach. Robotics and Automation, Icra 09, IEEE International Conference on (pp.1370 - 1376). IEEE.机械臂动力学与控制的研究拉斯彼得Ellekilde摘要操作器和移动平台的组合提供了一种可用于广泛应用程序高效灵活的操作系统,特别是在服务性机器人领域。
在机械臂众多挑战中其中之一是确保机器人在潜在的动态环境中安全工作控制系统的设计。
在本文中,我们将介绍移动机械臂用动力学系统方法被控制的使用方法。
该方法是一种二级方法, 是使用竞争动力学对于统筹协调优化移动平台以及较低层次的融合避障和目标捕获行为的方法。
I介绍在过去的几十年里大多数机器人的研究主要关注在移动平台或操作系统，并且在这两个领域取得了许多可喜的成绩。
今天的新挑战之一是将这两个领域组合在一起形成具有高效移动和有能力操作环境的系统。
特别是服务性机器人将会在这一方面系统需求的增加。
大多数西方国家的人口统计数量显示需要照顾的老人在不断增加,尽管将有很少的工作实际的支持他们。
这就需要增强服务业的自动化程度，因此机器人能够在室内动态环境中安全的工作是最基本的。
图、1 一台由赛格威RMP200和轻重量型库卡机器人组成的平台这项工作平台用于如图1所示,是由一个Segway与一家机器人制造商制造的RMP200轻机器人。
其有一个相对较小的轨迹和高机动性能的平台使它适应在室内环境移动。
库卡工业机器人具有较长的长臂和高有效载荷比自身的重量,从而使其适合移动操作。
当控制移动机械臂系统时,有一个选择是是否考虑一个或两个系统的实体。
在参考文献[1]和[2]中是根据雅可比理论将机械手末端和移动平台结合在一起形成一个单一的控制系统。
模糊控制的移动机器人的外文翻译.doc
1998年的IEEE国际会议上机器人及自动化Leuven ，比利时1998年5月一种实用的办法--带拖车移动机器人的反馈控制F. Lamiraux and J.P. Laumond拉斯，法国国家科学研究中心法国图卢兹{florent ,jpl}@laas.fr摘要本文提出了一种有效的方法来控制带拖车移动机器人。
轨迹跟踪和路径跟踪这两个问题已经得到解决。
接下来的问题是解决迭代轨迹跟踪。
并且把扰动考虑到路径跟踪内。
移动机器人Hilare的实验结果说明了我们方法的有效性。
1引言过去的8年，人们对非完整系统的运动控制做了大量的工作。
布洛基[2]提出了关于这种系统的一项具有挑战性的任务，配置的稳定性，证明它不能由一个简单的连续状态反馈。
作为替代办法随时间变化的反馈[10,4,11,13,14,15,18]或间断反馈[3]也随之被提出。
从[5] 移动机器人的运动控制的一项调查可以看到。
另一方面，非完整系统的轨迹跟踪不符合布洛基的条件，从而使其这一个任务更为轻松。
许多著作也已经给出了移动机器人的特殊情况的这一问题[6,7,8,12,16]。
所有这些控制律都是工作在相同的假设下：系统的演变是完全已知和没有扰动使得系统偏离其轨迹。
很少有文章在处理移动机器人的控制时考虑到扰动的运动学方程。
但是[1]提出了一种有关稳定汽车的配置，有效的矢量控制扰动领域，并且建立在迭代轨迹跟踪的基础上。
存在的障碍使得达到规定路径的任务变得更加困难，因此在执行任务的任何动作之前都需要有一个路径规划。
在本文中，我们在迭代轨迹跟踪的基础上提出了一个健全的方案，使得带拖车的机器人按照规定路径行走。
该轨迹计算由规划的议案所描述[17] ，从而避免已经提交了输入的障碍物。
在下面，我们将不会给出任何有关规划的发展，我们提及这个参考的细节。
而且，我们认为，在某一特定轨迹的执行屈服于扰动。
我们选择的这些扰动模型是非常简单，非常一般。
它存在一些共同点[1]。
翻译-全向轮移动机器人的设计和控制
全向轮移动机器人的设计和控制050308225 Alex.Wang摘要这篇论文介绍一个全向移动机器人作为教育学习。
由于它的全向轮设计，这种机器人拥有有各个方向移动的能力。
这篇论文主要提供了一些关于常用的和特殊的车轮设计，以及全向轮机械设计方面和电子控制方法：远程控制、自动导航寻迹和自动控制的方法。
1、引言移动机器人在工业和技术方面应用的重要性正在日益的增加，在无人监控值守、检查作业、运输运送领域已经得到了广泛的应用。
一个更加紧俏的市场是移动娱乐机器人的开发。
作为一个全自动的移动机器人，其中一个主要的应用需求是它的空间移动能力，同时能够避免障碍物并且发现去下一站的路径。
为了能实现这种任务，能够引导机器人移动的功能如定位、导航必须为机器人提供他当前位置信息，这就意味着，它要借助于多个传感器，外部的状态参考和算法。
为实现移动机器人能够在狭窄的区域移动并且避开障碍物，必须具备良好的移动性能并得到正确而巧妙的引导，这些能力主要取决于车轮的设计。
关于这方面的研究正在持续不断的进行，以改善移动机器人系统的自动导航能力。
本篇论文介绍一种全方向的移动机器人作为教育之用。
采用特殊的Mecanum轮设计，使这种机器人拥有全部方向的移动能力。
论文目前提供一些关于传统的和特殊的车轮设计、机械结构设计以及电路和控制方法、远程遥控、线性跟踪（LINE FOLLOW）、自动控制方面的信息。
由于这种机器人的移动能力和它各种控制方法的多样选择性，本章中讨论的机器人可以作为一个非常有趣的教育性平台。
这篇论文是一项在Robotics Laboratory of the Mechanical Engineering Faculty, ”Gh. Asachi” Iasi理工大学研究成果的总结报告。
2、全方向移动能力“全方向”这个术语是用来描述一个系统在任意的环境结构中立刻向某一方向移动的能力。
机器人型运动装置通常是为在平坦的平面上移动而设计的，运行在仓库地面、路面、LAKE、桌面等。
移动机器人的导航与运动控制算法研究
移动机器人的导航与运动控制算法研究随着科技的快速发展，移动机器人已经成为现实生活中的一部分。
移动机器人的导航与运动控制算法的研究，对于实现机器人智能化、自主化以及高效性具有重要意义。
本文将对移动机器人导航与运动控制算法的研究进行探讨，并介绍目前主流的几种算法。
移动机器人的导航算法主要包括路径规划、环境感知和定位。
路径规划是机器人从当前位置到目标位置的路径选择，环境感知则是机器人通过传感器获取周围环境信息，以便更好地进行路径规划和避障，而定位则是机器人获取自身位置信息的过程。
在路径规划方面，A*算法是一种常用的搜索算法，它通过建立搜索树来找到最短路径。
A*算法的核心思想是同时考虑启发式函数和实际代价函数，以选择最佳路径。
此外，Dijkstra算法和D*算法也常用于路径规划。
Dijkstra算法通过计算节点之间的最短距离来确定路径，而D*算法则是在遇到环境变化时，可以通过增量式的方式进行路径更新。
在环境感知方面，移动机器人通常会配备各种传感器，如摄像头、激光雷达和超声波传感器等。
这些传感器可以帮助机器人感知周围的障碍物、地图等环境信息。
通过对环境信息的获取和处理，机器人可以根据目标位置和现实环境进行综合考虑，以便找到最佳路径。
定位是移动机器人导航算法的重要一环。
目前常用的定位方法包括惯性导航系统（INS）、全局定位系统（GPS）和视觉定位等。
INS通过测量机器人的线性加速度和角速度来估计其位置和姿态，而GPS则是通过接收卫星信号来获取机器人的经纬度信息。
视觉定位则是利用摄像头获取环境图像，通过图像处理和特征匹配来确定机器人的位置。
在运动控制方面，控制算法的设计主要涉及机器人的轨迹跟踪和姿态控制。
轨迹跟踪是指机器人按照指定的路径进行运动，并通过不断调整控制参数，使机器人能够更好地跟踪预定轨迹。
姿态控制则是指机器人根据期望姿态和当前实际姿态之间的差距，通过控制器进行调整，以使机器人能够保持稳定。
常见的轨迹跟踪算法包括PID控制、模糊控制和神经网络控制等。
轮式机器人的路径规划与控制技术研究
轮式机器人的路径规划与控制技术研究随着科技的不断进步，轮式机器人已经成为了人工智能领域中的重要组成部分。
轮式机器人可广泛应用于各种环境下，包括室内、室外、平地、山地、水下等多种环境，使其具有广泛的应用前景。
但是，要让轮式机器人能够在复杂的环境下进行准确的路径规划并执行动作，需要借助于强大的技术支持。
本文将主要介绍轮式机器人的路径规划与控制技术研究。
一、路径规划技术路径规划是一项基本但十分关键的技术，它需要根据机器人所处的环境及任务需求，选择适当的路径来实现任务。
对于轮式机器人，我们通常采用三种不同的技术来完成路径规划：传统的基于轨迹的技术、图形化的技术以及基于学习的强化学习技术。
1. 基于轨迹的路径规划基于轨迹的路径规划是一种较为传统且较为简单的路径规划方式，适用于较为简单的环境。
该方法通过计算机模拟机器人的运动轨迹，进而进行路径规划。
这种方法的优点是计算速度较快，适用于较为简单的机器人应用场合。
但是该方法在复杂环境下的精度会受到很大的影响。
2. 图形化的路径规划图形化的路径规划方法是一种基于图形化交互的路径规划技术。
这种方法主要利用计算机程序来模拟出机器人及其周围的环境，通过交互式屏幕及热键的控制来对机器人进行路径规划。
相对于传统的基于轨迹的路径规划方法，该方法克服了精度不够高的问题，具有更好的精度和适用性。
但是该方法需要进行大量的手动操作，并且需要较高的人机交互能力。
3. 基于学习的强化学习技术基于学习的强化学习技术是一种先进而全新的路径规划技术，该技术运用了神经网络的方法，对机器人进行实时学习，使其能够适应更加复杂的环境，并识别出各种条件下的最佳路径。
该方法不仅可以减少规划过程的工作量，而且还能够自动对机器人进行学习和优化，大大提高了机器人的工作效率和速度。
但是由于该方法需要高度的计算能力和运算时间，所以目前还不引导广泛使用。
二、控制技术控制技术是机器人完成任务的基本技术之一，对于轮式机器人这样的移动式机器人，准确的控制其运动轨迹是十分重要的。
机器人外文翻译(文献翻译-中英文翻译)
外文翻译外文资料：RobotsFirst, I explain the background robots, robot technology development. It should be said it is a common scientific and technological development of a comprehensive results, for the socio-economic development of a significant impact on a science and technology. It attributed the development of all countries in the Second World War to strengthen the economic input on strengthening the country's economic development. But they also demand the development of the productive forces the inevitable result of human development itself is the inevitable result then with the development of humanity, people constantly discuss the natural process, in understanding and reconstructing the natural process, people need to be able to liberate a slave. So this is the slave people to be able to replace the complex and engaged in heavy manual labor, People do not realize right up to the world's understanding and transformation of this technology as well as people in the development process of an objective need. Robots are three stages of development, in other words, we are accustomed to regarding robots are divided into three categories. is a first-generation robots, also known as teach-type robot, it is through a computer, to control over one of a mechanical degrees of freedom Through teaching and information stored procedures, working hours to read out information, and then issued a directive so the robot can repeat according to the people at that time said the results show this kind of movement again, For example, the car spot welding robots, only to put this spot welding process, after teaching, and it is always a repeat of a work It has the external environment is no perception that the force manipulation of the size of the work piece there does not exist, welding 0S It does not know, then this fact from the first generation robot, it will exist this shortcoming, it in the 20th century, the late 1970s, people started to study the second-generation robot, called Robot with the feeling that This feeling with the robot is similar in function of a certain feeling, forinstance, force and touch, slipping, visual, hearing and who is analogous to that with all kinds of feelings, say in a robot grasping objects, In fact, it can be the size of feeling out, it can through visual, to be able to feel and identify its shape, size, color Grasping an egg, it adopted a acumen, aware of its power and the size of the slide. Third-generation robots, we were a robotics ideal pursued by the most advanced stage, called intelligent robots, So long as tell it what to do, not how to tell it to do, it will be able to complete the campaign, thinking and perception of this man-machine communication function and function Well, this current development or relative is in a smart part of the concept and meaning But the real significance of the integrity of this intelligent robot did not actually exist, but as we continued the development of science and technology, the concept of intelligent increasingly rich, it grows ever wider connotations.Now, I would like to briefly outline some of the industrial robot situation. So far, the industrial robot is the most mature and widely used category of a robot, now the world's total sales of 1.1 million Taiwan, which is the 1999 statistics, however, 1.1 million in Taiwan have been using the equipment is 75 million, this volume is not small. Overall, the Japanese industrial robots in this one, is the first of the robots to become the Kingdom, the United States have developed rapidly. Newly installed in several areas of Taiwan, which already exceeds Japan, China has only just begun to enter the stage of industrialization, has developed a variety of industrial robot prototype and small batch has been used in production.Spot welding robot is the auto production line, improve production efficiency and raise the quality of welding car, reduce the labor intensity of a robot. It is characterized by two pairs of robots for spot welding of steel plate, bearing a great need for the welding tongs, general in dozens of kilograms or more, then its speed in meters per second a 5-2 meter of such high-speed movement. So it is generally five to six degrees of freedom, load 30 to 120 kilograms, the great space, probably expected that the work of a spherical space, a high velocity, the concept of freedom, that is to say, Movement is relatively independent of the number of components, the equivalent of our body, waist is a rotary degree of freedom We have to be able to hold his arm, Arm can be bent, then this three degrees of freedom, Meanwhile there is a wristposture adjustment to the use of the three autonomy, the general robot has six degrees of freedom. We will be able to space the three locations, three postures, the robot fully achieved, and of course we have less than six degrees of freedom. Have more than six degrees of freedom robot, in different occasions the need to configure.The second category of service robots, with the development of industrialization, especially in the past decade, Robot development in the areas of application are continuously expanding, and now a very important characteristic, as we all know, Robot has gradually shifted from manufacturing to non-manufacturing and service industries, we are talking about the car manufacturer belonging to the manufacturing industry, However, the services sector including cleaning, refueling, rescue, rescue, relief, etc. These belong to the non-manufacturing industries and service industries, so here is compared with the industrial robot, it is a very important difference. It is primarily a mobile platform, it can move to sports, there are some arms operate, also installed some as a force sensor and visual sensors, ultrasonic ranging sensors, etc. It’s surrounding environment for the conduct of identification, to determine its campaign to complete some work, this is service robot’s one of the basic characteristics.For example, domestic robot is mainly embodied in the example of some of the carpets and flooring it to the regular cleaning and vacuuming. The robot it is very meaningful, it has sensors, it can furniture and people can identify, It automatically according to a law put to the ground under the road all cleaned up. This is also the home of some robot performance.The medical robots, nearly five years of relatively rapid development of new application areas. If people in the course of an operation, doctors surgery, is a fatigue, and the other manually operated accuracy is limited. Some universities in Germany, which, facing the spine, lumbar disc disease, the identification, can automatically use the robot-aided positioning, operation and surgery Like the United States have been more than 1,000 cases of human eyeball robot surgery, the robot, also including remote-controlled approach, the right of such gastrointestinal surgery, we see on the television inside. a manipulator, about the thickness fingers such a manipulator, inserted through the abdominal viscera, people on the screen operating the machines hand, it also used the method of laser lesion laser treatment, this is the case, peoplewould not have a very big damage to the human body.In reality, this right as a human liberation is a very good robots, medical robots it is very complex, while it is fully automated to complete all the work, there are difficulties, and generally are people to participate. This is America, the development of such a surgery Lin Bai an example, through the screen, through a remote control operator to control another manipulator, through the realization of the right abdominal surgery A few years ago our country the exhibition, the United States has been successful in achieving the right to the heart valve surgery and bypass surgery. This robot has in the area, caused a great sensation, but also, AESOP's surgical robot, In fact, it through some equipment to some of the lesions inspections, through a manipulator can be achieved on some parts of the operation Also including remotely operated manipulator, and many doctors are able to participate in the robot under surgery Robot doctor to include doctors with pliers, tweezers or a knife to replace the nurses, while lighting automatically to the doctor's movements linked, the doctor hands off, lighting went off, This is very good, a doctor's assistant.Robot is mankind's right-hand man; friendly coexistence can be a reliable friend. In future, we will see and there will be a robot space inside, as a mutual aide and friend. Robots will create the jobs issue. We believe that there would not be a "robot appointment of workers being laid off" situation, because people with the development of society, In fact the people from the heavy physical and dangerous environment liberated, so that people have a better position to work, to create a better spiritual wealth and cultural wealth.译文资料：机器人首先我介绍一下机器人产生的背景，机器人技术的发展，它应该说是一个科学技术发展共同的一个综合性的结果，同时，为社会经济发展产生了一个重大影响的一门科学技术，它的发展归功于在第二次世界大战中各国加强了经济的投入，就加强了本国的经济的发展。
基于ROS的轮式机器人定位与导航方法研究
基于ROS的轮式机器人定位与导航方法研究摘要：本文介绍了ROS轮式机器人系统在未知环境下的自主移动所依赖的传感器。
对可视化地图、栅格地图、拓扑地图、特征地图的特性进行了分析，选择了构建简单便于及时更新的栅格地图模型。
通过对导航所需要的5个步骤实现了轮式机器人在仿真环境下定位与导航的功能。
关键词：ROS;轮式机器人；定位；导航Robot Operating System简称ROS，是一种依赖于Linux内核的开源元操作系统。
ROS含有丰富的组件化工具包以及大量的工具、协议，来简化我们对机器人的控制，从而大大的提高了研发效率。
本文基于ROS研究轮式机器人的定位与导航方法。
首先，进行传感器选型和地图选择，配置机器人运行环境。
然后，通过SLAM建图拼接形成完整的地图环境。
最后，通过控制机器人的移动速度和方向实现轮式机器人的定位与导航功能。
1 传感器选型轮式机器人系统中实现自主移动，必须要考虑未知的环境特征。
在设计机器人系统时，能够提取环境信息的传感器必不可少。
这里对能够实现导航与定位目标的常用传感器进行描述[1]。
激光雷达：根据发射维数的不同来实现对不同待测目标的距离测量。
如一维激光雷达用于测量单向距离，三维激光雷达用于测量空间上物体距离。
线束数量的多少也作为激光雷达被选用时的参考依据，普通机器人通常使用线束较少的雷达。
激光雷达的优点在于其响应快、数据量小，缺点是成本较高。
里程计：常见的是编码器，用来控制机器人所处的位置或被电机所驱动的关节。
机器人移动时，车轮的旋转会触发编码器测量轮毂转数。
当测出车轮半径时，就可以计算出机器人某段时间内的移动距离和瞬时速度。
相机：在机器人SLAM技术中用到的相机根据摄像头个数有单目相机、双目相机之分；普通单目相机无法完成静态下测量距离的目标，双目相机虽然弥补了这一缺点，但是双目相机进行自身标定时颇为复杂，数据处理也比较困难。
而深度相机不仅可以提供彩色图像，还能获取单一像素的深度信息，目前室内机器人主要采用深度相机方案进行信息获取。
移动机器人路径规划和导航(英文)
Autonomous Mobile Robots, Chapter 6
6.2.1
Road-Map Path Planning: Voronoi Diagram
• Easy executable: Maximize the sensor readings • Works also for map-building: Move on the Voronoi edges
© R. Siegwart, I. Nourbakhsh
Autonomous Mobile Robots, Chapter 6
6.2.1
Road-Map Path Planning: Adaptive Cell Decomposition
© R. Siegwart, I. Nourbakhsh
Autonomous Mobile Robots, Chapter 6
© R. Siegwart, I. Nourbakhsh
Autonomous Mobile Robots, Chapter 6
6.2.1
Road-Map Path Planning: Voronoi, Sysquake Demo
© R. Siegwart, I. Nourbakhsh
Autonomous Mobile Robots, Chapter 6
Ø Topological or metric or a mixture between both.
• First step:
Ø Representation of the environment by a road-map (graph), cells or a potential field. The resulting discrete locations or cells allow then to use standard planning algorithms.
人形机器人中英文对照外文翻译文献
中英文对照翻译最小化传感级别不确定性联合策略的机械手控制摘要：人形机器人的应用应该要求机器人的行为和举止表现得象人。
下面的决定和控制自己在很大程度上的不确定性并存在于获取信息感觉器官的非结构化动态环境中的软件计算方法人一样能想得到。
在机器人领域，关键问题之一是在感官数据中提取有用的知识，然后对信息以及感觉的不确定性划分为各个层次。
本文提出了一种基于广义融合杂交分类（人工神经网络的力量，论坛渔业局）已制定和申请验证的生成合成数据观测模型，以及从实际硬件机器人。
选择这个融合，主要的目标是根据内部（联合传感器）和外部（ Vision 摄像头）感觉信息最大限度地减少不确定性机器人操纵的任务。
目前已被广泛有效的一种方法论就是研究专门配置5个自由度的实验室机器人和模型模拟视觉控制的机械手。
在最近调查的主要不确定性的处理方法包括加权参数选择（几何融合），并指出经过训练在标准操纵机器人控制器的设计的神经网络是无法使用的。
这些方法在混合配置，大大减少了更快和更精确不同级别的机械手控制的不确定性，这中方法已经通过了严格的模拟仿真和试验。
关键词：传感器融合，频分双工，游离脂肪酸，人工神经网络，软计算，机械手，可重复性，准确性，协方差矩阵，不确定性，不确定性椭球。
1 引言各种各样的机器人的应用（工业，军事，科学，医药，社会福利，家庭和娱乐）已涌现了越来越多产品，它们操作范围大并呢那个在非结构化环境中运行 [ 3,12,15]。
在大多数情况下，如何认识环境正在发生变化且每个瞬间最优控制机器人的动作是至关重要的。
移动机器人也基本上都有定位和操作非常大的非结构化的动态环境和处理重大的不确定性的能力[ 1,9,19 ]。
每当机器人操作在随意性自然环境时，在给定的工作将做完的条件下总是存在着某种程度的不确定性。
这些条件可能，有时不同当给定的操作正在执行的时候。
导致这种不确定性的主要的原因是来自机器人的运动参数和各种确定任务信息的差异所引起的。
轮式移动机器人动力学建模与运动控制技术
WMR具有结构简单、控制方便、运动灵活、维护容易等优点，但也存在一些局限性，如对环境的适应性、运动稳定性、导航精度等方面的问题。
轮式移动机器人的定义与特点特点定义军事应用用于生产线上的物料运输、仓库管理等，也可用于执行一些危险或者高强度任务，如核辐射环境下的作业。
工业应用医疗应用第一代WMR第二代WMR第三代WMRLagrange方程控制理论牛顿-Euler方程动力学建模的基本原理车轮模型机器人模型控制系统模型030201轮式移动机器人的动力学模型仿真环境模型验证性能评估动力学模型的仿真与分析开环控制开环控制是指没有反馈环节的控制，通过输入控制信号直接驱动机器人运动。
反馈控制理论反馈控制理论是运动控制的基本原理，通过比较期望输出与实际输出之间的误差，调整控制输入以减小误差。
闭环控制闭环控制是指具有反馈环节的控制，通过比较实际输出与期望输出的误差，调整控制输入以减小误差。
运动控制的基本原理PID控制算法模糊控制算法神经网络控制算法轮式移动机器人的运动控制算法1 2 3硬件实现软件实现优化算法运动控制的实现与优化路径规划的基本原理路径规划的基本概念路径规划的分类路径规划的基本步骤轮式移动机器人的路径规划方法基于规则的路径规划方法基于规则的路径规划方法是一种常见的路径规划方法，它根据预先设定的规则来寻找路径。
其中比较常用的有A*算法和Dijkstra算法等。
这些算法都具有较高的效率和可靠性，但是需要预先设定规则，对于复杂的环境适应性较差。
基于学习的路径规划方法基于学习的路径规划方法是一种通过学习来寻找最优路径的方法。
它通过对大量的数据进行学习，从中提取出有用的特征，并利用这些特征来寻找最优的路径。
其中比较常用的有强化学习、深度学习等。
这些算法具有较高的自适应性，但是对于大规模的环境和复杂的环境适应性较差。
基于决策树的路径规划方法基于强化学习的路径规划方法决策算法在轮式移动机器人中的应用03姿态与平衡控制01传感器融合技术02障碍物识别与避障地图构建与定位通过SLAM（同时定位与地图构建）技术构建环境地图，实现精准定位。
人形机器人论文中英文资料对照外文翻译
人形机器人论文中英文资料对照外文翻译| |在获取信息和感觉器官的非结构化动态环境中，后续的决策和对自身不确定性的控制在很大程度上共存。
软件计算方法也可以被人们想象出来。
在机器人领域，关键问题之一是从感觉数据中提取有用的知识，然后将信息和感觉的不确定性分成不同的层次本文提出了一种基于广义融合混合分类(人工神经网络的力量，论坛渔业局)的生成合成数据的观察模型，该模型已经制定并应用于验证，以及一种从实际硬件机器人生成合成数据的模型当选择这种融合时，主要目标是根据内部(关节传感器)和外部(视觉摄像机)的感觉信息最小化机器人操作的不确定性任务目前，一种被广泛有效使用的方法是研究具有5个自由度的实验室机器人和具有模型模拟视觉控制的机械手。
最近研究的处理不确定性的主要方法包括选择加权参数(几何融合)，并且指出在标准机械手控制器设计中训练的神经网络是不可用的。
这些方法大大降低了机械手控制的不确定性，在不同层次的混合配置中更快更准确。
这些方法通过了严格的模拟和实验。
关键词:传感器融合、频分双工、游离脂肪酸、人工神经网络、软计算、操纵器、重复性、准确性、协方差矩阵、不确定性、不确定性椭球1简介越来越多的产品出现在各种机器人的应用中(工业、军事、科学、医学、社会福利、家庭和娱乐)。
它们在广泛的范围内运行，哪一个在非结构化环境中运行在大多数情况下，了解环境是如何变化的以及如何在每一瞬间最佳地控制机器人的动作是非常重要的。
移动机器人基本上也有能力定位和操作非常大的非结构化动态环境，并处理重大的不确定性。
对于机器人运动的最佳控制来说，了解周围环境在每一瞬间的变化是至关重要的。
移动机器人本质上还必须在非常大的未成熟的动态环境中导航和操作，并处理显著的不确定性。
当机器人在自然的不确定环境中工作时，给定工作的完成条件总是存在一定程度的不确定性。
在执行给定的操作时，这些条件有时会发生变化。
导致不确定性的主要原因是机器人运动参数和各种任务定义信息中出现的差异。
【机械专业英文文献】导航的轮式移动机器人的控制
附件2：外文原文（复印件）Navigation and Control of a Wheeled Mobile RobotAbstract:Several approaches for incorporating navigation function approach into different controllers are developed in this paper for task execution by a nonholonomic system (e.g., a wheeled mobile robot) in the presence of known obstacles. The first approach is a path planning-based control with planning a desired path based on a 3-dimensional position and orientation information. A navigation-like function yields a path from an initial configuration inside the free configuration space of the mobile robot to a goal configuration. A differentiable, oscillator-based controller is then used to enable the mobile robot to follow the path and stop at the goal position. A second approach is developed for a navigation function that is constructed using 2-dimensional position information. A differentiable controller is proposed based on this navigation function that yields asymptotic convergence. Simulation results are provided to illustrate the performance of the second approach.1 IntroductionNumerous researchers have proposed algorithms to address the motion control problem associated with robotic task execution in an obstacle cluttered environment. A comprehensive summary of techniques that address the classic geometric problem of constructing a collision-free path and traditional path planning algorithms is provided in Section 9, .Literature Landmarks of Chapter 1 of [19]. Since the pioneering work by Khatib in [13], it is clear that the construction and use of potentialfunctions has continued to be one of the mainstream approaches to robotic task execution among known obstacles. In short, potential functions produce a repulsive potential field around the robot workspace boundary and obstacles and an attractive potential Þeld at the goal configuration. A comprehensive overview of research directed at potential functions is provided in [19]. One of criticisms of the potential function approach is that local minima can occur that can cause the robot to get stuck without reaching the goal position. Several researchers have proposed approaches to address the local minima issue (e.g., see [2],[3], [5], [14], [25]). One approach to address the local minima issue was provided by Koditschek in [16] for holonomic systems (see also [17] and [22]) that is based on a special kind of potential function, coined a navigation function, that has a refined mathematical structure which guarantees a unique minimum exists. By leveraging from previous results directed at classic (holonomic) systems, more recent research has focused on the development of potential function-based approaches for more challenging nonholonomic systems (e.g., wheeled mobile robots (WMRs)). For example, Laumond et al. [18] used a geometric path planner to generate a collision-free path that ignores the nonholonomic constraints of a WMR, and then divided the geometric path into smaller paths that satisfy the nonholonomic constraints, and then applied an optimization routine to reduce the path length. In [10] and [11], Guldner et al. use discontinuous, sliding mode controllers to force the position of a WMR to track the negative gradient of a potential function and to force the orientation to align with the negative gradient. In [1], [15], and [21], continuous potential field-based controllers are developed to also ensure position tracking of the negative gradient of a potential function, and orientation tracking of the negative gradient. More recently, Ge and Cui present a new repulsive potential function approach in [9] to address thecase when the goal is non-reachable with obstacles nearby (GNRON). In [23] and [24], Tanner et al. exploit the navigation function research of [22] along with a dipolar potential field concept to develop a navigation function-based controller for a nonholonomic mobile manipulator. Specifically, the results in [23] and [24] use a discontinuous controller to track the negative gradient of the navigation function, where a nonsmooth dipolar potential field causes the WMR to turn in place at the goal position to align with a desired orientation. In this paper, two different methods are proposed to achieve a navigation objective for a nonholonomic system. In the first approach, a 3-dimensional (3D) navigation-like function-based desired trajectory is generated that is proven to ultimately approach to the goal position and orientation that is a unique minimum over the WMR free configuration space. A continuous control structure is then utilized that enables the WMR to follow the path and stop at the goal position and orientation set point (i.e., the controller solves the unified tracking and regulation problem). The unique aspect of this approach is that the WMR reaches the goal position with a desired orientation and is not required to turn in place as in many of the previous results. As described in [4] and [20], factors such as the radial reduction phenomena, the ability to more effectively penalize the robot for leaving the desired contour, the ability to incorporate invariance to the task execution speed, and the improved ability to achieve task coordination and synchronization provide motivation to encapsulate the desired trajectory in terms of the current position and orientation. For the on-line 2D problem, a continuous controller is designed to navigate the WMR along the negative gradient of a navigation function to the goal position. As in many of the previous results, the orientation for the on-line 2D approach requires additional development (e.g., a separate regulation controller; a dipolar potential field approach [23], [24]; or a virtualobstacle [9]) to align the WMR with a desired orientation. Simulation results are provided to illustrate the performance of the second approach.2 Kinematic ModelThe class of nonholonomic systems considered in this paper can be modeled as a kinematic wheelwhere are defined asIn (1), the matrix is defined as followsand the velocity vector is defined aswith vc(t), ωc(t) ∈R denoting the linear and angular velocity of the system. In (2), xc(t), yc(t), and θ(t) ∈R denote the position and orientation, respectively, xc(t), yc(t) denote the Cartesian components of the linear velocity, and θ(t) ∈ R denotes the angular velocity.3 Control ObjectiveThe control objective in this paper is to navigate a non-holonomic system (e.g., a wheeled mobile robot) along a collision-free path to a constant,goal position and orientation, denoted by , in an obstacle cluttered environment with known obstacles. Specifically, the objective is to control the non-holonomic system along a path from an initial position and orientation to q∗∈D, where D denotes a free configuration space. The free configuration space D is a subset of thewhole configuration space with all configurations removed that involve a collision with an obstacle. To quantify the path planning-based control objective, the difference between the actual Cartesian position and orientation and the desired position and orientation, denotedby, is defined asas followswhere the desired trajectory is designed so that qd(t) → q∗. Motived by the navigation function approach in [16], a navigation-like function is utilized to generate the desired path qd(t). Specifically, the navigation-like function used in this paper is defined as follows Definition 1 Let D be a compact connected analytic manifold with boundary, and let q∗be a goal point in the interior of D. The navigation-like function ϕ(q): D →[0, 1], is a function satisfies the following properties:1. ϕ (q(t)) is first order and second order differentiable (i.e., and´exist on D).2. ϕ (q(t)) obtains its maximum value on the boundary of D.3. ϕ (q(t)) has unique global minimum at q (t) = q∗.4. If with εz, εr ∈ R being known positive constants.5. If ϕ(q(t)) is ultimately bounded by ε, then is ultimately bounded by εr with ε∈ R being some known positive constant.4 Online 3D Path Planner4.1 Trajectory PlanningThe 3D desired trajectory can be generated online as followswhere ϕ(q) ∈ R denotes a navigation-like function defined in Definition1, denotes the gradient vector of ϕ(q), and is anadditional control term to be designed. Assumption The navigation-like function defined in Definition 1 along with the desired trajectory generated by (6) ensures an auxiliary terms N (·) ∈ R3, defined assatisfy the following inequalitywhere the positive function ρ (·) is nondecreasing in and . The inequality given by (8) will be used in the subsequent stability analysis.4.2 Model TransformationTo achieve the control objective, a controller must be designed to track the desired trajectory developed in (6) and stop at the goal position q∗. To this end, the unified tracking and regulation controller presented in [7] can be used. To develop the controller in [7], the open-loop error system defined in (5) must be transformed into a suitable form. Specifically, the position and orientation tracking error signals defined in (5) are related to the auxiliary tracking error variables w(t) ∈R and through the following global invertible transformation [8]After taking the time derivative of (9) and using (1)-(5) and (9), the tracking error dynamics can be expressed in terms of the auxiliary variables defined in (9) as follows [8]where denotes a skew-symmetric matrix defined asand is defined asThe auxiliary control input introduced in (10)is defined in terms of and as follows4.3 Control DevelopmentTo facilitate the control development, an auxiliary error signal, denotedby, is defined as the difference between the subsequentlydesigned dynamic oscillator-like signal and the transformed variable z(t), defined in (9), as followsBased on the open-loop kinematic system given in (10) and the subsequent stability analysis, we design u(t) as follows [7]where k2 ∈ R is a positive, constant control gain. The auxiliary control term introduced in (15) is defined aswhere the auxiliary signal zd(t) is defined by the following differential equation and initial conditionThe auxiliary terms Ω1 (w, f, t) ∈ R and δd(t) ∈ R are defined asandrespectively, k1, α0, α1, ε1 ∈R are positive, constant control gains, and was defined in (12). As described in [8], motivation for the structure of (17) and (19) is based on the fact thatBased on (9), e (t) can be expressed in terms of,and zd (t) as followswhere are defined as followsMotivated by the subsequent stability analysis, the additional control term vr (t) in (6) is designed as followswhere k3, k4 ∈R denotes positive, constant control gains, and the positive functions ρ1 (zd1, z1, qd, e),ρ2 (zd1, z1, qd, e) ∈ R are defined as follows4.4 Closed-loop Error SystemAfter substituting (15) into (10), the dynamics for w(t) can be obtained as followswhere (14) and the properties of J in (11) were utilized. After substituting (16) into (26) for only the second occurrence of ua(t), utilizing (20) and the properties of J in (11), the final expression for the closed-loop error system for w(t) can be obtained as followsTo determine the closed-loop error system for, we take the timederivative of (14) and then substitute (10) and (17) into the resulting expression to obtain the following expressionAfter substituting (15) and (16) into (28), (28) can be rewritten as followsAfter substituting (18) into (29) for only the second occurrence of Ω1 (t) and then canceling common terms, the following expression can be obtainedSince the bracketed term in (30) is equal to ua (t) defined in (16), the final expression for the closed-loop error system for can be obtained asfollowsRemark 1 Based on the fact that δd (t) of (19) exponentially approachesan arbitrarily small constant, the potential singularities in (16), (17), and (18) are always avoided.4.5 Stability AnalysisTheorem 1 Provided qd (0) ∈ D, the desired trajectory generated by (6) along with the additional control term vr (t) designed in (24) ensures thatand.where εr is defined in Definition 1.Proof: Let V (t) ∈ R denote the following functionwhere k ∈R is a positive constant, V1 (t) ∈R denotes the following functionand V2 (qd) : D → R denotes a function as followsAfter taking the time derivative of (33) and then substituting (27) and (31) into the resulting expression and cancelling common terms, the following expression can be obtainedAfter taking the time derivative of (34) and utilizing (6), the following expression can be obtainedwhere N (·) is defined in (7). Based on (8), úV2 (t) can be upper bounded as followsAfter substituting (21) into (37), the following inequality can be obtainedwhere the vector is defined as followsand the positive function ρ1 (zd1, z1, qd, e) andρ2 (zd1, z1, qd, e) are defined in (25). After substituting (24) into (38), V2 (t) can be rewritten as followsBased on (35) and (40), the time derivative of V (t) in (32) can be upper bounded by the following inequalitywhere the positive constant are defined as followsCase 1: If , from the Property 4 in Definition 1, it is clearthatCase 2: If , it is clear from (32), (33), (34), and (41) thatwhere and are positive constants. Based on (42), V (t) can be upper bounded as followsthereforeBased on (32), (34), and (44), it is clear thatIf qd (0) is not on the boundary of D, ϕ(qd (0)) < 1. Then k can be adjusted to ensureBased on (45) and (46), ϕ (qd (t)) < 1, hence qd (t) ∈ D from Definition1. It is clearly from (43) that ϕ (qd) is ultimately bounded by²z. Therefore, if, k4 can be adjusted to ensure, whereε is defined in Definition 1. Hence by the Property 5 in Definition 1,is ultimately bounded by εr.¤Theorem 2 The kinematic control law given in (15)-(19) ensures global uniformly ultimately bounded (GUUB) position and orientation tracking in the sense thatwhere ε1 was given in (19), , and ε3and γ0 are positive constants. Proof: Based on (33) and (35), V1 (t) of (35) can be upper bounded as followsBased on (48), the following inequality can be obtainedBased on (33), (49) can be rewritten as followswhere the vector Ψ1 (t) is de fined in (39). From (33) and (49), it is clear that w (t) ,∈L∞. Based on (19) and (20), we can conclude that zd (t)∈ L∞. From (14) and, zd (t) ∈ L∞, it is clear that z (t) ∈ L∞. Since w(t), z (t) ∈ L∞, based on the inverse transformation from (9), e (t) ∈L∞. Based on qd (t) ∈ L∞ from Theorem 1 and e (t) ∈ L∞, it is clear that q (t) ∈ L∞. From (22)-(25), qd (t), zd (t), z (t), e (t) ∈ L∞, and the properties in Definition 1, we can conclude that vr (t), qd (t) ∈ L∞. Based on (12) and q (t), z (t), qd (t) ∈ L∞, f (θ, z2, qd) ∈ L∞. Then Ω1 (t) ∈ L∞ from(18). Then u (t), ua (t), zd (t) ∈ L∞ from (15)-(17). Based on the fact thatf (θ, z2, qd), z (t), u (t) ∈ L∞, then (10) can be used to conclude w (t), z (t) ∈ L∞. It is clear from z(t) , zd (t) ∈ L∞ that∈ L∞. Then standard signal chasing arguments can be employed to conclude that all of the remaining signals in the control and the system remain bounded during closed-loop operation.Based on (19), (20), (39), and (50), the triangle inequality can be applied to (14) to prove thatUtilizing (50)-(51), the result given in (47) can be obtained from taking the inverse of the transformation given in (9). ¤Remark 2 Although qd (t) is a collision-free path, the stability result in Theorem 2 only ensures practical tracking of the path in the sense that the actual WMR trajectory is only guaranteed to remain in a neighborhood of the desired path. From (5) and (47), the following bound can bedevelopedwhere qd (t) ∈ D based on the proof for Theorem 1. To ensure that q (t) ∈ D, the free configuration space needs to be reduced to incorporate the effects of the second and third terms on the right hand side of (52). To this end, the size of the obstacles could be increased by, where ε3ε1 can be made arbitrarily small by adjusting the control gains. To minimize the effects of ε2, the initial conditions w (0) and z (0) (and hence, could be required to be sufficiently small enough to yield a feasible path to the goal.5 Online 2D NavigationIn the previous approach, the size of the obstacles is required to be increased due to the fact that the navigation-like function is formulated in terms of the desired trajectory. In the following approach, the navigation function proposed in [22] is formulated based on current position feedback, and hence, q (t) can be proven to be a member of D without placing restrictions on the initial conditions.5.1 Trajectory PlanningLet ϕ(xc, yc) ∈R denote a 2D position-based navigation function defined in D that is generated online, where the gradient vector of ϕ(xc, yc) is defined as followsLet θd (xc, yc) ∈R denote a desired orientation that is defined as a function of the negated gradient of the 2D navigation function as followswhere arctan 2 (·) : R2 → R denotes the four quadrant inverse tangent function [26], where θd (t) is confined to the followingregion As stated in [21], by defining, then θd(t) remains continuous alongany approaching direction to the goal position. See Appendix for an expression for θd(t) based on the previous continuous definition for θd(t). Remark 3 As discussed in [22], the construction of the function ϕ(q(t)), coined a navigation function, that satisfies the first three properties in Definition 1 for a general obstacle avoidance problem is nontrivial. Indeed, for a typical obstacle avoidance, it does not seem possible toconstruct ϕ(q(t)) such that only at q (t) = q∗. That is, as discussed in [22], the appearance of interior saddle points (i.e., unstable equilibria) seems to be unavoidable; however, these unstable equilibria do not really cause any difficulty in practice. That is, ϕ(q(t)) can be constructed as shown in [22] such that only a .few. initial conditions will actually get stuck on the unstable equilibria. 5.2 Control Development Based on the open-loop system introduced in (1)-(4) and the subsequent stability analysis, the linear velocity control input vc (t) is designed as followswhere kv ∈R denotes a positive, constant control gain, and was introduced in (5). After substituting (55) into (1), the following closed-loop system can be obtainedThe open-loop orientation tracking error system can be obtained bytaking the time derivative of in (5) as followswhere (1) was utilized. Based on (57), the angular velocity control inputωc (t) is designed as followswhere kω ∈ R denotes a positive, constant control gain, and θd(t) denotes the time derivative of the desired orientation. See Appendix for an explicit expression forθd (t). After substituting (58) into (57), the closed-loop orientation tracking error system is given by the following linear relationshipLinear analysis techniques can be used to solve (59) as followsAfter substituting (60) into (56) the following closed-loop error system can be determined5.3 Stability AnalysisTheorem 3 The control input designed in (55) and (58) along with the navigation function ensure asymptotic navigation in the sense thatProof: Let: D → R denote the following non-negative functionAfter taking the time derivative of (63) and utilizing (1), (53), and (56), the following expression can be obtainedBased on the development provided in Appendix, the gradient of thenavigation function can be expressed as followsAfter substituting (65) into (64), the following expression can be obtainedAfter utilizing a trigonometric identity, (66) can be rewritten as followswhere g(t) ∈ R denotes the following positive functionBased on (53) and the property of the navigation function (Similar to theProperty 1 of Definition 1), it is clear that on D; hence, (55) can be used to conclude that vc (t) ∈ L∞ on D. Development is also provided in the Appendix that proves θd (t) ∈ L∞ on D; hence, (58) can be used to show that ωc (t) ∈ L∞ on D. Based on the fact that vc (t) ∈L∞ on D, (1)-(4) can be used to prove that xc (t), yc (t) ∈ L∞ on D. After taking the time derivative of (53) the following expression can be obtainedSince xc (t), yc (t) ∈L∞ on D, and since each element of the Hessian matrix in (69) is bounded by the property of the navigation function (Similar to the Property 1 ofDefinition 1), it is clear that gú(t) ∈ L∞ on D. Based on (63), (67), (68), and the fact that g(t) ∈ L∞ on D, then Lemma A.6 of [6] can be invoked to prove thatin the region D. Based on the fact that 1 from (60), then (70)can be used to prove that. Therefore the result in (62) can be obtained based on the analysis in Remark 3. ¤Remark 4 The control development in this section is based on a 2D position navigation function. To achieve the objective, a desired orientation θd (t) was defined as a function of the negated gradient of the 2D navigation function. The previous development can be used to provethe result in (62). If a navigation function can be found suchthat, then asymptotic navigation can be achieved by the controller in (55) and (58); otherwise, a standard regulation controller (e.g., see [8] for several candidates) could be implemented to regulate theorientation of the WMR from. Alternatively, a dipolar potential field approach [23], [24] or a virtual obstacle [9] could be utilized to align the gradient field of the navigation function to the goal orientation of the WMR.6 Simulation ResultsTo illustrate the performance of the controller given in (55) and (58), a numerical simulation was performed to navigate the WMR fromto.Since the properties of a navigation function are invariant under a diffeomorphism, a diffeomorphism is developed to map the WMR free configuration space to a model space [17]. Specifically, a positive function was chosen as followswhere κ is positive integer parameter, and the boundary functionand the obstacle function are defined as followsIn (72), and are the centers of the boundary and the obstacle respectively, r0, r1 ∈R are the radii of the boundary and the obstacle respectively. From (71) and (72), it is clear that the model space is a unit circle that excludes a circle described by the obstaclefunction. If more obstacles are present, the corresponding obstacle functions can be easily incorporated into the navigation function [17]. In [17], Koditschek proved that in (71) is the navigationfunction for, provided that κ is big enough. For the simulation, the model space configuration is selected as followswhere the initial and goal configuration were selected asThe control inputs defined in (55) and (58) were utilized to drive the WMR to the goal point along the negated gradient angle. The control gains kv and kω were adjusted to the following values to yield the best performanceOnce the WMR reached the goal position, the regulation controller in [8]was implemented to regulate the WMR from . The actual trajectory of WMR is shown in Figure 1. The outer circle in Figure1 depicts the outer boundary of the obstacle free space and the inner circle represents the boundary around an obstacle. The resulting position and orientation errors for the WMR are depicted in Figure 2, where therotational error shown in Figure 2 is the error between the actual orientation and goal orientation. The control in-put velocities vc(t) and ωc(t) defined in (55) and (58), respectively, are depicted in Figure 3. Note that the angular velocity input was artificially saturated between ±90[deg ·s−1].7 ConclusionsTwo approaches are developed to incorporate navigation function approach into different controllers for task execution by a WMR in the presence of known obstacles. The first approach utilizes a navigation-like function that is based on 3D position and orientation information. The navigation-like function yields a path from an initial configuration inside the free configuration space to a goal configuration. A differentiable, oscillator-based controller is then used to enable the mobile robot to follow the path and stop at the goal position. Using this approach, a WMR was proven to yield uniformly ultimately bounded path following and regulation to the goal point with an arbitrarily defined goal orientation (i.e., the WMR is not required to spin in place at the goal position to achieve a desired orientation). A second approach is developed that uses a navigation function that is constructed using 2D position information. A differentiable controller is proposed based on this navigation function. The advantage of this approach is that it yieldsasymptotic position convergence; however, the WMR cannot stop at an arbitrary orientation without additional development. Simulation results are provided to illustrate the performance of the second approach. AppendixBased on the definition of θd (t) in (54), θd (t) can be expressed in terms of the natural logarithm as follows [26]where . After exploiting the following identities [26]and then utilizing (74) the following expressions can be obtainedAfter utilizing (75) and (76), the following expression can be obtainedBased on the expression in (74), the time derivative of θd (t) can be written as followswhere·After substituting (1), (79), and (80) into (78), the following expression can be obtainedAfter substituting (55) and (77) into (81), the following expression can be obtained¸.By part 1 of Definition 1, each element of the Hessian matrix is bounded;hence, from (82), it is straightforward that。
机器人外文翻译(中英文翻译)
机器人外文翻译(中英文翻译)机器人外文翻译(中英文翻译)With the rapid development of technology, the use of robots has become increasingly prevalent in various industries. Robots are now commonly employed to perform tasks that are dangerous, repetitive, or require a high level of precision. However, in order for robots to effectively communicate with humans and fulfill their intended functions, accurate translation between different languages is crucial. In this article, we will explore the importance of machine translation in enabling robots to perform translation tasks, as well as discuss current advancements and challenges in this field.1. IntroductionMachine translation refers to the use of computer algorithms to automatically translate text or speech from one language to another. The ultimate goal of machine translation is to produce translations that are as accurate and natural as those generated by human translators. In the context of robots, machine translation plays a vital role in allowing them to understand and respond to human commands, as well as facilitating communication between robots of different origins.2. Advancements in Machine TranslationThe field of machine translation has experienced significant advancements in recent years, thanks to breakthroughs in artificial intelligence and deep learning. These advancements have led to the development of neural machine translation (NMT) systems, which have greatly improved translation quality. NMT models operate by analyzinglarge amounts of bilingual data, allowing them to learn the syntactic and semantic structures of different languages. As a result, NMT systems are capable of providing more accurate translations compared to traditional rule-based or statistical machine translation approaches.3. Challenges in Machine Translation for RobotsAlthough the advancements in machine translation have greatly improved translation quality, there are still challenges that need to be addressed when applying machine translation to robots. One prominent challenge is the variability of language use, including slang, idioms, and cultural references. These nuances can pose difficulties for machine translation systems, as they often require a deep understanding of the context and cultural background. Researchers are currently working on developing techniques to enhance the ability of machine translation systems to handle such linguistic variations.Another challenge is the real-time requirement of translation in a robotic setting. Robots often need to process and translate information on the fly, and any delay in translation can affect the overall performance and efficiency of the robot. Optimizing translation speed without sacrificing translation quality is an ongoing challenge for researchers in the field.4. Applications of Robot TranslationThe ability for robots to translate languages opens up a wide range of applications in various industries. One application is in the field of customer service, where robots can assist customers in multiple languages, providing support and information. Another application is in healthcare settings, where robots can act as interpreters between healthcare professionals and patientswho may speak different languages. Moreover, in international business and diplomacy, robots equipped with translation capabilities can bridge language barriers and facilitate effective communication between parties.5. ConclusionIn conclusion, machine translation plays a crucial role in enabling robots to effectively communicate with humans and fulfill their intended functions. The advancements in neural machine translation have greatly improved translation quality, but challenges such as language variability and real-time translation requirements still exist. With continuous research and innovation, the future of machine translation for robots holds great potential in various industries, revolutionizing the way we communicate and interact with technology.。
轮式移动机器人定位和导航系统设计
信 息 技 术DOI：10.16661/ki.1672-3791.2004-5154-8568轮式移动机器人定位和导航系统设计董明泽1 韩雨薇1 许凯成2 段睿劼1 朱天宇1(1.中国计量大学量新学院; 2.中国计量大学机电工程学院 浙江杭州 310018)摘 要：该文设计了一套基于开源机器人操作系统（ROS）和激光雷达的移动机器人控制系统方案，以满足当前室内机器人在定位与地图构建上的需求。
该系统以开源卡片式电脑树莓派3B+为核心控制器，使用STM32作为驱动控制板，在Linux系统下使用ROS分布式框架下进行软件算法的开发。
根据机器人的状态和用户命令可实现人机交互、SLAM地图扫描绘制、WiFi远程控制、即时定位和室内导航的功能。
实际调试结果表明，系统能够构建出与实际环境差别较小的特征图，并对平台实时位置进行精确的定位，能有效完成定位和导航的任务。
关键词：ROS SLAM 激光雷达 同步定位与地图构建 自主导航中图分类号：TP242 文献标识码：A 文章编号：1672-3791(2020)11(a)-0031-03 Design of Wheeled Mobile Robot Positioning and NavigationSystemDONG Mingze1 HAN Yuwei1 XU Kaicheng2 DUAN Ruijie1 ZHU Tianyu1(1.Liangxin College, China Jiliang University; 2.College of Mechanical and Electrical Engineering, ChinaJiliang University, Hangzhou, Zhejiang Province, 310018 China) Abstract: This paper designs a set of mobile robot control system solutions based on open source robot operating system (ROS) and lidar to meet the needs of current indoor robots in positioning and map construction. This system uses the open source card computer Raspberry Pi 3B+ as the core controller, uses STM32 as the drive control board, and uses the ROS distributed framework to develop software algorithms under the Linux system. According to the state of the robot and user commands, it can realize the functions of human-computer interaction, SLAM map scanning and drawing, WiFi remote control, instant positioning and indoor navigation. The actual debugging results show that the system can construct a feature map witha small difference from the actual environment, and accurately locate the real-time position of the platform,which can effectively complete the positioning and navigation tasks.Key Words: ROS; SLAM; Lidar; Synchronous positioning and map construction; Autonomous navigation机器人技术是一门快速发展的学科，它包含着深厚的科学理论，长期以来吸引了许多研究人员。
工业机器人英汉词汇
工业机器人英汉词汇Aabrasive wheel 砂轮绝对精度absolute accuracy交流变频器驱动AC inverter drive加速性能 acceleration performance加速时间acceleration time准确定位accurate positioning适应控制adaptive controladaptive robot 适应机器⼈附加轴additional axis附加负载additional loadadditional mass附加质量附加操作additional operation㬵黏剂密封adhesive sealingadvanced collision avoidance高级碰撞避免航空航天工业 aerospace industryagricultural robot农业机器人air robot 空中机器人air tube 空气管alignment pose 校准位姿全电动工业机器人 all-electric industrial robotant colony algorithm蚁群算法 anthropomorphic robot 拟人机器人应用程序application program圆弧示教arc teachingarc welding 点焊，电弧焊弧焊机器人arc welding purpose robot电弧焊机器人arc welding robotarch motion 圆弧运动arm 手臂手臂配置arm configuration关节模型articulated model铰接式机器人，关节(形)机器人 articulated robot关节结构articulated structure人工智能artificial intelligence流水线，装配线assembly lineassembly robot 装配机器人atomization air雾化空气attained pose 实到位姿增强现实技术 augmented reality technologyauto part 汽车零件自动码垛automated palletizingautomated production 自动化生产automatic assembly line自动装配线自动控制automatic control末端执行器自动更换装置 automatic end effector exchanger自动物流运输automatic logistics transportautomatic mode 自动模式自动操作automatic operation自动换刀automatic tool changerautomatically controlled自动控制automation technology 自动化技术汽车行业automotive industry辅助轴电缆auxiliary axis cableaxis 轴axis movement 轴运动BBase 机座机座坐标系base coordinate system机座安装面base mounting surfacebeltless structure无带结构bend motion 弯曲运动big data 大数据bio-inspired robotics仿生机器人制动过滤器brake filter制动电阻brake resistor内置碰撞检测功能 built-in collision detection feature内置控制器built-in controller内置梯形图逻辑处理 built-in ladder logic processingbus cable 总线电缆C电缆干扰cable interferencecamera sensor 相机传感器基于相机的工件定位 camera-based part locationCartesian coordinate笛卡尔坐标系笛卡尔坐标机器人 Cartesian coordinate robot直⻆坐标机器人cartesian robot儿童看护机器人child care robotclean room 洁净室clean room robot 清洁室机器人cloud computing 云计算云存储技术cloud storage technology协作机器人collaborative robot彩色触摸屏color touch screencombustible gas 可燃气体command pose 指令位姿commissioning 试运行communication feature 通信功能communication protocol 通信协议紧凑式六臂机器人compact six-axis robotcompliance 柔顺性component placemen 元件贴装复合材料composite materialcompound movement 复合运动compressed air 压缩空气计算机数控computer numerical control计算机数控机床 computer numerical control machine计算机数控系统 computer numerical control systemcomputing control 计算控制computing power 计算能力构形configuration无缝连接connect seamlessly可连接控制器connectable controllerconsumable part 中小型零部件消费类电子产品consumer electronicscontinuous path 连续路径连续路径控制continuous path control轨迹控制continuous- path controlled控制算法control algorithmcontrol electronics电子控制装置control movement 控制运动control program 控制程序control scheme 控制方案control system 控制系统控制器机柜;控制柜 controller cabinet控制器系统面板 controller system panel (CSP)人机协作 cooperation of humans and machines坐标变换 coordinate transformation核心竞争力core competitiveness对应关节corresponding joint曲线示教curve teaching网络物理系统cyber-physical systemcycle 循环cycle time 循环时间圆柱坐标系 cylindrical coordinate systemcylindrical joint圆柱关节圆柱坐标机器人cylindrical robotD达芬奇手术机器人 DaVinci surgical robot电弧焊机器人 dedicated arc welding robot防护等级degree of protectiondegrees of freedom 自由度Delta并联关节机器人 Delta parallel joint robotDelta robot Delta机器人DexTAR教育机器人 DexTAR educational robotdie-casting machine压铸机数字动力digital power直接空气管路direct air line直接耦合direct coupling直接驱动direct drive残障辅助机器人 disability auxiliary robotdisplacement machine 变位机距离准确度distance accuracy距离重复性distance repeatability分布关节distributed jointDOF 自由度double-arm SCARA robot 双臂SCARA机器人 drawing machine 拉丝机drift of pose accuracy位姿准确度漂移位姿重复性漂移 drift of pose repeatability伺服驱动器轴drive controller for axesdrive controller伺服驱动器drive mechanism 驱动机构drive power supply驱动电源驱动比drive ratio驱动单元drive unitdriving device驱动装置dual arm 双臂。
机设专业智能化的物流搬运机器人-AGV毕业论文外文文献翻译及原文
毕业设计（论文）外文文献翻译文献、资料中文题目：智能化的物流搬运机器人－AGV文献、资料英文题目：文献、资料来源：文献、资料发表（出版）日期：院（部）：专业：机设专业班级：姓名：学号：指导教师：翻译日期： 2017.02.14本科毕业外文翻译Intelligent logistics handling robot--AGVHandling the logistics function is one of the elements of the logistics systems have a high rate, logistics occupy an important part of the cost. United States industrial production process Handling costs account for 20-30% of the cost. German logistics enterprises Material handling costs account for one-third of the turnover. Japan logistics handling costs account for the GNP 10.73%,and China production logistics handling costs account for about 15.5% of the manufacturing cost. All of the world have been seeking mechanization and intelligent handling technology and equipment. AGV, a flexible and intelligent logistics handling robots, from the 1950s, storage industry begans to use. now in the manufacturing sector, ports, terminals and other areas of universal application.AGV notable feature is unmanned, the AGV is equipped with an automatic guidance system, system can be protected in no artificial pilot circumstances can be scheduled along the route will automatically, goods or materials from the threshold automatically delivered to the destination. Another feature of the AGV is good flexibility and a high degree of automation and a high level of intelligence, AGV according to the route of storage spaces, such as changes in the production process and the flexibility to change, running path and the cost of change with the traditional carousels and rigid transmission line compared to low. AGV is equipped with the general handling agencies, equipment and other logistics automatic interface, Implementation of goods and material handling and the removal process automation. Moreover, the AGV is also cleaner production characteristics, AGV rely on the built-in battery powered. running process without the noise, pollution-free, and can be applied to many of the requirements in the working environment cleaner place.ⅠAGV typesAGV it has been since the invention of a 50-year history, with the expansion of areas of application, of the types and forms of diversity has become. Often under the AGV will automatically process the way of AGV navigation divided into the following categories :1.Electromagnetic Induction-guided AGVElectromagnetic Induction general guide is on the ground, along a predetermined routeof the buried cable, when the high-frequency currents flowing through wires, Traverseelectromagnetic field generated around, AGV symmetrical installed two electromagnetic sensors, they receive the electromagnetic signal intensity differences reflect AGV deviated from the path degree. AGV control system based on this bias to control the vehicle's steering, Continuous dynamic closed-loop control to ensure AGV path for the creation of a stable tracking. This guide electromagnetic induction method of navigation in the vast majority of the AGVS commercial use, particularly applies to the large and medium-sized AGV.2. Laser-guided AGVThe AGV species can be installed on a rotating laser scanner, running path along the walls or pillars installed a high reflective of positioning signs, AGV rely on the laser scanner fired a laser beam, followed by the reflective signs around the positioning of the laser beam back, on-board computer to calculate the current vehicle position and the direction of movement, adopted and built-in digital maps correction compared to the position, thus achieving automatic removal.Currently, the types of AGV increasingly prevalent. And the basis of the same guiding principles, if the laser scanner replacement for infrared transmitters, ultrasonic transmitters. is laser-guided AGV can become infrared-guided AGV and ultrasound-guided AGV.3. Vision-guided AGVVision-guided AGV is under rapid development and maturity of the AGV. The species AGV is equipped with a CCD camera and sensors. on-board computer equipped with AGV wishes to the route of the surrounding environment image database. AGV moving process, dynamic access to traffic cameras around environmental information and images and image databases, thus determine the current location of the next stage will make a decision.AGV such as setting up does not require any physical path, in theory, has the best guide Flexible, With the computer image acquisition, storage and processing of the rapid development of technology, the kinds of practical AGV is growing.In addition, there are ferromagnetic gyro inertial-guided AGV, optical-guided AGV variety of forms of AGV.Ⅱ Application of AGV1. WarehousingWarehousing AGV is the first application of the place. In 1954 the first to AGV in the United States state of South Carolina Mercury M otor Freight company's operational warehouse for storage of goods from achieving automatic removal. At present the world is about 2 million operation in a wide range of AGV 2,100 large and small warehouses. Videocon Group in 2000, running the operation zone warehouse, 9 AGV Taiwan formed asoft bank automatic handling system, successfully completed the 23,400 daily conveying goods and parts handling tasks.2. ManufacturingAGV production in the manufacturing sector in line to succeed, efficient, accurate and flexible materials to complete the task of handling. And may be composed of multiple AGV Flexible handling of the logistics system Along with handling the production line can process adjustments and timely adjustment make a production line to produce more than 10 types of products, greatly improving production flexibility and the competitiveness of enterprises. 1974 Sweden's V olvo Kalmar car assembly plants in order to improve the transport system flexibility AGVS based tools to carry automatic car assembly line, from the assembly line more than capable of carrying the body of car components AGVS use of the assembly line. reduced assembly time by 20% and 39% decrease assembly fault, the investment recovery period decreased 57% labor decreased by 5%. Currently, AGV in the world's major car manufacturers, such as General Motors, Toyota, Chrysler, public works, such as automobile manufacturing and assembly line is being widely used.In recent years, as the basis for CIMS removal tool, the AGV to the mechanical application of in-depth processing, production of home appliances, microelectronics manufacturing, tobacco and other industries, production and processing areas to become the most widely AGV areas.3. Post office, library, port and airportAt post offices, libraries, and airport terminals occasions, the delivery of the existence of operational changes, dynamic nature, processes recurring adjustments, and removal processes in a single, features AGV concurrent operations, automation, Intelligent and flexible to the characteristics of a good occasion to meet on-removal requirements. Sweden in 1983 in Stockholm offices Slovakia, Japan in 1988 in Tokyo, Tama offices, China in 1990 in Shanghai started to use postal hub AGV complete removal products work. Port of Rotterdam in the Netherlands. 50 known as the "yard tractors" AGV completed container from the side of the delivery of several hundred yards from the The repeatability warehouse work.4. Tobacco, medicine, food, chemicalsFor the removal operation is clean, safe, non-polluting emissions, and other special requirements of the tobacco, pharmaceutical, food, chemical and other industries, AGV application also be in focus. Many cigarette enterprises laser-guided AGV completed pallet cargo handling work such as Philip Morris tobacco company 、Royal tobacco company etc.5. Dangerous places and special servicesMilitarily, the AGV to the automatic driving-based Integrated detection and other demolition equipment, Mine can be used for battlefield reconnaissance and position, the British military is developing a MINDER Recce is a reconnaissance vehicle, with mine detection, destruction and the ability to route automatically verify type reconnaissance vehicles. In the steel plant, AGV Charge for delivery, reducing the labor intensity. In nuclear power plants and the use of nuclear radiation preservation of the storage sites, AGV used for the delivery to avoid the danger of radiation. In the film and film storage, AGV be in the dark environment, accurate and reliable transportation of materials and semi-finished products.Ⅲ AGV routes and scheduling methodAGV use of a route optimization and real-time scheduling AGV is the current field of a hotspot. Practical, it was the methods used are :1.Mathematical programmingAGV to the task of choosing the best and the best path can be summed up as a task scheduling problem. Mathematical programming methods to solve scheduling problems is the optimal solution to the traditional method. The method of solving process is actually a resource constraint to the optimization process. Practical methods of the main integer programming, dynamic programming, petri methods. Scheduling of the small-scale cases, such methods can get better results, but with the increased scale of operation, Solving the problem of time-consuming exponential growth, limitations of the method in charge,mass-line optimization and scheduling application.2.SimulationSimulation of the actual scheduling environment modeling, AGV thereby to a scheduling program for the implementation of computer simulation. Users and researchers can use simulation means to scheduling program for testing, monitoring, thereby changing the selection and scheduling strategy. Practical use of a discrete event simulation methods, object-oriented simulation and three-dimensional simulation technology, Many AGV software can be used for scheduling simulation, which, Lanner Group Witness software can quickly build simulation models, Implementation of 3D simulation and demonstration of the results of the analysis.3.ARTIFICIAL INTELLIGENCEA way for the activation process AGV described as a constraint in meeting the solution set Search optimal solution process. It said the use of knowledge of the technical knowledge included, Meanwhile the use of search technology seeks to provide a satisfactory solution. Specific methods of expert system, genetic algorithms, heuristics, neural network algorithm.Within this total, the expert system in which more practical use. It will dispatch experts abstract experience as a system can understand and implement the scheduling rules, and using conflict resolution techniques to solve large-scale AGV scheduling rules and the expansion of the conflict.Because neural network with parallel computing, distributed storage knowledge, strong adaptability, and therefore, for it to become a large-scale AGV Scheduling is a very promising approach. At present, the neural network method for a successful TSP-NP problem solving. Neural networks can optimize the composition of the solution into a discrete dynamic system of energy function, through minimizing the energy function to seek optimization solution.Genetic algorithm simulates natural process of biological evolution and genetic variation and the formation of an optimal solution. Genetic algorithm for the optimization of the AGV scheduling problem, First through the coding of a certain number of possible scheduling program into the appropriate chromosome, and the calculation of each chromosome fitness (such as running the shortest path), through repeated reproduction, crossover, Find fitness variation large chromosomes, AGV scheduling problem that is the optimal solution.Using a single method to solve scheduling problems, there were some flaws. Currently, a variety of integration methods to solve the scheduling problem AGV is a hotspot. For example, expert system integration and genetic algorithm, expert knowledge into the chromosome of the initial formation of the group, Solution to accelerate the speed and quality.智能化的物流搬运机器人－AGV装卸搬运是物流的功能要素之一，在物流系统中发生的频率很高，占据物流费用的重要部分。
AGV自动导航小车外文翻译
and stability
over a wide range
can guarantee
over a wide range of parameter
changes, but have difficulty in guaranteeing performance (i.e., percent overshoot, damping, etc.) as well as stability. Another approach, feedback linearization, is promising for dealing with the nonlinearities present in the car model, but has difficulties with plant variations over time unless they are known a prioti. Optimal control can minimize a performance index related to control performance and passenger ride comfort with some inherent robustness properties (infinite gain margin and ~t60 degree phase margin), but these robust properties are only for a particular type of uncertainty, and are lost without full state feedback. (Some of the optimal control robustness properties can be regained with loop transfer recovery.) Model reference adaptive controllers, however, can guarantee both stability and performance over a wide range of slowly varying parameter changes as long as several conditions are met. These conditions are described in Section 3. Difficulties with model reference adaptive controllers (as well as other controllers) arise when saturation appears on the input or states. For vehicle applications, the steering angle is limited mechanically, and also practically because the linearized car model is only valid for small steering angles, especially at higher velocities. For example, the steering on the GMC Blazer discussed in this paper is limited to approximately &28 degrees. However, depending on the velocity and vehicle trajectory, much smaller steering angles are needed to keep the lateral acceleration less than 0.2g (on the order of f4 degrees). In this paper, the adaptation gains were chosen so that the steering command remained reasonable, even during adaptation. There is, however, a tradeoff regarding input amplitude and input frequency during adaptation. Some work has been done on investigating the stability of adaptive systems with constrained states/inputs, but this work is not applicable to the control of unstable plants with relative degree 2 (the car problem) [23-251. Therefore, this remains an area of further research. This paper is organized as follows. The vehicle model is discussed in Section 2. Section 3 outlines the adaptive controller design. Simulation results are presented in Section 4. Finally, a summary of the results and our conclusions appear in Section 5.
“移动机器人定位与导航”课程教学大纲
课程目标
毕业要求 2 3 6 12
1.理解移动机器人定位与导航的基本原理及其应用，国内外移 0.4 0.3 0.3 0.2
动机器人定位与导航研究和应用的最新进展。
2.使学生能使用定位和导航处理方法，掌握机器人运动、导航、 0.3 0.4 0.2 0.2
路径规划问题的求解方法。
3.培养学生熟悉机器人运动、智能无人驾驶、自主导航等相关
“移动机器人定位与导航”课程教学大纲（质量标准）
课程名称
移动机器人定位与导航
英文名称
Positioning and Navigation Technology of Mobile Robots
课程编号
080719
开课学期
六
课程性质
专业限选课
课程属性
必修课
课程学分
3
适用专业
机器人工程
课程学时 总学时：48； 其中理论学时：32 实验实践学时：16 上机学时：0
学历，具有讲师以上技术职称；
2.具有高校教师资格证书；
3.具备相关项目经验教师优先考虑。有扎实的数学理论基础和编程经验，关注
本学科的发展趋势，能将人工智能、模式识别新理论补充进课程； 师资标准
4.熟悉高等教育规律，有一定的教学经验，具备一定专业建设能力，能遵循应
用型本科的教学规律，正确分析、设计、实施及评价课程；
知识要点：EKF-SLAM 算法过程，EKF-SLAM 仿真实现。
学习目标：掌握导航与定位方法：在仿真环境下实现移动机器人自主。
授课建议：本部分计划 2 学时，授课方式采用仿真实例授课。
实验二：基于 UKF-SLAM 改进算法的仿真实验（目标 1，目标 2，目标 3，目标 4）
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毕业设计(论文)外文资料翻译系部：机械工程专业：机械工程及自动化姓名：学号：外文出处：Control and(用外文写)Robotics(CRB) Technical Report 附件：1.外文资料翻译译文；2.外文原文。
附件1：外文资料翻译译文轮式移动机器人的导航与控制摘要：本文研究了把几种具有导航功能的方法运用于不同的控制器开发，以实现在一个已知障碍物前面控制一个开环系统（例如：轮式移动机器人）执行任务。
第一种方法是基于三维坐标路径规划的控制方法。
具有导航功能的控制器在自由配置的空间中生成一条从初始位置到目标位置的路径。
位移控制器控制移动机器人沿设置的路径运动并停止在目标位置。
第二种方法是基于二维坐标路径规划的控制方法。
在二维平面坐标系中建立导航函数，基于这种导航函数设计的微控制器是渐进收敛控制系统。
仿真结果被用来说明第二种控制方法的性能。
1介绍很多研究者已经提出不同算法以解决在障碍物杂乱的环境下机器人的运动控制问题。
对与建立无碰撞路径和传统的路径规划算法，参考文献[19]的第一章第九部分中提供了的全面总结。
从Khatib在参考文献[13]的开创性工作以来，很显然控制机器人在已知障碍物下执行任务的主流方法之一依然是构建和应用位函数。
总之，位函数能够提供机器人工作空间、障碍位置和目标的位场。
在参考文献[19]中提供对于位函数的全面研究。
应用位函数的一个问题是局部极小化的情况可能发生以至于机器人无法到达目标位置。
不少研究人士提出了解决局部极小化错误的方法（例如参考文献[2], [3],[5], [14], [25]）。
其中Koditschek在参考文献[16]中提供了一种解决局部极小化错误的方法，那是通过基于一种特殊的位函数的完整系统构建导航函数，此函数有精确的数学结构，它能够保证存在唯一最小值。
在针对标准的 (完整的)系统的先前的结果的影响下, 面对更多的具有挑战性的非完整系统，越来越多的研究集中于位函数方法的发展(例如.,机器人)。
例如, Laumond 等人 [18] 用几何路线策划器构建了一条忽略机器人非完全约束的无障碍路线, 然后把几何线路分成更短的线路来满足非完全限制,然后应用最佳路线来减少路程。
在 [10] 和 [11]中, Guldner 等人使用间断变化的模式控制器迫使机器人的位置沿着位函数的负倾斜度变动，及其定位与负倾斜度一致。
在[1], [15], 和 [21]中,持续的位场控制器也保证了位函数的负倾斜度的位置追踪和定位追踪。
在[9]中，面对目标因为周边的障碍物而不能达到这一情况时，Ge和Cui 最近提出一种新的排斥的位函数的方法来解决这一问题。
在 [23]和[24]中, Tanner 等人采用[22] 中提出的导航函数研究和偶极位场概念为一个不完全移动操纵器建立导航函数控制器。
特别是, [23] 和 [24] 中的结果使用了间断控制器来追踪导航函数的负倾斜度, 在此过程中，一个不平坦的偶极位场使得机器人按照预想的定位拐入目标位置。
本文介绍了为不完全系统达到导航目标的两种不同的方法。
在第一个方法中, 产生了一个三维空间似导航函数的预想的轨道，它接近于机器人自由配置空间上的唯一最小值的目标位置和定位。
然后利用连续控制结构使机器人沿着这条路线走，在目标位置和定位点停下(例如,控制器解决一体化的追踪和调节问题)。
这种方法特别的地方是机器人根据预想的定位到达目标位置，而不需要像许多先前的结果中一样转弯。
正如 [4] 和 [20]中描述的一样, 一些因素如光线降低现象，更有效处罚离开预期周线的机器人的能力，使执行任务速度恒定的能力，以及达到任务协调性和同步性的能力提高等为按照目前位置和定位压缩预期轨道提供动机。
至于即时的二维空间问题设计一个连续控制器，沿着一个导航函数的负倾斜度驾驶机器人到达目标位置。
像许多先前的结果一样，在线二维空间方法的定位需要进一步发展 (例如, 一个单独的调节控制器，一个偶极位场方法[23], [24]; 或一个有效障碍物[9])来使机器人与预期的定位在一条线上。
模拟结果阐明了第二种方法的效果。
2 运动学模型本文所讨论的不完全系统的种类可以作为运动转轮的模型这里定义为在(1)中, 矩阵定义为速度向量定义为其中vc(t), ωc(t) ∈ R 表示系统线速度和角速度。
在(2)中, xc(t), yc(t),θ(t) ∈ R分别表示位置和定位，xc(t),yc(t) 表示线速度的笛卡尔成分,θ(t) ∈ R 表示角速度。
3 控制目标本文的控制目标是在一个有障碍物且混乱的环境下，沿着无碰撞轨道驾驶不完全系统（例如，机器人）到达不变的目标位置和定位，用表示。
特别是从起始位置和定位沿着轨道控制不完全系统，q∗∈ D, 这里的 D 表示一个自由的配置空间。
自由配置空间D是整个配置空间的子集，除去了所有含有障碍物碰撞的配置。
使轨道计划控制量化，实际笛卡尔位置和定位与预想的位置和定位之间的差异可表示为,定义为如下这里设计了预想的轨道，因此 qd(t) → q∗.[16]中，运用导航函数方法, 利用似导航函数生成预期路线qd(t)。
在本文中似导航函数有如下定义：定义1 把D作为连接解析流形和边界的纽带, 把q∗当作D内部的目标点. 似导航函数ϕ(q) :D →[0, 1] 是符合下列条件的函数：1. ϕ (q(t)) 第一个命令和可辨第二个命令 (例如,存在与D中的和)。
2. ϕ (q(t)) 在D的边界有最大变量。
3. ϕ (q(t)) 在 q (t) = q∗上有唯一的全局最小值.4. 如果，其中εz, εr ∈ R 是正常数。
5. 如果ϕ(q(t))被ε限制,那么被εr 限制，其中ε∈ R是正常数。
4 在线三维空间轨道计划4.1 轨道计划生成的预期的三维空间轨道如下：其中ϕ(q) ∈ R 表示定义1中定义的似导航函数, 表示ϕ(q)的倾斜向量,是另加的限制条件。
假设定义1中定义的似导航函数，沿着由(6)生成的预期轨道，确保了辅助条件N (·) ∈ R3, 表示为满足了下面的不等式其中正函数ρ (·) 在和中是不减少的。
(8) 中给的不等式将在以后的稳定性分析中用到。
4.2 模型转换为了达到控制目标，控制器必须能够追踪预期轨道，停在目标位置q∗上. 最后, 使用[7] 中提到的统一追踪和调节控制器。
为了改进[7]中的控制器,必须把(5)中定义的开路错误系统转换为合适的形式。
(5)中定义的位置和定位循迹误差信号通过以下全应可逆转换[8]和辅助循迹误差变量w(t) ∈ R 和有关。
运用 (9)中的时间导数和 (1)-(5)及(9)后, 根据(9)定义的辅助变数，循迹误差可表示为 [8]其中表示不相称矩阵，定义为定义为(10)中介绍的辅助控制输入根据和定义如下¸.4.3 控制发展为了促进控制发展, 一个辅助误差信号, 用表示, 是后来设计的动态似振荡器信号和转换的变量z(t)之间的差别,如下根据(10)中开路运动系统和后来的稳定性分析, 我们把 u(t)设计为[7]其中 k2 ∈ R 是正的不变的控制增长率。
(15)中介绍的辅助控制条件定义为其中辅助信号zd(t)由下列微分方程式和初始条件决定辅助条件Ω1(w, f, t) ∈ R and δd(t) ∈ R 分别为和, k1, α0, α1, ε 1 ∈ R是正的不变的控制增长率, 在(12)中有定义。
正如 [8]中描述的一样, (17)和(19)中结构是以以下事实为基础的根据(9), e (t) f能够用, 和表示出来，如下其中表示为在随后的稳定性分析推动下，附加的限制条件vr (t) 表示如下其中 k3, k4 ∈ R 是正的不变的控制增长率, 正函数ρ1 (zd1, z1, qd, e), ρ2 (zd1, z1, qd, e) ∈ R 表示为4.4 闭环误差系统把(15)替换到(10)中后, 得到含有w(t) 如下的公式这里利用了(14)和(11)中J的属性。
第二次出现 ua(t)时把(16)替换到(26)中，利用(20)和(11)中J的属性, 最终得到的w(t)闭环误差系统表达式如下为了确定闭环误差系统, 我们运用(14)中的时间导数，替换 (10) 和(17) 到最终表达式，达到下面的表达式替换(15)和(16)到(28), (28) 可以写成第二次出现Ω1 (t) 时，替换(18)到(29) ，然后删去相同部分，得到表达式：因为(30)中的相等条件和 (16)中定义的ua (t)是一样的, 得到闭环误差系统的最终表达式如下备注1根据(19)中δd (t )接近任意小常量，(16), (17),和(18)中禁止产生位奇点。
4.5 稳定性分析法则1 倘若qd (0) ∈ D, (6)中产生的预期轨道连同附加的限制条件vr (t) 保证了和，其中εr在定义1中有解释。
证明: 让V (t) ∈ R 表示下面的函数其中 k ∈ R 是一个正常数, V1 (t) ∈ R 表示下面的函数V2 (qd) : D → R 表示下面的一个函数运用(33)中时间导数，替换 (27) 和(31) 到最终的表达式，删去相同部分, 得到下面的表达式运用(34)中时间导数和(6), 得到下面的表达式其中 N (·) 在(7)中有定义。
根据 (8), V2 (t) 是上限，如下替换 (21)到(37), 得到下面的不等式其中向量表示如下正函数ρ1 (zd1, z1, qd, e) 和ρ2 (zd1, z1, qd, e)在(25)中有所定义。
替换 (24)到(38), V2 (t)可以重新写成如下根据 (35) 和 (40), (32)中 V (t)的时间导数可以按下面的不等式得到上限其中正常数表示如下.案例 1: 如果,根据定义1中属性4，得到案例 2: 如果,根据 (32), (33),(34), 和(41) 得到其中和是正常数. 根据 (42), V (t)得到上限如下因此根据 (32), (34), 和 (44),得到如果 qd (0)不在D的边界, ϕ(qd (0)) < 1， k 可以符合根据 (45) 和(46), ϕ(qd (t)) < 1, 因此从定义1得到qd (t) ∈ D，从(43) 可以得出，ϕ(qd) 最终被限制。
因此, 如果, k4 则符合 , 其中ε在定义1中有解释，进而在定义1的属性5中得到定义, 最终被εr限制。
法则2 (15)-(19)中运动学控制法保证全局统一最终限制的(GUUB) 位置和定位按下面公式追踪其中ε 1 在(19)中给定, , ε 3 和γ0 是正常数.证明: 根据 (33) 和(35), V1 (t) 得到上限如下根据 (48), 得到下面的不等式根据 (33), (49) 可以被写成其中向量Ψ1 (t)在(39)中有定义。