外文翻譯移動機(jī)器人基于LFR激光探測器和IR的MFVFA方法

1、畢業(yè)設(shè)計(jì)(論文)外文資料翻譯學(xué)院 (系)： 機(jī)械工程學(xué)院 專 業(yè)： 機(jī)械工程及自動化 姓 名： 學(xué) 號： (用外文寫)外文出處： mobile robot navigation using modified flexible vector field approach with laser range finder and ir sensor 附 件： 1.外文資料翻譯譯文；2.外文原文。 指導(dǎo)教師評語：譯文的意思基本正確，語句較通順。專業(yè)性術(shù)語的翻譯也較為得當(dāng)。譯文的數(shù)量已超過學(xué)校規(guī)定的要求。這說明該生具有較強(qiáng)的科技文獻(xiàn)的閱讀理解與翻譯能力。 簽名： 年 月 日注：請將該封面與附件裝訂成冊。

2、附件1：外文資料翻譯譯文移動機(jī)器人基于lfr激光探測器和ir的mfvfa方法摘要： 在公共空間，移動的機(jī)器人可以用作導(dǎo)游者。指導(dǎo)一個(gè)人到達(dá)目標(biāo)位置，移動的機(jī)器人的路徑應(yīng)該安全，可以避免障礙物并生成良好的導(dǎo)航的路徑。一般來說，激光測距儀是用來檢測在移動機(jī)器人周圍的地圖。我們建議移動機(jī)器人的導(dǎo)航方法用我們的，我們的方法可以在檢測緊急情況下使用，它是我們開發(fā)的一種移動機(jī)器人的導(dǎo)航改性的柔性的矢量場與激光測距儀和紅外傳感器的方法，因?yàn)樗哂诩す鉁y距儀響應(yīng)的頻率，通過實(shí)驗(yàn)結(jié)果表明了我們提出的控制方案和避障方法應(yīng)用在公共場所里移動機(jī)器人的控制是非常有效的。關(guān)鍵詞： 移動機(jī)器人導(dǎo)航 改性的柔性的矢量場方法

3、激光測距儀 紅外傳感器。1. 引言一個(gè)移動機(jī)器人可以在公共場所當(dāng)向?qū)?，比如市場，郵局，圖書館等等，最重要的功能是弄夠找到路徑到達(dá)目標(biāo)和導(dǎo)航的目標(biāo)位置。無論怎樣，在公共場所，移動機(jī)器人應(yīng)該能夠在避免障礙和到達(dá)目標(biāo)位置同時(shí)進(jìn)行。像一把椅子，一個(gè)架子和一個(gè)人等等這樣的障礙。為達(dá)到導(dǎo)航目標(biāo)位置的目的，許多研究者給了勢場法的地址12。一個(gè)向量場柱狀圖3 5和動態(tài)窗口的方法6。由于市場是由許多狹窄的通道和許多障礙組成。以下是把勢場法應(yīng)用到市場里的機(jī)器人身上遇到的一些困難： 1)在近空間的障礙中很難找到通口。當(dāng)機(jī)器人在狹窄的通道中移動會發(fā)生擺動運(yùn)動。2)還有矢量場柱狀圖對環(huán)境地圖的變化敏感，但是卻不能找到到

4、達(dá)目標(biāo)的路徑，因?yàn)槲覀儚囊苿訖C(jī)器人的完成中僅僅能得到角度的信息。動態(tài)窗口中使用了以目標(biāo),離障礙距離和速度為標(biāo)題作為移動機(jī)器人的參數(shù)在動態(tài)窗口可以通過優(yōu)化過程找到最佳速度。但是這不是唯一的為避免障礙而獲得到達(dá)目標(biāo)最短路徑的方法。為了使機(jī)器人在公共場所中安全和穩(wěn)定運(yùn)行，一個(gè)新的導(dǎo)航方法是非常必要的，這種方法對于導(dǎo)航移動機(jī)器人環(huán)境的變化和運(yùn)動最短路徑的能力是敏感的。因此，我們提出一個(gè)新的移動機(jī)器人導(dǎo)航的方法，即移動機(jī)器人的導(dǎo)航改性的柔性的矢量場與激光測距儀和紅外傳感器的方法。在我們提出的方法中，由于路徑信息的獲得來自于障礙（作為圓）和移動機(jī)器人（作為一點(diǎn)）的幾何關(guān)系，可以減少處理載荷。由于我們在移動

5、機(jī)器人中開發(fā)了具有差別驅(qū)動結(jié)構(gòu)，當(dāng)控制移動機(jī)器人趨向產(chǎn)生路徑的時(shí)候，首先應(yīng)該在運(yùn)動學(xué)條件下考慮移動機(jī)器人穩(wěn)定的速度。通常，如果把路徑規(guī)劃和路徑跟蹤分開，就會存在各種追蹤控制方法，比如滑動模式，線性化，反演，神經(jīng)網(wǎng)絡(luò)，神經(jīng)模糊系統(tǒng)。無論怎樣，使用在傳統(tǒng)的控制方法中，當(dāng)追蹤突然發(fā)生錯誤時(shí)，產(chǎn)生的這個(gè)基本速度命令是以極端大的估值和因遭受速度暴漲開始的。 在這篇論文中我們提出一個(gè)新的速度變化圖的方法來保證移動機(jī)器人穩(wěn)定的運(yùn)動。如果我們第一時(shí)間假想移動機(jī)器人開始的位置和目標(biāo)位置 ，在移動機(jī)器人的開始位置，目標(biāo)位置和現(xiàn)在位置之間使用歐氏距離，我們就能生產(chǎn)參考的速度變化圖。 2. 移動機(jī)器人平臺和路徑規(guī)劃2

6、.1 移動機(jī)器人平臺圖1是我們開發(fā)的移動機(jī)器人平臺。gimar, 移動機(jī)器人差別驅(qū)動結(jié)構(gòu)有非完整約束。它有若干個(gè)傳感器來檢測移動機(jī)器人周圍的狀況。在這次研究中，我們僅僅使用兩個(gè)傳感器，一個(gè)是（激光測距儀）檢測掃描地圖的數(shù)據(jù)和另外一個(gè)是（紅外傳感器）緊急停止和避免障礙。這兩個(gè)傳感器各有優(yōu)缺點(diǎn)。激光測距儀生產(chǎn)詳細(xì)地掃描地圖數(shù)據(jù)，但是它比紅外傳感器運(yùn)行的慢。另外一方面，紅外傳感器比激光測距儀運(yùn)行迅速除了它生成一點(diǎn)數(shù)據(jù)。因此，我們打算提出的是為穩(wěn)定驅(qū)動控制和避障這兩個(gè)傳感器的數(shù)據(jù)相結(jié)合的方法。2.2 路徑規(guī)劃為了到達(dá)目標(biāo)位置，在它開始移動之前我們就應(yīng)該知道它的路徑了。如果地圖數(shù)據(jù)時(shí)提前給出，我們就能生

7、成安全路徑到達(dá)目標(biāo)的立場，否則我們就不能得到完整的路徑。在文中，我們假設(shè)如下：1.地圖數(shù)據(jù)時(shí)沒有提前給出。2.僅僅給出了移動機(jī)器人的初始位置和目標(biāo)位置。3.移動機(jī)器人沒有滑運(yùn)動，我們可以從里程表的信息中知道移動機(jī)器人的的位置和移動機(jī)器人的姿勢由以上三個(gè)假設(shè)，我們提出了路徑規(guī)劃策略在圖2中表明。圖2 危險(xiǎn)的區(qū)域指示了移動機(jī)器人沒有障礙碰撞的一個(gè)區(qū)域。我們就可以直觀地知道紅線邊沿的路徑是最短的安全的路徑。我們事先沒有地圖數(shù)據(jù)，我們就建議找出下一個(gè)預(yù)期的點(diǎn)，這一點(diǎn)來自危險(xiǎn)區(qū)域的圓和移動機(jī)器人現(xiàn)在位置的幾何關(guān)系。雖然障礙到處都是，但是從我們提出的改性的柔性的矢量場的方法中我們就可以決定下一個(gè)預(yù)期的點(diǎn)，

8、在掃描期間里路徑規(guī)劃過程不斷的被廢除。3. 移動機(jī)器人控制非完整移動機(jī)器人可以有兩個(gè)坐標(biāo)系統(tǒng)，一個(gè)是由xg,yg ,sg構(gòu)成的世界坐標(biāo)系和另一個(gè)是由xl,yl,6l構(gòu)成的本地坐標(biāo)系.在圖3中，c是機(jī)器人中心點(diǎn)，d是做齒輪和右齒輪的距離，b是齒輪中心和主銷后傾的距離。v和w意思是移動機(jī)器人的線速度和角速度。自由移動的移動機(jī)器人是作為完整的移動機(jī)器人有三個(gè)自由度（x,y,z和零點(diǎn)）。但是，由于運(yùn)動學(xué)上的限制，非完整移動機(jī)器人的自由度減少到兩個(gè)。在沒有滑動的條件下，一個(gè)非完整移動機(jī)器人的運(yùn)動限制由以下公式給出從運(yùn)動控制角度看，我們開發(fā)的移動機(jī)器人有vc線速度和wc角速度的兩個(gè)自由度，把這兩個(gè)齒輪的直

9、徑，半徑，角速度描繪成兩輪的速度（wl, wr.）。可以用以下兩輪的角的速度關(guān)系來敘述線速度和角速度。為了控制移動機(jī)器人，兩個(gè)齒輪的速度可以分成兩部分：一個(gè)是確定移動機(jī)器人的線速度和另一個(gè)是追蹤移動機(jī)器人的姿態(tài)。vl = vc - wc vr = vc + wc vc 是線速度控制部分和wc是角速度控制部分。線速度控制的目標(biāo)是根據(jù)移動機(jī)器人的位置和目標(biāo)位置的距離來控制移動機(jī)器人的速度。在接下來的分段中，我們提出用歐氏距離來生成線速度的方法。移動機(jī)器人通過控制wc能夠避免障礙。在接下來的章節(jié)中，我們將解釋利用mfvfa對姿態(tài)誤差反饋的方法4. 仿真和實(shí)驗(yàn)結(jié)果由于提出避障運(yùn)算法則要用在狹窄的市場空

10、間中為目的，就在通道中設(shè)置像架子之類的物體作為測試環(huán)境。我們組成了市場模型的兩部分。這個(gè)測試環(huán)境的尺寸是由6米*6米。我們假設(shè)沒有人在測試環(huán)境中。在實(shí)驗(yàn)結(jié)果中，點(diǎn)線指示了要求的位置和紅線指示了激光掃描數(shù)據(jù)。圖表中也展示了多障礙情況。圖11是我們看到這個(gè)實(shí)驗(yàn)結(jié)果。如果在測試環(huán)境中有多障礙，這時(shí)提出的運(yùn)算法則能夠提供安全通道，保證移動機(jī)器人無碰撞地通過通道。之后，那個(gè)運(yùn)算法則生成到達(dá)要求位置的最短軌跡圖.10圖.10穩(wěn)定的曲率跟蹤運(yùn)算法則仿真結(jié)果（a）右拐彎仿真的結(jié)果 （b）左拐彎仿真的結(jié)果圖.10展示穩(wěn)定的曲率跟蹤運(yùn)算法則仿真結(jié)果。這個(gè)結(jié)果表明了當(dāng)機(jī)器人進(jìn)入了危險(xiǎn)區(qū)域是機(jī)器人是怎樣逃離危險(xiǎn)區(qū)域的

11、。無論機(jī)器人的位置在哪里，是在危險(xiǎn)區(qū)域還是在碰撞區(qū)域，在危險(xiǎn)區(qū)域圓周圍都可以控制機(jī)器人。圖.10表明了，機(jī)器人利用柔性的矢量場根據(jù)機(jī)器人的位置直接脫離危險(xiǎn)區(qū)域。因此，移動機(jī)器人移動到安全區(qū)域和接受穩(wěn)定的最短的路徑到達(dá)目標(biāo)位置。 圖.11圖.11： 機(jī)器人和多障礙之間避免碰撞的結(jié)果（a）-在運(yùn)用運(yùn)算法則前機(jī)器人和多障礙之間避免碰撞的結(jié)果（b）-在運(yùn)用運(yùn)算法則后機(jī)器人和多障礙之間避免碰撞的結(jié)果圖.12 展示了機(jī)器人軌跡。圖中的數(shù)據(jù)來自圖11實(shí)驗(yàn)的結(jié)果。圖12展示了機(jī)器人安全地避免障礙的結(jié)果。我們設(shè)定f200,10q), f(150,120), p(200,100), p(16g,180)作為開始位

12、置，設(shè)定p(240,450), p(i30,500), (230,500), (170,600)為要求的位置。我們把機(jī)器人的速度設(shè)成70cm/s與人類步行速度一樣.我們成功地通過在開始位置和要求的位置之間隨意地設(shè)定障礙的狹窄的通道。圖.12 展示了機(jī)器人避開障礙物。圖.12：在運(yùn)用避障運(yùn)算法則后機(jī)器人的軌跡(a)機(jī)器人的軌跡從（200，100）到(240，450cm),(b)機(jī)器人的軌跡從（180，160）到(160，520cm),(c)機(jī)器人的軌跡從（200，100）到(230，500cm),(d)機(jī)器人的軌跡從（160，180）到(170，600cm),5. 結(jié)論在這篇論文中，我們討論了導(dǎo)

13、航方法關(guān)于在改性的柔性的矢量場中使用激光測距儀和紅外傳感器的方法。該控制器分為線速度控制和角速度控制部分。利用歐氏距離并考慮了移動機(jī)器人在穩(wěn)定運(yùn)動時(shí)而生成線速度剖面圖 。角速度部分，我們利用了虛擬圓起源于角點(diǎn)和切向直線。無論怎樣，因?yàn)橐苿訖C(jī)器人存在碰撞區(qū)域，所以我們通過利用提出的穩(wěn)定曲率運(yùn)算法則來控制移動機(jī)器人。附件2：外文原文mobile robot navigation using modified flexible vector field approach with laser range finder and ir sensorjinpyo hong ,youjun choi and

14、 kyihwan parkabstract: in the public space, a mobile robot is adopted as a guider. for guiding a person to the goal position, the mobile robot should make the safe path ,avoid the obstacles and navigate the generated path well. in general, laser range finder is used for the detection of the map arou

15、nd the mobile robot. we propose mobile robot navigation method using our developed a modified flexible vector field approach with laser range finder and ir sensor which is used for detecting the emergency status because it has higher response frequency than that of lrf. we will verify that our propo

16、sed control scheme and obstacle avoidance algorithm are useful enough to apply to the mobile robot control in the public space by showing experimental results. keywords: mobile robot navigation, mfvfa(modified flexible vector field approach, lrf(laser range finder), ir sensor)1. introductionwhen a m

17、obile robot is operated as a guider in the public spaces such as a market, a post office, a museumand so on, the most important functionality is the ability to find the path to reach a goal and navigate the goal position. however, in the public space, since there are many obstacles like a shelf, a c

18、hair and a person, the mobile robot should avoid the obstacles and reach the goal position simultaneously. for the purpose of navigate the target position, many researchers have addressed a potential field method12, a vector field histogram3 5 and a dynamic window approach6. since the market is comp

19、osed of many narrow passageways and a lot obstacles, there are some difficulty to apply the potential field into our robot as a market application as follows: 1) it is hard to find the passage in the close spaced obstacles. 2) there is an oscillation motion when the robot moves in the narrow passage

20、ways. also the vector field histogram is sensitive to the change of the environmental map but it can not find the shortest path to reach the goal because we only get the angle information for the mobile robot to go through. the dynamic window uses the heading to goal, distance to obstacles and the v

21、elocity of the mobile robot as parameters. since the dynamic window has an optimisation process for finding the best velocity, this solution is not unique to the shortest path for avoiding the obstacle. for the safe and stable operation of the mobile robot in the public space, a new navigation metho

22、d is essentially needed which is sensitive to the change of the environment and capable of moving in the shortest path as a guider. therefore, we proposed a new mobile robot navigation method using modified flexible vector field approach with laser range finder(lrf) and infra red(ir) sensor. in our

23、proposed method, since the path informationis obtained from the geometric relation of the obstacle as a circle and the mobile robot as a point, the processing load can be decreased. since our developed mobile robot has a differential drive structure, the stable velocity of the mobile robot should be

24、 firstly considered in this kinematic condition when the mobile robot is controlled toward the generated path. usually, if the path planner and the path tracker are divided, the various tracking control method are existed like as sliding mode, linearization, backstepping, neural networks, neuro-fuzz

25、y systems. however, the generated root velocity command using those conventional control approaches start with a very large value, and suffers from velocity jumps when sudden tracking errors occur. therefore, we propose a new velocity profiling approach guaranteeing the stable movement of the mobile

26、 robot in this paper. if we assumed that the initial position of the mobile robot and the goal position are given at the first time, we can generate the reference velocity profile using euclidean distances among the start position , the goal position and the current mobile robot position.2. mobile r

27、obot platform and path planning2.1 mobile robot platform figure 1 is our developed mobile robot platform, gimar. the mobile robot has a differential drive structure that has non-holonomic constraint. it has several sensors for detecting the status around the mobile robot. in this research, we only u

28、sed two sensors that one is lrf for detecting the scan map data and another is ir sensor for emergency stop and obstacle avoidance. these two sensors have advantages and drawbacks respectively. lrf generates the scan map data in detail but it is operated slowly compared with ir sensor. on the other

29、hand, ir sensor operates rapidly compared with lrf but it generates one point data. therefore, we intend to propose the method combining two sensors data for stable driving control and obstacle avoidance.2.2 path planningin order lo reach the goal position, we should know he path before starting lo

30、move. if the map data is given in advance, we can generate the safe path to arrive at the goal position but otherwise, we can not do the entire path. in this paper, we assume as follows.1 .map data is not given in advanced.2. only the initial position of the mobile robot and the goal position are gi

31、ven.3. mobile robot has no slip motion and we can know the position and posture of the mobile robot from the odometry information.under three assumptions, our proposed path planning strategy is shown in fig. 2. in fig. 2. the dangerous region indicates an area that the mobile robot has no collision

32、with obstacles. we can know intuitively (hat red line path is the shortest path in safely. since we don't have map data in advance, we suggest that we find out the next desired point from the geometric relation of dangerous region circle and the current mobile robot position. although the obstac

33、le exists anywhere, we can determine the next desired point from the our proposed approach, modified flexible vector field approach(mfvfa) because the path planning process is repealed during scan period continuously.3. mobile robot controla nonholonomic mobile robot can be represented by two coordi

34、nate systems which are the world coordinate system xg,yg ,sg and the local coordinate system xl,yl,6l,. in figure 3, c is the robot center point, d is the distance between the left wheel and the right wheel and b is the distance between the center of the wheel and the caster. v and w mean the linear

35、 velocity and the angular velocity of the mobile robot.a freely movable mobile robot that is referred as holo-nomic mobile robot has three degrees of freedom(d.o.f.) x, y, and 0, however, because of the kinematical constraint, the degrees of the freedom for a nonholonomic mobile robot reduces to two

36、. on the conditions of non-slipping, the kinematic constraint of a nonhomolonomic mobile robot is given asfrom the motion control perspective, our developed mobile robot has 2 d.o.f., vc, wc , where vc is the linear velocity and wc is the angular velocity of the mobile robot.the velocities of the tw

37、o wheels are represented as the diameter of the wheel, r, and the angular velocity wl, wr. .the linear velocity and angular velocity can be described as the relation of e both wheels' angular velocities as follows.in order to control the mobile robot, the velocities of the two wheels can be divi

38、ded us two parts: one is the part determining the linear velocity of the mobile robot and another is the part tracking the posture of the mobile robot.vl = vc - wc vr = vc + wc where, vc is the linear velocity control part and wc is the angular velocity control part. the objective of the linear velo

39、city control is the velocity control of the mobile robot according to the distance between the robot position and the goal position. in the next subsection, we propose the linear velocity generation method using euclidean distance. the mobile robot can avoid the obstacle by controlling the wc. we wi

40、ll explain the posture error feedback method using mfvfa in the next section.4. simulation and experimental resultssince we have suggested the obstacle avoidance algorithm for the purpose of using in the narrow space at the market, shelf-like objects are set in the passageway for a test environment.

41、 we made up two sections of the market model as a test environment. the size of the test environment is 6 meter by 6 meter. we assume that there are no people in the test environment. in the experimental results, dotted line indicates the desired position, and the red line indicates laser scanning d

42、ata.the result of multi-obstacle case is also shown in fig. 11. as we see this experimental result in fig. 11, if there are multi-obstacles in the test environments, the proposed algorithm provides the safe passageway that the robot can go through without collision. after that, the algorithm generat

43、es the shortest trajectory to approach the desired position. fig. 10fig. 10 simulation result of stable curvature tracking algorithm - a) the right turning simulation result, b) the left turning simulation resultfig. 10 show simulation results of the stable curvature tracking algorithm. these result

44、s show how the robot escapes dangerous region when the robot moves into the dangerous region. wherever the robot is positioned between the dangerous region and collision region, the robot can be controlled toward the circumference of the dangerous region circle. as shown in fig. 10, the robot makes

45、flexible vector fields with the direction to out-of-dangerous region according to the location of the robot. therefore, the mobile robot moves to the safe region and follows the stable and shortest path for arriving at the goal position. fig. 11fig 11 : result of the collision avoidance between robo

46、t and multi-obstacles - (a) before applying the collision avoidance algorithm between robot and multi-obstacles (b) after applying the collision avoidance algorithm between robot and multi-obstaclesthe trajectories of the robot are shown in fig. 12 obtained from the experimental results which are sh

47、own in fig. 11. fig. 12 shows the result that the robot avoids obstacles safely. we set f200,10q), f(150,120), p(200,100), p(16g,180) as starting positions and p(240,450), p(i30,500), (230,500), (170,600) as desired positions. we move the robot with speed or 70cm/s which is same as the walking speed

48、 of the human. we made narrow passageway by adding arbitrary obstacles between starting position and desired position. since there is an obstacle, the robot avoids obstacles, as shown in fig. 12. fig. 12fig. 12: trajectories of robot after applying obstacle avoidance algorithm (a) trajectory of the

49、robot from (200, 100) to (240, 450cm),(b) trajectory of the robot from (180, 160) to (160, 520) cm, (c) trajectory of the robot from (200.100) to (230, 500) cm, (d) trajectory of the robot from (160,180) to (170, 600) cm.5. conclusionin this paper, we discussed about the navigation method using the modified flexible vector field with