喜歡就充值下載吧。。資源目錄里展示的文件全都有，，請(qǐng)放心下載，，有疑問咨詢QQ:414951605或者1304139763 ======================== 喜歡就充值下載吧。。資源目錄里展示的文件全都有，，請(qǐng)放心下載，，有疑問咨詢QQ:414951605或者1304139763 ======================== 河北建筑工程學(xué)院 畢業(yè)設(shè)計(jì)（論文）外文資料翻譯 系別： 機(jī) 械 工 程 系 專業(yè)： 機(jī)械設(shè)計(jì)制造及其自動(dòng)化 班級(jí)： 機(jī)083班 姓名： 王曉良 學(xué)號(hào)： 2008307335 外文出處：Proceedings ofthe 1998 IEEE International Conference on Robotics & Automation 附 件：1、外文原文；2、外文資料翻譯譯文。 指導(dǎo)教師評(píng)語： 簽字： 年 月 日 Proceedings ofthe 1998 IEEE International Conference on Robotics & Automation Leuven, Belgium May 1998 1 12 A practical approach to feedback control for a mobile robot with trailer F. Lamiraux and J.P. Laumond LAAS-CNRS Toulouse, France {florent ,jpl}@laas.fr Abstract This paper presents a robust method to control a mobile robot towing a trailer. Both problems of trajectory tracking and steering to a given configuration are addressed. This second issue is solved by an iterative trajectory tracking. Perturbations are taken into account along the motions. Experimental results on the mobile robot Hilare illustrate the validity of our approach. 1 Introduction Motion control for nonholonomic systems have given rise to a lot of work for the past 8 years. Brockett’s condition [2] made stabilization about a given configuration a challenging task for such systems, proving that it could not be performed by a simple continuous state feedback. Alternative solutions as time-varying feedback [l0, 4, 11, 13, 14, 15, 18] or discontinuous feedback [3] have been then proposed. See [5] for a survey in mobile robot motion control. On the other hand, tracking a trajectory for a nonholonomic system does not meet Brockett’s condition and thus it is an easier task. A lot of work have also addressed this problem [6, 7, 8, 12, 16] for the particular case of mobile robots. All these control laws work under the same assumption: the evolution of the system is exactly known and no perturbation makes the system deviate from its trajectory.Few papers dealing with mobile robots control take into account perturbations in the kinematics equations. [l] however proposed a method to stabilize a car about a configuration, robust to control vector fields perturbations, and based on iterative trajectory tracking. In this paper, we propose a robust scheme based on iterative trajectory tracking, to lead a robot towing a trailer to a configuration. The trajectories are computed by a motion planner described in [17] and thus avoid obstacles that are given in input. In the following.We won’t give any development about this planner,we refer to this reference for details. Moreover,we assume that the execution of a given trajectory is submitted to perturbations. The model we chose for these perturbations is very simple and very general.It presents some common points with [l]. The paper is organized as follows. Section 2 describes our experimental system Hilare and its trailer:two hooking systems will be considered (Figure 1).Section 3 deals with the control scheme and the analysis of stability and robustness. In Section 4, we present experimental results. The presence of obstacle makes the task of reaching a configuration even more difficult and require a path planning task before executing any motion. 2 Description of the system Hilare is a two driving wheel mobile robot. A trailer is hitched on this robot, defining two different systems depending on the hooking device: on system A, the trailer is hitched above the wheel axis of the robot (Figure 1, top), whereas on system B, it is hitched behind this axis (Figure l , bottom). A is the particular case of B, for which = 0. This system is however singular from a control point of view and requires more complex computations. For this reason, we deal separately with both hooking systems. Two motors enable to control the linear and angular velocities （，) of the robot. These velocities are moreover measured by odometric sensors, whereas the angle between the robot and the trailer is given by an optical encoder. The position and orientation（,,）of the robot are computed by integrating the former velocities. With these notations, the control system of B is: （1） Figure 1: Hilare with its trailer 3 Global control scheme 3.1 Motivation When considering real systems, one has to take into account perturbations during motion execution.These may have many origins as imperfection of the motors, slippage of the wheels, inertia effects ... These perturbations can be modeled by adding a term in the control system (l),leading to a new system of the form where may be either deterministic or a random variable.In the first case, the perturbation is only due to a bad knowledge of the system evolution, whereas in the second case, it comes from a random behavior of the system. We will see later that this second model is a better fit for our experimental system. To steer a robot from a start configuration to a goal, many works consider that the perturbation is only the initial distance between the robot and the goal, but that the evolution of the system is perfectly known. To solve the problem, they design an input as a function of the state and time that makes the goal an asymptotically stable equilibrium of the closed loop system. Now, if we introduce the previously defined term in this closed loop system, we don't know what will happen. We can however conjecture that if the perturbation is small and deterministic, the equilibrium point (if there is still one) will be close to the goal, and if the perturbation is a random variable, the equilibrium point will become an equilibrium subset.But we don't know anything about the position of these new equilibrium point or subset. Moreover, time varying methods are not convenient when dealing with obstacles. They can only be used in the neighborhood of the goal and this neighborhood has to be properly defined to ensure collision-free trajectories of the closed loop system. Let us notice that discontinuous state feedback cannot be applied in the case of real robots, because discontinuity in the velocity leads to infinite accelerations. The method we propose to reach a given configuration tn the presence of obstacles is the following. We first build a collision free path between the current configuration and the goal using a collision-freemotion planner described in [17], then we execute the trajectory with a simple tracking control law. At the end of the motion, the robot does never reach exactly the goal because of the various perturbations, but a neighborhood of this goal. If the reached configuration is too far from the goal, we compute another trajectory that we execute as we have done for the former one. We will now describe our trajectory tracking control law and then give robustness issues about our global iterative scheme. 3.2 The trajectory tracking control law In this section, we deal only with system A. Computations are easier for system B (see Section 3.4). Figure 2: Tracking control law for a single robot A lot of tracking control laws have been proposed for wheeled mobile robots without trailer. One of them [16],a lthough very simple, give excellent results.If are the coordinates of the reference robot in the frame of the real robot (Figure 2), and if are the inputs of the reference trajectory, this control law has the following expression: （2） The key idea of our control law is the following: when the robot goes forward, the trailer need not be stabilized (see below). So we apply (2) to the robot.When it goes backward, we define a virtual robot (Figure 3) which is symmetrical to the real one with respect to the wheel axis of the trailer: Then, when the real robot goes backward, the virtual robot goes forward and the virtual system is kinematically equivalent to the real one. Thus we apply the tracking control law (2) to the virtual robot. Figure 3: Virtual robot A question arises now: is the trailer really always stable when the robot goes forward ? The following section will answer this question. 3.3 Stability analysis of the trailer We consider here the case of a forward motion , the backward motion being equivalent by the virtual robot transformation. Let us denote by a reference trajectory and bythe real motion of the system. We assume that the robot follows exactly its reference trajectory: and we focus our attention on the trailer deviation.The evolution of this deviation is easily deduced from system (1) with (System A): is thus decreasing iff （3） Our system is moreover constrained by the inequalities （4） so that and (3) is equivalent to （5） Figure 4 shows the domain on which is decreasing for a given value of . We can see that this domain contains all positions of the trailer defined by the bounds (4). Moreover, the previous computations permit easily to show that 0 is an asymptotically stable value for the variable . Thus if the real or virtual robot follows its reference forward trajectory, the trailer is stable and will converge toward its own reference trajectory. Figure 4: Stability domain for 3.4 Virtual robot for system B When the trailer is hitched behind the robot, the former construction is even more simple: we can replace the virtual robot by the trailer. In this case indeed, the velocities of the robot and of the trailer are connected by a one-to-one mapping.The configuration of the virtual robot is then given by the following system: and the previous stability analysis can be applied as well, by considering the motion of the hitching point. The following section addresses the robustness of our iterative scheme. 3.5 Robustness of the iterative scheme We are now going to show the robustness of the iterative scheme we have described above. For this,we need to have a model of the perturbations arising when the robot moves. [l] model the perturbations by a bad knowledge of constants of the system, leading to deterministic variations on the vector fields. In our experiment we observed random perturbations due for instance to some play in the hitching system. These perturbations are very difficult to model. For this reason,we make only two simple hypotheses about them: where s is the curvilinear abscissa along the planned path, and are respectively the real and reference configurations, is a distance over the configuration space of the system and , are positive constants.The first inequality means that the distance between the real and the reference configurations is proportional to the distance covered on the planned path. The second inequality is ensured by the trajectory tracking control law that prevents the system to go too far away from its reference trajectory. Let us point out that these hypotheses are very realistic and fit a lot of perturbation models. We need now to know the length of the paths generated at each iteration. The steering method we use to compute these paths verifies a topological property accounting for small-time controllability[17]. This means that if the goal is sufficiently close to the starting configuration, the computed trajectory remains in a neighborhood of the starting configuration. In [9]we give an estimate in terms of distance: if and are two sufficiently close configurations, the length of the planned path between them verifies where is a positive constant. Thus, if is the sequence of configurations reached after i motions, we have the following inequalities: These inequalities ensure that distCS is upper bounded by a sequence of positive numbers defined by and converging toward after enough iterations. Thus, we do not obtain asymptotical stability of the goal configuration, but this result ensures the existence of a stable domain around this configuration.This result essentially comes from the very general model of perturbations we have chosen. Let us repeat that including such a perturbation model in a time varying control law would undoubtedly make it lose its asymptotical stability.The experimental results of the following section show however, that the converging domain of our control scheme is very small. 4 Experimental results We present now experimental results obtained with our robot Hilare towing a trailer, for both systems A and B. Figures 5 and 6 show examples of first paths computed by the motion planner between the initial Figure 5: System A: the initial and goal configurations and the first path to be tracked Figure 6: System B: the initial and goal configurations, the first path to be tracked and the final maneuver configurations (in black) and the goal configurations (in grey), including the last computed maneuver in the second case. The lengths of both hooking system is the following: ,cm for A and cm,cm for B. Tables 1 and 2 give the position of initial and final configurations and the gaps between the goal and the reached configurations after one motion and two motions, for 3 different experiments. In both cases, the first experiment corresponds to the figure.Empty columns mean that the precision reached after the first motion was sufficient and that no more motion was performed. Comments and Remarks: The results reported in the tables 1 and 2 lead to two main comments. First,the precision reached by the system is very satisfying and secondly the number of iterations is very small (between 1 and 2). In fact, the precision depends a lot on the velocity of the different motions. Here the maximal linear velocity of the robot was 50 cm/s. 5 Conclusion We have presented in this paper a method to steer a robot with one trailer from its initial configuration to a goal given in input of the problem. This method is based on an iterative approach combining open loop and close loop controls. It has been shown robust with respect to a large range of perturbation models. This robustness mainly comes from the topological property of the steering method introduced in [17]. Even if the method does not make the robot converge exactly to the goal, the precision reached during real experiments is very satisfying. Table 1: System A: initial and final configurations,gaps between the first and second reached configurations and the goal Table 2: System B: initial and final configurations,gaps between the first and second reached configurations and the goal References [1]M. K. Bennani et P. Rouchon. Robust stabilization of flat and chained systems. in European Control Conference,1995. [2]R.W. Brockett. Asymptotic stability and feedback stabilization. in Differential Geometric Control Theory,R.W. Brockett, R.S. Millman et H.H. Sussmann Eds,1983. [3]C. Canudas de Wit, O.J. Sordalen. Exponential stabilization of mobile robots with non holonomic constraints.IEEE Transactions on Automatic Control,Vol. 37, No. 11, 1992. [4]J. M. Coron. Global asymptotic stabilization for controllable systems without drift. in Mathematics of Control, Signals and Systems, Vol 5, 1992. [5]A. De Luca, G. Oriolo et C. Samson. Feedback control of a nonholonomic car-like robot, "Robot motion planning and control". J.P. Laumond Ed., Lecture Notes in Control and Information Sciences, Springer 'Verlag, to appear. [6]R. M. DeSantis. Path-tracking for a tractor-trailerlike robot. in International Journal of Robotics Research,Vol 13, No 6, 1994. [7]A. Hemami, M. G. Mehrabi et R. M. H. Cheng. Syntheszs of an optimal control law path trackang an mobile robots. in Automatica, Vol 28, No 2, pp 383-387, 1992. [8] Y. Kanayama, Y. Kimura, F. Miyazaki et T.Nogushi.A stable tracking control method for an autonomous mobile robot. in IEEE International Conference on Robotics and Automation, Cincinnati, Ohio, 1990. [9] F. Lamiraux.Robots mobiles ci remorque : de la planification de chemins d: l ' e x h t i o n de mouuements,PhD Thesis N7, LAAS-CNRS, Toulouse, September 1997. [l0] P. Morin et C. Samson. Application of backstepping techniques to the time-varying exponential stabitisation of chained form systems. European Journal of Control, Vol 3, No 1, 1997. [11] J. B. Pomet. Explicit design of time-varying stabilizang control laws for a class of controllable systems without drift. in Systems and Control Letters, North [12] M. Sampei, T. Tamura, T. Itoh et M. Nakamichi.Path tracking control of trailer-like mobile robot. in IEEE International Workshop on Intelligent Robots and Systems IROS, Osaka, Japan, pp 193-198, 1991. [13] C. Samson. Velocity and torque feedback control of a nonholonomic cart. International Workshop in Adaptative and Nonlinear Control: Issues in Robotics, Grenoble, France, 1990. [14] C. Samson. Time-varying feedback stabilization of carlike wheeled mobile robots. in International Journal of Robotics Research, 12(1), 1993. [15] C. Samson. Control of chained systems. Application to path following and time-varying poznt-stabilization. in IEEE Transactions on Automatic Control, Vol 40,No 1, 1995. [16] C. Samson et K. Ait-Abderrahim. Feedback control of a nonholonomic wheeled cart zncartesaan space.in IEEE International Conference on Robotics and Automation, Sacramento, California, pp 1136-1141,1991. [17] S. Sekhavat, F. Lamiraux, J.P. Laumond, G. Bauzil and A. Ferrand. Motion planning and control for Hilare pulling a trader: experzmental issues. IEEE Int. Conf. on Rob. and Autom., pp 3306-3311, 1997. [18] O.J. Splrdalen et 0. Egeland. Exponential stabzlzsation of nonholonomic chained systems. in IEEE Transactions on Automatic Control, Vol 40, No 1, 1995. Bolland, Vol 18, pp 147-158, 1992. 一種實(shí)用的辦法--帶拖車移動(dòng)機(jī)器人的反饋控制 F. Lamiraux and J.P. Laumond 拉斯，法國國家科學(xué)研究中心 法國圖盧茲 {florent ,jpl}@laas.fr 摘 要 本文提出了一種有效的方法來控制帶拖車移動(dòng)機(jī)器人。軌跡跟蹤和路徑跟蹤這兩個(gè)問題已經(jīng)得到解決。接下來的問題是解決迭代軌跡跟蹤。并且把擾動(dòng)考慮到路徑跟蹤內(nèi)。移動(dòng)機(jī)器人Hilare的實(shí)驗(yàn)結(jié)果說明了我們方法的有效性。 1引言 過去的8年，人們對(duì)非完整系統(tǒng)的運(yùn)動(dòng)控制做了大量的工作。布洛基[2]提出了關(guān)于這種系統(tǒng)的一項(xiàng)具有挑戰(zhàn)性的任務(wù)，配置的穩(wěn)定性，證明它不能由一個(gè)簡(jiǎn)單的連續(xù)狀態(tài)反饋。作為替代辦法隨時(shí)間變化的反饋[10,4,11,13,14,15,18]或間斷反饋[3]也隨之被提出。從 [5] 移動(dòng)機(jī)器人的運(yùn)動(dòng)控制的一項(xiàng)調(diào)查可以看到。另一方面，非完整系統(tǒng)的軌跡跟蹤不符合布洛基的條件，從而使其這一個(gè)任務(wù)更為輕松。許多著作也已經(jīng)給出了移動(dòng)機(jī)器人的特殊情況的這一問題[6,7,8,12,16]。 所有這些控制律都是工作在相同的假設(shè)下：系統(tǒng)的演變是完全已知和沒有擾動(dòng)使得系統(tǒng)偏離其軌跡。很少有文章在處理移動(dòng)機(jī)器人的控制時(shí)考慮到擾動(dòng)的運(yùn)動(dòng)學(xué)方程。但是[1]提出了一種有關(guān)穩(wěn)定汽車的配置，有效的矢量控制擾動(dòng)領(lǐng)域，并且建立在迭代軌跡跟蹤的基礎(chǔ)上。 存在的障礙使得達(dá)到規(guī)定路徑的任務(wù)變得更加困難，因此在執(zhí)行任務(wù)的任何動(dòng)作之前都需要有一個(gè)路徑規(guī)劃。 在本文中，我們?cè)诘壽E跟蹤的基礎(chǔ)上提出了一個(gè)健全的方案，使得帶拖車的機(jī)器人按照規(guī)定路徑行走。該軌跡計(jì)算由規(guī)劃的議案所描述[17] ，從而避免已經(jīng)提交了輸入的障礙物。在下面，我們將不會(huì)給出任何有關(guān)規(guī)劃的發(fā)展，我們提及這個(gè)參考的細(xì)節(jié)。而且，我們認(rèn)為，在某一特定軌跡的執(zhí)行屈服于擾動(dòng)。我們選擇的這些擾動(dòng)模型是非常簡(jiǎn)單，非常一般。它存在一些共同點(diǎn)[1]。 本文安排如下：第2節(jié)介紹我們的實(shí)驗(yàn)系統(tǒng)Hilare及其拖車：兩個(gè)連接系統(tǒng)將被視為（圖1） 。第3節(jié)處理控制方案及分析的穩(wěn)定性和魯棒性。在第4節(jié)，我們介紹本實(shí)驗(yàn)結(jié)果 。 圖1帶拖車的Hilare 2 系統(tǒng)描述 Hilare是一個(gè)有兩個(gè)驅(qū)動(dòng)輪的移動(dòng)機(jī)器人。拖車是被掛在這個(gè)機(jī)器人上的，確定了兩個(gè)不同的系統(tǒng)取決于連接設(shè)備：在系統(tǒng)A的拖車拴在機(jī)器人的車輪軸中心線上方（圖1 ，頂端），而對(duì)系統(tǒng)B是栓在機(jī)器人的車輪軸中心線的后面（圖1 ，底部)。 A對(duì)B來說是一種特殊情況，其中 = 0 。這個(gè)系統(tǒng)不過單從控制的角度來看，需要更多的復(fù)雜的計(jì)算。出于這個(gè)原因，我們分開處理掛接系統(tǒng)。兩個(gè)馬達(dá)能夠控制機(jī)器人的線速度和角速度（，)。除了這些速度之外，還由傳感器測(cè)量，而機(jī)器人和拖車之間的角度，由光學(xué)編碼器給出。機(jī)器人的位置和方向（,,）通過整合前的速度被計(jì)算。有了這些批注，控制系統(tǒng)B是： （1） 3 全球控制方案 3.1目的 當(dāng)考慮到現(xiàn)實(shí)的系統(tǒng)，人們就必須要考慮到在運(yùn)動(dòng)的執(zhí)行時(shí)產(chǎn)生的擾動(dòng)。 這可能有許多的來源，像有缺陷的電機(jī)，輪子的滑動(dòng)，慣性的影響... 這些擾動(dòng)可以被設(shè)計(jì)通過增加一個(gè)周期在控制系統(tǒng)（1） ，得到一個(gè)新的系統(tǒng)的形式 在上式中可以是確定性或隨機(jī)變量。 在第一種情況下，擾動(dòng)僅僅是由于系統(tǒng)演化的不規(guī)則，而在第二種情況下，它來自于該系統(tǒng)一個(gè)隨機(jī)行為。我們將看到后來，這第二個(gè)模型是一個(gè)更適合我們的實(shí)驗(yàn)系統(tǒng)。 為了引導(dǎo)機(jī)器人，從一開始就配置了目標(biāo)，許多工程認(rèn)為擾動(dòng)最初只是機(jī)器人和目標(biāo)之間的距離，但演變的系統(tǒng)是完全眾所周知的。為了解決這個(gè)問題，他們?cè)O(shè)計(jì)了一個(gè)可輸入的時(shí)間-狀態(tài)函數(shù)，使目標(biāo)達(dá)到一個(gè)漸近穩(wěn)定平衡的閉環(huán)系統(tǒng)?，F(xiàn)在，如果我們介紹了先前定義周期在這個(gè)閉環(huán)系統(tǒng)，我們不知道將會(huì)發(fā)生什么。但是我們可以猜想，如果擾動(dòng)很小、是確定的、在平衡點(diǎn)（如果仍然還有一個(gè)）將接近目標(biāo)，如果擾動(dòng)是一個(gè)隨機(jī)變數(shù)，平衡點(diǎn)將成為一個(gè)平衡的子集。 但是，我們不知道這些新的平衡點(diǎn)或子集的位置。 此外，在處理障礙時(shí)，隨時(shí)間變化的方法不是很方便。他們只能使用在附近的目標(biāo)，這附近要適當(dāng)界定，以確保無碰撞軌跡的閉環(huán)系統(tǒng)。請(qǐng)注意連續(xù)狀態(tài)反饋不能適用于真實(shí)情況下的機(jī)器人，因?yàn)殚g斷的速度導(dǎo)致無限的加速度。 我們建議達(dá)成某一存在障礙特定配置的方法如下。我們首先在當(dāng)前的配置和使用自由的碰撞議案所描述[17]目標(biāo)之間建立一個(gè)自由的碰撞路徑，然后，我們以一個(gè)簡(jiǎn)單的跟蹤控制率執(zhí)行軌跡。在運(yùn)動(dòng)結(jié)束后，因?yàn)檫@一目標(biāo)的各種擾動(dòng)機(jī)器人從來沒有完全達(dá)到和目標(biāo)的軌跡一致，而是這一目標(biāo)的左右。如果達(dá)到配置遠(yuǎn)離目標(biāo)，我們計(jì)算另一個(gè)我們之前已經(jīng)執(zhí)行過的一個(gè)軌跡。 現(xiàn)在我們將描述我們的軌跡跟蹤控制率，然后給出我們的全球迭代方法的魯棒性問題。 3.2軌跡跟蹤控制率 在這一節(jié)中，我們只處理系統(tǒng)A。對(duì)系統(tǒng)B容易計(jì)算（見第3.4節(jié)）。