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外文翻译AnadaptivedynamiccontrollerforautonomousmobilerobotrajectorytrackingabstractThispaperproposesanadaptivecontrollertoguideanunicycle-likemobilerobot duringtrajectorytracking.Initially,thedesiredvaluesofthelinearandangular velocitiesaregenerated,consideringonlythekinematicmodeloftherobot.Next,suchvaluesareprocessedtocompensatefortherobotdynamics,thusgeneratingthe commandsoflinearandangularvelocitiesdeliveredtotherobotactuators.The parameterscharacterizingtherobotdynamicsareupdatedon-line,thusproviding smallererrorsandbetterperformanceinapplicationsinwhichtheseparameterscan vary,suchasloadtransportation.Thestabilityofthewholesystemisanalyzedusing Lyapunovtheory,andthecontrolerrorsareprovedtobeultimatelybounded.Simulationandexperimentalresultsarealsopresented,whichdemonstratethegood performanceoftheproposedcontrollerfortrajectorytrackingunderdifferentload conditions.1.Introduction Amongdifferentmobilerobotstructures,unicycle-likeplatformsarefrequently adoptedtoaccomplishdifferenttasks,duetotheirgoodmobilityandsimple configuration.Nonlinearcontrolforthistypeofrobothasbeenstudiedforseveral yearsandsuchrobotstructurehasbeenusedinvariousapplications,suchassurveillanceandfloorcleaning.Otherapplications,likeindustrialloadtransportation usingautomatedguidedvehicles(AGVs)automatichighwaymaintenanceand construction,andautonomouswheelchairs,alsomakeuseoftheunicycle-likestructure.Someauthorshaveaddressedtheproblemoftrajectorytracking,aquite importantfunctionalitythatallowsamobilerobottodescribeadesiredtrajectory whenaccomplishingatask. AnimportantissueinthenonlinearcontrolofAGVsisthatmostcontrollers designedsofararebasedonlyonthekinematicsofthemobilerobot. However,whenhigh-speedmovementsand/orheavyloadtransportationare required,itbecomesessentialtoconsidertherobotdynamics,inadditiontoits kinematics.Thus,somecontrollersthatcompensatefortherobotdynamicshavebeen proposed. Asanexample,FierroandLewis(1995)proposedacombinedkinematic/torque controllawfornonholonomicmobilerobotstakingintoaccountthemodeledvehicle dynamics.Thecontrolcommandstheyusedweretorques,whicharehardtodealwithwhenregardingmostcommercialrobots.Moreover,onlysimulationresultswere reported.FierroandLewis(1997)alsoproposedarobust-adaptivecontrollerbasedon neuralnetworkstodealwithdisturbancesandnon-modeleddynamics,although notreportingexperimentalresults.DasandKar(2006)showedanadaptivefuzzy logic-basedcontrollerinwhichtheuncertaintyisestimatedbyafuzzylogicsystem anditsparametersweretunedon-line.Thedynamicmodelincludedtheactuator dynamics,andthecommandsgeneratedbythecontrollerwerevoltagesfortherobot motors. TheNeuralNetworkswereusedforidentificationandcontrol,andthecontrol signalswerelinearandangularvelocities,buttherealtimeimplementationoftheirsolutionrequiredahigh-performancecomputerarchitecturebasedonamultiprocessor system. On the other hand, De La Cruz and Carelli(2006) proposed a dynamic model using linear and velocities as inputs,and showed the design of a trajectory tracking controller based on their model. One advantage of their controller is that its parameters are directly related to the robot parameters. However, if the parameters are not correctly identified or if they change with time,for example, due to load variation, the performance of their controller will be severely affected.To reduce performance degradation, on-line parameter adaptation becomes quite important in applications in which the robot dynamic parameters may vary, such as load transportation.It is also useful when the knowledge of the dynamic parameters is limited or does not exist at all.In this paper, an adaptive trajectory-tracking controller based on the robot dynamics is proposed, and its stability property is proved using the Lyapunov theory. The design of the controller was divided in two parts, each part being a controller itself. The first one is a kinematic controller, which is based on the robot kinematics,and the second one is a dynamic controller, which is based on the robot dynamics.The dynamic controller is capable of updating the estimated parameters, which are directly related to physical parameters of the robot. Both controllers working together form a complete trajectory-tracking controller for the mobile robot. The controller shave been designed based on the model of a unicycle-like mobile robot proposed by De La Cruz and Carelli A s-modification term is applied to the parameter- updating law to prevent possible parameter drift. The asymptotic stability of both the kinematic and the dynamic controllers is proven. Simulation results show that parameter drift does not arise even when the system works for a long period of time. Experimental results regarding such a controller are also presented and show that the proposed controller is capable of updating its parameters in order to reduce the tracking error. An experiment dealing with the case of load transportation is also presented, and the results show that the proposed controller is capable of guiding the robot to follow a desired trajectory with a quite small error even when its dynamic parameters change.The main contributions of the paper are: (I) the use of a dynamic model whose in put commands are velocities, which is usual in commercial mobile obots, while most of the works in the literature deals with torque commands; (2) the design of an adaptive controller with a s-modification term, which makes it robust, with the corresponding stability study for the whole adaptive control system; and (3) the presentation of experimental results showing the good performance of the controller in a typical industrial application, namely load transportation.2. Dynamic model In this section, the dynamic model of the unicycle-like mobile robot proposed by De La Cruz and Carelli (2006) is reviewed. Fig. 1depicts the mobile robot, its parameters and variables of interest. u and o are the linear and angular velocities developed by the robot, respectively, G is the center of mass of the robot, C is the position of the castor wheel, E is the location of a tool on board the robot, h is the point of interest with coordinates x and y in the XY plane, c is the robot orientation,and a is the distance between the point of interest and the central point of the virtual axis linking the traction wheels (point B). The complete mathematical model is written as.whr g nd r th drd vlu f th lnr nd ngulr vlt,respectively, and represent the input signals of the system.A vector of identified parameters and a vector of parametric uncertainties are associated with the above model of the mobile robot, which are, respectively.where dx and dy are functions of the slip velocities and the robot orientation, duand do are functions of physical parameters as mass, inertia, wheel and tire diameters,parameters of the motors and its servos, forces on the wheels, etc., and are consideredas disturbances.The equations describing the parameters h were firstly presented in, and arereproduced here for convenience. They are It is important to point out that a nonholonomic mobile robot must be oriented according to the tangent of the trajectory path to track a trajectory with small error. Otherwise, the control errors would increase. This is true because the nonholonomic platform restricts the direction of the linear velocity developed by the robot. So, if the robot orientation is not tangent to the trajectory, the distance to the desired position at each instant will increase. The fact that the control errors converge to a bounded value shows that robot orientation does not need to be explicitly controlled, and will be tangent to the trajectory path while the control errors remain small.3. Experimental results To show the performance of the proposed controller several experiments andsimulations were executed. Some of the results are presented in this section. The .proposed controller was implemented on a Pioneer 3-DX mobile robot, which admits linear and angular velocities as input reference signals, and for which the distance b inFig. 2 is nonzero. In the first experiment, the controller was initialized with the dynamic parametersof a Pioneer 2-DX mobile robot, weighing about 10 kg (which were obtained via identification). Both robots are shown in Fig.3,where the Pioneer 3-DX has a laser sensor weighing about 6 kg mounted on its platform, which makes its dynamics significantly different from that of the Pioneer2-DX.In the experiment, the robot starts at x=0.2m and y=0.0 m, and should follow aeircular trajectory of reference. The center of the reference circle is at x =0.0m and y=0.8 m. The reference trajectory starts at x=0.8m and y=0.8m and follows a circle having a radius of 0.8 m. After 50 s, the reference trajectory suddenly changes to a circle of radius 0.7 m. After that, the radius of the reference trajectory alternates between 0.7 and 0.8m each 60 s.presents the reference and the actual robot trajectories for a part of the experiment that includes a change in the trajectory radius, In this case, the parameter updating was active.shows the distance errors for experiments using the proposed controller, wit hand without parameter updating, to follow the described reference trajectory. The distance error is defined as the instantaneous distance between the reference and the robot position. Notice the high initial error, which is due to the fact that the reference trajectory starts at a point that is far from the initial robot position. First, the proposed controller was tested with no parameter updating. It can be seen in Fig. 5 that, in this .case, the trajectory tracking error exhibits a steady-state value of about 0.17 m, which does not vary even after the change in the radius of the reference trajectory. This figure also presents the distance error for the case in which the dynamic parameters are updated. By activating the parameter-updating, and repeating the same experiment,the trajectory tracking error achieves a much smaller value, in comparison with the case in which is no ig .3. the robpts uesd in the experiments.4. ConcusionAn adaptive trajectory-tracking controller for a unicycle-like mobile robot was designed and fully tested in this work. Such a controller is divided in two parts, which are based on the kinematic and dynamic models of the robot. The model on side red takes the linear and angular velocities as input reference signals, which is usual when regarding commercial mobile robots. It was considered a parameter-updating law for the dynamic part of the controller, improving the system performance. A s-modification term was included in the parameter up dating law to prevent possible parameter drift. Stability analysis based on Lyapunov theory was performed for both kinematic and dynamic controller. For the last one, stability was proved considering a parameter-updating law with and without the s-modification term.Experimental results were presented, and showed the good performance of the proposed controller for trajectory tracking when applied to an experimental mobile robot.A long-term simulation result was also presented to demonstrate that the updated parameters converge even if the system works for a long period of time. The results proved that the proposed controller is capable of tracking a desired trajectory with as mall distance error when the dynamic parameters are adapted. The importance of on-line parameter updating was illustrated for the cases where the robot parameters are. not exactly known or might change from task to task. A possible application for the proposed controller is to industrial AGVs used for load transportation, because on-line parameter adaptation would maintain small tracking error even in the case ofimportant changes in the robot load.一种用于自主移动机器人目标跟踪的自适应动态控制器摘要本文提出了一种自适应控制器来指导单轮移动机器人进行轨迹跟踪。在初始阶段，只考虑机器人的运动学模型，即可得到所需的线速度和角速度。然后，对这些值进行处理以补偿机器人的动力学，从而生成传递给机器人执行器的线速度和角速度命令。表征机器人动力学的参数是在线更新的，因此在这些参数可以变化的应用中，如负载运输，提供了更小的误差和更好的性能。利用李亚普诺夫理论分析了整个系统的稳定性，证明了控制误差是有界的。仿真和实验结果表明，该控制器在不同负载条件下具有良好的跟踪性能。1 .介绍在不同的移动机器人结构中，由于单环类平台具有良好的机动性和简单的配置，因此常被用于完成不同的任务。针对这类机器人的非线性控制研究已有多年，该机器人结构已应用于监视、地板清洗等诸多领域。其他应用，如使用自动导向车辆(AGVs)的工业负荷运输，自动公路维护和建设，以及自动轮椅，也使用了独轮车式的结构。一些作者已经解决了轨迹跟踪的问题，这是一个非常重要的功能，允许移动机器人在完成任务时描述所需的轨迹。agv非线性控制的一个重要问题是，目前设计的控制器大多只基于移动机器人的运动学。然而，当需要高速运动和/或重载运输时，除了考虑机器人的运动学外，还必须考虑机器人的动力学。因此，提出了一些补偿机器人动力学的控制器。Fierro和Lewis(1995)以非完整移动机器人为例，提出了一种考虑建模车辆动力学的运动学/转矩联合控制律，其控制指令为力矩，对于大多数商用机器人来说，力矩是难以处理的。此外,只报道。仿真结果Fierro和刘易斯(1997)也提出了一个基于神经网络的鲁棒自适应控制器来处理干扰和non-modeled动态,虽然不是报告实验结果。Das和冰斗(2006)显示一个自适应模糊控制器基于逻辑的模糊逻辑系统估计的不确定性和参数调优在线。动态模型包括执行器动力学，控制器生成的命令为机器人电机的电压。神经网络用于辨识和控制，控制信号为线速度和角速度，但其实时实现要求基于多处理器系统的高性能计算机体系结构。另一方面，De La Cruz和Carelli(2006)提出了一个以线性和速度为输入的动态模型，并展示了基于该模型的轨迹跟踪控制器的设计。其控制器的一个优点是其参数与机器人参数直接相关。但是，如果参数识别不正确，或者随着时间的推移而变化，例如由于负载的变化，会严重影响控制器的性能。为了减少性能下降，在线参数自适应在机器人动态参数变化的应用中变得非常重要，例如负载运输。当动态参数的知识有限或根本不存在时，它也很有用。本文提出了一种基于机器人动力学的自适应轨迹跟踪控制器，并用李亚普诺夫理论证明了其稳定性。控制器的设计分为两部分，每一部分都是控制器本身。第一个是基于机器人运动学的运动控制器，第二个是基于机器人动力学的动态控制器。动态控制器能够更新与机器人物理参数直接相关的估计参数。这两个控制器共同工作，形成了一个完整的移动机器人轨迹跟踪控制器。基于De La Cruz和Carelli提出的单环类移动机器人模型设计了控制器，并将s修正项应用于参数更新律中，以防止可能出现的参数漂移。证明了运动控制器和动态控制器的渐近稳定性。仿真结果表明，即使系统工作时间较长，也不会产生参数漂移。实验结果表明，该控制器具有较强的参数更新能力，能够有效地降低跟踪误差。实验结果表明，该控制器能够在动态参数变化的情况下，以较小的误差引导机器人沿预定轨迹运动。本文的主要贡献是:(I)使用了一个动态模型，该模型的put命令中包含速度，这在商用移动机器人中很常见，而文献中的大部分工作都是关于扭矩命令的;(2)设计了具有修改项的自适应控制器，使其具有鲁棒性，并对整个自适应控制系统进行了相应的稳定性研究;(3)实验结果表明，该控制器在典型的工业应用，即负荷输送中具有良好的性能。2. 动态模型本节对De La Cruz和Carelli(2006)提出的单环类移动机器人的动力学模型进行了综述。图1描述了移动机器人及其感兴趣的参数和变量。u和o线速度和角速度都是由机器人,分别G是机器人的质心,C是castor轮的位置,E是一个工具的位置上机器人,h是感兴趣的点与XY平面的x和y坐标,C是机器人取向和兴趣点之间的距离和中心点的虚拟轴连接牵引轮(B点),写成完整的数学模型。分别代表系统的输入信号。上述移动机器人模型分别与确定的参数向量和参数不确定性向量相关联，分别为其中dx和dy是滑移速度和机器人方向的函数，是质量、惯量、车轮和轮胎直径、电机及其伺服参数、车轮上的力等物理参数的函数，被认为是扰动。文中首先给出了参数h的描述方程，为了方便起见，在此重新给出。他们是需要指出的是，非完整移动机器人必须根据轨迹轨迹的切线进行定向，才能跟踪误差较小的轨迹。否则，控制误差将会增加。这是真的，因为非完整平台限制了机器人所发展的线速度方向。所以，如果机器人的方向与轨迹不相切，那么每一瞬间到目标位置的距离就会增加。控制误差收敛到有界值的事实表明，机器人的姿态不需要显式控制，在控制误差较小的情况下与轨迹轨迹相切。3.实验结果为了验证该控制器的性能，进行了实验和仿真。本节将介绍一些结果。该控制器是在一个先进的3-DX移动机器人上实现的，该机器人以线速度和角速度作为输入参考信号，距离b在图中。2是零。在第一个实验中，控制器初始化为一个先锋2-DX移动机器人的动态参数，重约10公斤(通过辨识得到)。两个机器人如图3所示，其中先锋3-DX的平台上安装了一个重约6公斤的激光传感器，这使得它的动力学特性与先锋2- dx明显不同。在实验中，机器人从x=0.2m开始，y=0.0 m开始，应遵循参考的圆周轨迹。参考圆的中心在x =0.0m和y=0.8 m处。参考轨迹从x=0.8m, y=0.8m开始，沿半径为0.8m的圆运动。50秒后，参考轨迹突然变为半径为0.7 m的圆。之后，参考轨迹半径每60秒在0.7 0.8m之间变化。给出了部分实验机器人轨迹的参考和实际轨迹，其中包括轨迹半径的变化，在这种情况下，参数更新是主动的。给出了该控制器在不进行参数更新的情况下，跟踪所述参考轨迹的距离误差。距离误差定义为参考点到机器人位置的瞬时距离。注意初始误差很大，这是由于参考轨迹从远离初始机器人位置的点开始。首先，在不更新参数的情况下对该控制器进行了测试。从图5中可以看出，在这种情况下，轨迹跟踪误差的稳态值约为0.17 m，即使在参考轨迹半径改变后也没有变化。该图还显示了动态参数更新时的距离误差。通过激活参数更新，并重复相同的实验，与ig .3不存在的情况相比，轨迹跟踪误差的值要小得多。这些机器人在实验中使用。4. 结论设计了一种适用于单环类移动机器人的自适应轨迹跟踪控制器，并对其进行了全面测试。基于机器人的运动学和动力学模型，将该控制器分为两部分。侧边红色的模型以线速度和角速度作为输入参考信号，这在商用移动机器人中很常见。该方法被认为是控制器动态部分的参数更新律，提高了系统性能。为了防止参数漂移，在参数更新律中加入了s修正项。对运动控制器和动态控制器进行了基于李雅普诺夫理论的稳定性分析。最后，证明了考虑参数更新律的稳定性，其中包含和不包含s修正项。给出了实验结果，并将该控制器应用于实验移动机器人的轨迹跟踪中，取得了较好的效果。仿真结果表明，即使系统工作时间较长，更新后的参数也会收敛。实验结果表明，该控制器在动态参数调整的情况下，能够以较小的距离误差跟踪目标轨迹。阐述了机器人参数在线更新的重要性。不完全知道或可能在不同的任务之间更改。该控制器的一个可能的应用是用于工业agv的负荷运输，因为即使在机器人负荷发生重要变化的情况下，在线参数自适应也能保持较小的跟踪误差。RaspberryPi3 2016RaspberryPi3UserGuideByTed LebowskiCopyright 2016 Ted Lebowski -All rights reserved.This document is geared towards providing exact and reliable information in regards to the topic and issue covered. 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All trademarks and brands within this book are for clarifying purposes only and are the owned by the owners themselves, not affiliated with this document.EffectiveuseofTerminalcommandsnfthktfungtrmnlbngbltnvgturflsystem.Firstly,runthefollowingcommand:ls-la.Youshouldseesomethingsimilarto:TheIscommandliststhecontentsofthedirectorythatyouarecurrentlyinoryourpresentworkingdirectory.The-lacomponentofthecommandiswhatsknownasaflag.Flagsmodifythecommandthatsbeingrun.Inordertonavigatetootherdirectoriesthe changedirectorycommand,cdcanbeused.Youcanspecifythedirectorythatyouwanttobyeithertheabsoluteortherelativepath.Soifyouwantedtonavigatetothe/pi directory,youcouldeitherdocd/home/pi/orjustpiifyouarecurrentlyin/home.Therearesomespecialcasesthatmaybeuseful:actsasanaliasforyourhomedirectory,so/Desktopisthesameas/home/pi/Desktop;.and.arealiasesforthecurrentdirectoryandthe parentdirectoryrespectively,e.g.ifyouwerein/home/pi.Auto-detect command Rather than type every command, the terminal allows you to scroll through previous commands that you run by pressing the up or down keys on your keyboard. If you are writing the name of a file or directory as part of a command then the pressing tab :will attempt to Auto complete the name of what you are typing. For example, if you have a file in a directory called Test File Name then pressing tab after typing T will allow you to choose from all file and directory names beginning with an in the current directory,allowing you to choose Test File Name.Sudo privilege Some command that make permanent changes to the state of your system require you to have root privileges to run. The command temporarily gives your account (if you re not already logged in as root) the ability to run these commands, provided your user name is in a list of users . When you append sudo to the start of a command and press enter you will be asked for your password, if that is entered correctly then the command you want to run will be run using root privileges. Be careful, though some commands that require sudo to run can irreparably damage your system so be careful!Install Software or other utilities using apt-get Rather than using the Pi Store to download new software you can use the command apt-get, this is the packag
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