非完整轮式移动机器人运动规划与控制研究
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
近年来随着科技进步,通过利用移动机器人在复杂环境下进行探测和开发,人类的研究领域进一步扩展。将移动机器人作为实验平台,人们可以对人类思维模式进行探索,研究复杂智能体行为的产生。移动机器人的运动规划与运动控制问题涉及到认知科学、模式识别、非线性控制等领域,所得到的成果也将带动军事、交通、工业等机器人系统应用领域的发展。本文以轮式移动机器人作为研究对象,重点研究移动机器人的运动控制与运动规划问题,主要研究内容与创新点概括如下:
     1.研究非完整轮式移动机器人的轨迹跟踪控制问题。不同于路径跟踪问题,轨迹跟踪控制不仅具有空间位置要求,同时具有时间要求,即在特定的时间到达特定的位置,使机器人跟踪一条以时间为参数的轨迹。本文提出一种不存在控制奇异点的轨迹跟踪方法。该方法并不是直接跟踪姿态角,而是根据当前的侧向误差设计一个引导角作为期望姿态,随着侧向误差的收敛该引导角也逐渐趋向于姿态角,以此实现位姿跟踪。将引导角作为虚拟输入,结合Backstepping方法设计了基于移动机器人运动学模型的轨迹跟踪控制律,并给出了参数选取条件,然后将基于运动学模型的控制律进行扩展,考虑到外部扰动的影响,设计了基于动力学模型的控制律。最后通过仿真验证了所设计的控制律的有效性。
     2.研究非完整轮式移动机器人的路径跟踪控制问题。路径跟踪的目标是控制移动机器人跟踪一条几何曲线而无时间要求。提出一种跟踪参数曲线路径的方法。以路径上期望位置为原点建立路径坐标系,并在路径坐标系中计算跟踪误差,设计一个引导角作为期望的角度误差,可以使侧向误差随着角度误差一起收敛,结合Backstepping设计了路径跟踪控制律以及路径参数的更新律,并分析了参数选取应满足的条件,可以使跟踪误差收敛。最后通过仿真验证了所提出方法的有效性。
     3.研究了包含模型不确定性的轮式移动机器人的运动控制问题。由于移动机器人的物理参数难以精确测定,因此首先假设除了车轮半径和和两驱动轮间距外,机器人其余的物理参数均为未知,基于模糊系统逼近非线性函数的能力解决模型不确定性带来的困难,并以移动机器人的轨迹跟踪控制为例,设计了控制律和模糊系统权值矩阵的更新律,通过仿真验证了所提出方法的有效性。然后进一步假设机器人所有物理参数均为未知,结合自适应Baskstepping和模糊系统设计了轨迹跟踪控制律、未知参数的自适应律和模糊系统权值矩阵的更新律,并通过仿真验证了所提出方法的有效性。
     4.提出一种改进的考虑加速度限制的移动机器人运动规划方法。在移动机器人跟踪预先规划轨迹的过程中,不可避免会产生跟踪误差。导致误差产生的原因之一是规划路径的曲率不连续,产生跟踪误差的另一个原因是车轮的打滑和侧滑。在移动机器人运动过程中,车轮与地面间的最大摩擦力决定了机器人运动过程中的加速度限制。包括机器人加速减速运动时的切向加速度及曲线运动时的侧向加速度。当加速度超出限制时,轮胎与地面间发生滑移,产生跟踪误差。给定规划起止位姿和速度,首先基于三次Bezier曲线进行了路径的规划。然后根据受到的最大加速度限制规划时间最优的最大允许速度轨线。对于可能会出现的规划速度轨线在起点和终点处的速度低于规划目标值的情况,给出了进一步的规划方法。仿真结果验证了该方法的有效性和求解的快速性。
With recent advances in technology, the fields of human research have been furtherexpanded through the use of mobile robot in a complex environment. People can explore thehuman thinking mode and discuss the generation of complex agent behaviors by taking themobile robot as an experimental platform. The motion planning and motion control issues of themobile robot are related to cognitive science, pattern recognition, nonlinear control, and otherfields. The resulting outcomes will also promote the development of military, transportation andindustrial robot system applications. In this dissertation, the motion planning and control forwheeled mobile robots is considered. Major researches and innovations are summarized asfollows:
     1. The trajectory tracking problem of nonholonomic wheeled mobile robots is considered.Unlike the path following problem, the trajectory tracking problem not only have the spatialposition requirements, but also have time requirements, i.e., to control the mobile robot reachesa specific location at a specific time. A novel trajectory tracking control method based on thedynamic model of a wheeled mobile robot is proposed. Tracking errors between the referenceand the real postures are calculated in the robot body coordinate system. Then the guidanceangle is designed by analyzing the relationship between lateral and angular errors. Thekinematics tracking controller is developed with the backstepping approach by taking theguidance angle as a virtual input. Parameter selection criterion for the controller is alsoinvestigated. By taking the external disturbances into account, a torque controller based on thedynamic model is derived. Simulation results verify the effectiveness of the proposed method.
     2. The problem of path following of nonholonomic wheeled mobile robots is considered.The goal of path following is to control the mobile robot to follow a geometric curve withoutany time requirements. A control scheme is proposed for the following of parametric curves. Tracking errors between the actual and desired postures are first calculated in the pathcoordinate system. The origin of the path frame is fixed to the desired poison on the referencepath. The guidance angle is assigned by analyzing the relationship between lateral and angularerror in the path frame. The path following controller and the update law of the path parameterare designed by using the Lyapunov direct method and backstepping technique. Simulationresults following a Bezier curve path demonstrate the effectiveness of the method.
     3. The control problem of wheeled mobile robots with model uncertainties is considered.Since the actual physical parameters of the mobile robot are difficult to determine accurately, itis first assumed that the physical parameters of the robot were assumed to be unknown exceptthe wheel radius and the distance between the two driving wheels. The problem is solved byusing the nonlinear approximation capacity of the fuzzy system. And by taking the trajectorytracking problem as an example, an integrated controller based on the dynamics model of themobile and the update law of fuzzy weight matrix are designed. Then it is further assumed thatall the physical parameters of the mobile robot are unknown. The control law is designed byusing the adaptive backstepping and the fuzzy approach. The adaptive law of unknownparameters and fuzzy weight matrix are also given.. The effectiveness of the proposed method isverified by simulation results.
     4. Proposed an improved method for mobile robot motion planning without violatingacceleration limits. When a mobile robot tracking a trajectory planned in advance, willinevitably produce tracking errors. One of the causes of the tracking error is that the curvature ofthe planned path is not continuous. Another reason for the generation of tracking error is theslippage of the driving wheels. When the acceleration limits are exceeded, slippage occursbetween the tire and the ground, resulting in tracking error. The acceleration limits aredetermined by the friction between the wheels and the ground. The tangential acceleration isresponsible for the change in the robot’s velocity. The radical acceleration is due to the curvatureof the path. Given the terminal posture sand velocities, the cubic Bezier curve is employed forpath planning. Then the maximum allowable velocity profile can be calculated according to theacceleration limits while the acceleration limits can be determined by the surface frictionbetween the wheels and the ground. Simulation results show the effectiveness and quickness ofthe proposed algorithm.
引文
[1] Hagras H A. A hierarchical type-2fuzzy logic control architecture for autonomous mobilerobots. IEEE Transactions on Fuzzy Systems,2004,12(4):524-539.
    [2] Brooks R A. A robust layered control system for a mobile robot. IEEE Journal of Roboticsand Automation,1986,2(1):14-23.
    [3] Arkin R C. Motor schema—based mobile robot navigation. The International journal ofrobotics research,1989,8(4):92-112.
    [4] Gat E. Integrating planning and reacting in a heterogeneous asynchronous architecture forcontrolling real-world mobile robots. In Proceedings of AAAI,1992:809-815.
    [5] Moravec H P, Elfes A. High resolution maps from wide angle sonar. In Proceedings ofIEEE International Conference on Robotics and Automation, IEEE,1985,2:116-121.
    [6] Kuipers B, Byun Y T. A robot exploration and mapping strategy based on a semantichierarchy of spatial representations. Robotics and autonomous systems,1991,8(1):47-63.
    [7] Chatila R, Laumond J. Position referencing and consistent world modeling for mobilerobots. In Proceedings of IEEE International Conference on Robotics and Automation,IEEE,1985,2:138-145.
    [8] Avots D, Lim E, Thibaux R, et al. A probabilistic technique for simultaneous localizationand door state estimation with mobile robots in dynamic environments. In Proceedings ofIEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE,2002,1:521-526.
    [9] Thrun S. Robotic mapping: A survey. Technical Report. CMU-CS-02-111, CarnegieMellon University,2002.
    [10] H hnel D, Schulz D, Burgard W. Mobile robot mapping in populated environments.Advanced Robotics,2003,17(7):579-597.
    [11] Thrun S, Liu Y, Koller D, et al. Simultaneous localization and mapping with sparseextended information filters. The International Journal of Robotics Research,2004,23(7-8):693-716.
    [12] Thrun S, Burgard W, Fox D. A probabilistic approach to concurrent mapping andlocalization for mobile robots. Autonomous Robots,1998,5(3-4):253-271.
    [13] Linaker F, Ishikawa M. Real-time appearance-based Monte Carlo localization. Roboticsand Autonomous Systems,2006,54(3):205-220.
    [14] Leonard J J, Durrant-Whyte H F. Mobile robot localization by tracking geometricbeacons. IEEE Transactions on Robotics and Automation,1991,7(3):376-382.
    [15] Hashimoto M, Kawashima H, Oba F. A multi-model based fault detection and diagnosisof internal sensors for mobile robot. In Proceedings of IEEE/RSJ InternationalConference on Intelligent Robots and Systems, IEEE,2003,4:3787-3792.
    [16] Verma V, Gordon G, Simmons R, et al. Real-time fault diagnosis [robot fault diagnosis].IEEE Transactions on Robotics&Automation,2004,11(2):56-66.
    [17] Duan Z H, Cai Z X. Fault diagnosis for mobile robots based on BPR and particle filter.Chinese Journal of Electronics,2006,15(4A):929-932.
    [18]段琢华,蔡自兴,于金霞,等.基于粒子滤波器的移动机器人惯导传感器故障诊断.中南大学学报:自然科学版,2005,36(4):642-647.
    [19] Murphy R R, Hershberger D. Handling sensing failures in autonomous mobile robots.The International Journal of Robotics Research,1999,18(4):382-400.
    [20] Brockett R W. Asymptotic stability and feedback stabilization. Differential GeometricControl Theory, Boston: Birkhauser,1983:181-191.
    [21] De Luca A, Oriolo G, Vendittelli M. Control of wheeled mobile robots: An experimentaloverview. RAMSETE, Berlin: Springer,2001:181-226.
    [22] Sontag E D. Stability and stabilization: discontinuities and the effect of disturbances.Nonlinear analysis, differential equations and control. Springer Netherlands,1999:551-598.
    [23] Lafferriere G A, Sontag E D. Remarks on control Lyapunov functions for discontinuousstabilizing feedback. In Proceedings of IEEE Conference on Decision and Control,1991:1115-1120.
    [24] Indiveri G. Kinematic time-invariant control of a2D nonholonomic vehicle. InProceedings of the38th IEEE Conference on Decision and Control,1999,3:2112-2117.
    [25] Aicardi M, Casalino G, Bicchi A, et al. Closed loop steering of unicycle like vehicles viaLyapunov techniques. IEEE Robotics&Automation Magazine,1995,2(1):27-35.
    [26]杨雪,唐功友,盖绍婷.自主式智能体有限时间停车问题及控制策略设计.控制与决策,2013,28(6):953-956.
    [27] Samson C. Time-varying feedback stabilization of car-like wheeled mobile robots. TheInternational journal of robotics research,1993,12(1):55-64.
    [28] Coron J M. Global asymptotic stabilization for controllable systems without drift.Mathematics of Control, Signals and Systems,1992,5(3):295-312.
    [29] Pomet J B. Explicit design of time-varying stabilizing control laws for a class ofcontrollable systems without drift. Systems&Control Letters,1992,18(2):147-158.
    [30] Murray R M. Control of nonholonomic systems using chained forms. Fields InstituteCommunications,1993,1:219-245.
    [31] Teel A R, Murray R M, Walsh G C. Non-holonomic control systems: from steering tostabilization with sinusoids. International Journal of Control,1995,62(4):849-870.
    [32] Hespanha J P, Liberzon D, Morse A S. Logic-based switching control of a nonholonomicsystem with parametric modeling uncertainty. Systems&Control Letters,1999,38(3):167-177.
    [33] Hespanha J P, Stephen Morse A. Stabilization of nonholonomic integrators vialogic-based switching. Automatica,1999,35(3):385-393.
    [34] Kolmanovsky I, Reyhanoglu M, McClamroch N H. Switched mode feedback controllaws for nonholonomic systems in extended power form. Systems&Control Letters,1996,27(1):29-36.
    [35] Ji M, Zhang Z, Biswas G, et al. Hybrid fault adaptive control of a wheeled mobile robot.IEEE/ASME Transactions on Mechatronics,2003,8(2):226-233.
    [36] Morin P, Samson C. Exponential stabilization of nonlinear driftless systems withrobustness to unmodeled dynamics. Control, Optimisation and Calculus of Variations,1999,4:1-35.
    [37] Thuilot B, D'Andrea-Novel B, Micaelli A. Modeling and feedback control of mobilerobots equipped with several steering wheels. IEEE Transactions on Robotics andAutomation,1996,12(3):375-390.
    [38]马保离,霍伟.移动小车的路径跟踪与镇定.机器人,1995,17(6):358-362.
    [39]孙多青,霍伟,杨枭.含模型不确定性移动机器人路径跟踪的分层模糊控制.控制理论与应用,2004,21(4):489-494.
    [40]王栋耀,马旭东,戴先中.非时间参考的移动机器人路径跟踪控制.机器人,2004,26(3):198-203.
    [41]苑晶,黄亚楼,孙凤池.带拖车移动机器人全局路径跟踪控制.控制与决策,2007,22(10):1119-1124.
    [42] Yue M, Hu P, Sun W. Path following of a class of non-holonomic mobile robot withunderactuated vehicle body. IET Control Theory&Applications,2010,4(10):1898-1904.
    [43] Breivic M, Fossen T I. Path following for marine surface vessels. In Proceedings of2004MTS/IEEE Oceans Conference,2004:2282-2289.
    [44] Breivic M, Fossen T I. Principles of guidance-based path following in2D and3D. InProceedings of the44th IEEE Conference on Decision and Control, and the EuropeanControl Conference,2005:627–634.
    [45] Breivic M, Fossen T I. Guidance laws for planar motion control. In Proceedings of the47th IEEE Conference on Decision and Control,2008:570–577.
    [46]郑泽伟,霍伟,诸兵.非完整移动机器人全局路径跟踪控制.控制理论与应用,2012,29(6):741-746.
    [47] Kanayama Y, Kimura Y, Miyazaki F, et al. A stable tracking control scheme for anautonomous mobile robot, In Proceedings of IEEE International Conference onRobotics and Automation,1990:384-389.
    [48] Murray R M, Walsh G, Sastry S. Stabilization and tracking for nonholonomic controlsystems using time-varying state feedback//IFAC SYMPOSIA SERIES. PERGAMONPRESS,1993:109-114.
    [49] Walsh G, Tilbury D, Sastry S, et al. Stabilization of trajectories for systems withnonholonomic constraints. IEEE Transactions on Automatic Control,1994,39(1):216-222.
    [50] Samson C, Ait-Abderrahim K. Feedback control of a nonholonomic wheeled cart incartesian space. In Proceedings of the IEEE International Conference on Robotics andAutomation,1991:1136-1141.
    [51] Jiang Z P, Lefeber E, Nijmeijer H. Saturated stabilization and tracking of anonholonomic mobile robot. Systems&Control Letters,2001,42(5):327-332.
    [52] Yuan J, Tang G Y. Finite-time tracking control algorithms based on variable structure formobile robots. In Proceedings of the29th Chinese Control Conference,2010:419-423.
    [53] Oriolo G, D’Luca A, Vendittelli M. WMR control via dynamic feedback linearization:design, implementation, and experimental validation. IEEE Transactions on ControlSystems Technology,2002,10(6):835-852.
    [54] Jiang Z P, Nijmeijev H. Tracking control of mobile robots: A case study in Backstepping.Automatica.1997,33(7):1393-1399.
    [55]吴卫国,陈辉堂,王月娟.移动机器人的全局轨迹跟踪控制.自动化学报,2001,21(3):326-331.
    [56]李世华,田玉平.非完整移动机器人的轨迹跟踪控制.控制与决策,2002,17(3):301-305.
    [57]董文杰,霍伟.链式系统的轨迹跟踪控制.自动化学报,2000,26(3):310-316.
    [58]董文杰,霍伟.受非完整约束移动机器人的跟踪控制.自动化学报,2000,26(01):1-6.
    [59] Kanellakopoulos I, Kokotovic P V, Morse A S. Systematic design of adaptive controllersfor feedback linearizable systems. IEEE Trans on Automatic Control,1991,36(11):1241-1253
    [60] Fierro R, Lewis F.L. Control of a nonholonomic mobile robot: backstepping kinematicsto dynamics. Journal of Robotic Systems,1997,14(3):149-163.
    [61] Fukao T, Nakagawa H, Adachi N. Adaptive tracking control of a nonholonomic mobilerobot. IEEE Transactions on Robotics and Automation,2000,16(5):609-615.
    [62]孙棣华,崔明月,李永福.具有参数不确定性的轮式移动机器人自适应backstepping控制.控制理论与应用.2012,29(9):1198-1204.
    [63]崔明月,孙棣华,李永福,等.轮子纵向打滑条件下的移动机器人自适应跟踪控制.控制与决策,2013,28(5):664-670.
    [64] Dong W, Huo W. Adaptive stabilization of uncertain dynamic non-holonomic systems.International Journal of Control,1999,72(18):1689-1700.
    [65] Dong W. On trajectory and force tracking control of constrained mobile manipulatorswith parameter uncertainty. Automatica,2002,38(9):1475-1484.
    [66] Dong W, Liang Xu W, Huo W. Trajectory tracking control of dynamic non‐holonomicsystems with unknown dynamics. International Journal of Robust and NonlinearControl,1999,9(13):905-922.
    [67] Dong W, Huo W, Tso S K, et al. Tracking control of uncertain dynamic nonholonomicsystem and its application to wheeled mobile robots. IEEE Transactions on Roboticsand Automation,2000,16(6):870-874.
    [68] Huang J T. Adaptive tracking control of high-order non-holonomic mobile robot systems.IET Control Theory&Applications,2009,3(6):681-690.
    [69] Henaff P, Chocron O. Adaptive learning control in evolutionary design of mobile robotsIn proceedings of the2002IEEE International Conference on Systems, Man andCybernetics,2002,345-349.
    [70] Rossomando F G, Soria C, Carelli R. Neural network-based compensation control ofmobile robots with partially known structure. IET Control Theory&Applications,2012,16(2):1851-1860.
    [71] Wang Z P, Ge S S, Lee T H. Adaptive neural network control of a wheeled mobile robotviolating the pure nonholonomic constraint. In Proceedings of the43rd IEEEConference on Decision and Control,2004:5198-5203.
    [72] Fierro R, Lewis F L. Control of a nonholonomic mobile robot using neural networks.IEEE Transactions on Neural Networks,1998,9(4):589-600.
    [73] Fierro R, Lewis F L. Robust practical point stabilization of a nonholonomic mobile robotusing neural networks. Journal of Intelligent and Robotic Systems,1997,20(2-4):295-317.
    [74] Watanabe K, Tang J, Nakamura M, et al. A fuzzy-gaussian neural network and itsapplication to mobile robot control. IEEE Transactions on Control Systems Technology,1996,4(2):193–199.
    [75] Sousa C D, Hemerly E M, Galvao R. Adaptive control for mobile robot using waveletnetworks. IEEE Transactions on Systems, Man, and Cybernetics,2002,32(4):493-504.
    [75] Oh J S, Park J B, Choi Y H. Stable path tracking control of a mobile robot using awavelet based fuzzy neural network. International Journal of Control Automation andSystems,2005,3(4):552.
    [77] Yoo S J, Choi Y H, Park J B. Generalized predictive control based on self-recurrentwavelet neural network for stable path tracking of mobile robots: Adaptive learningrates approach. IEEE Trans on Circuits and Systems,2006,53(6):1381-1394.
    [78] Das T, Kar I N. Design and implementation of an adaptive fuzzy logic-based controllerfor wheeled mobile robots. IEEE Trans on Control Systems Technology,2006,14(3):501-510.
    [79] Hou Z G, Zou A M, Cheng L, et al. Adaptive control of an electrically drivennonholonomic mobile robot via backstepping and fuzzy approach. IEEE Trans onControl Systems Technology,2009,17(4):803-815.
    [80] Kim S H, Park C K, Harashima F. Adaptive fuzzy controller design for trajectorytracking of a2DOF wheeled mobile robot using genetic algorithm. In Proceedings ofthe1998IEEE/RSJ International Conference onIntelligent Robots and Systems,1998:1584-1589.
    [81] Kim S H, Park C, Harashima F. A self-organized fuzzy controller for wheeled mobilerobot using an evolutionary algorithm. IEEE Transactions on Industrial Electronics,2001,48(2):467-474.
    [82] Hwang C L, Chang L J, Yu Y S. Network-based fuzzy decentralized sliding-modecontrol for car-like mobile robots. IEEE Trans on Neural Networks,2007,54(1):574-585.
    [83]曹洋,方帅,徐心和.加速度约束条件下的非完整移动机器人运动控制.控制与决策,2006,21(2):193-196.
    [84] Ng J, Br unl T. Performance comparison of Bug navigation algorithms. Journal ofIntelligent and Robotic Systems,2007,50(1):73-84.
    [85] Lumelsky V J, Stepanov A A. Dynamic path planning for a mobile automaton withlimited information on the environmen. IEEE Transactions on Automatic Control,1986,31(11):1058-1063.
    [86] Lumelsky V J, Stepanov A A. Path-planning strategies for a point mobile automatonmoving amidst unknown obstacles for arbitrary shape. Algorithmica,1987,2(1-4):403-430.
    [87] Kamon I, Rimon E, Rivlin E. TangentBug: a range-sensor-based navigation algorithm.International Journal of Robotics Research,1998,17(9):934-953.
    [88] Xu Q L, Tang G Y. Vectorization path planning for autonomous mobile agent inunknown environment. Neural Computing and Applications,2013,23(7-8):2129-2135.
    [89]张纯刚,席裕庚.全局环境未知时基于滚动窗口的机器人路径规划.中国科学: E辑,2001,31(1):51-58.
    [90]席裕庚,张纯刚.一类动态不确定环境下机器人的滚动路径规划.自动化学报,2002,28(2):161-175.
    [91]康亮,赵春霞,郭剑辉.未知环境下改进的基于BUG算法的移动机器人路径规划.系统仿真学报,2009(17):5414-5422.
    [92] Barraquand J, Latombe J C. Robot motion planning: A distributed representationapproach. The International Journal of Robotics Research,1991,10(6):628-649.
    [93]普雷帕拉塔,沙莫斯.计算几何导论.庄心谷译.北京:科学出版社,1990.
    [94] Takahashi O, Schilling R J. Motion planning in a plane using generalized Voronoidiagrams. IEEE Transactions on Robotics and Automation,1989,5(2):143-150.
    [95] Canny J, Donald B. Simplified voronoi diagrams. Discrete&Computational Geometry,1988,3(1):219-236.
    [96] Jung D, Gupta K K. Octree-based hierarchical distance maps for collision detection. InProceedings of the1996IEEE International Conference on Robotics and Automation,1996,1:454-459.
    [97] Khatib O. Real-time obstacle avoidance for manipulators and mobile robots. InProceedings of the IEEE International Conference on Robotics and Automation,1985,2:500-505.
    [98] Khatib O. Real-time obstacle avoidance for manipulators and mobile robots. TheInternational Journal of Robotics Research,1986,5(1):90-98.
    [99] Borenstein J, Koren Y. The vector field histogram-fast obstacle avoidance for mobilerobots. IEEE Transactions on Robotics and Automation,1991,7(3):278-288.
    [100] Koren Y, Borenstein J. Potential field methods and their inherent limitations for mobilerobot navigation. In Proceedings of the1991IEEE International Conference onRobotics and Automation,1991:1398-1404.
    [101] A’Cosío F, Casta eda P. Autonomous robot navigation using adaptive potential fields.Mathematical and computer modelling,2004,40(9):1141-1156.
    [102] Ren J, McIsaac K A, Patel R V, Peters T M. A Potential field model using generalizedsigmoid functions. IEEE Transactions on Systems, Man, and Cybernetics,2007,37(2):477-484.
    [103] Russell S J, Norvig P. Artifiacial Intelligent: A Modern Approach (Third Edition). USA:Prentice Hall,2009.97-107,126-129.
    [104] Hart P E, Nilsson N J, Raphael B. A formal basis for the heuristic determination ofminimum cost paths. IEEE Trans of Systems Science and Cybernetics,1968,4(2):100-107.
    [105] Stentz A. Optimal and efficient path planning for partially-known environments. InProceedings of the1994IEEE International Conference on Robotics and Automation,1994:3310-3317.
    [106] Stentz A. The focussed D*algorithm for real-time replanning. In Proceedings of the14thInternational Joint Conference on Artificial Intelligence.1995:1652-1659.
    [107] Koenig S, Likhachev M. Improved fast replanning for robot navigation in unknownterrain. In Proceedings of the IEEE International Conference on Robotics andAutomation,2002:968-975.
    [108] Arkin R C. Behavior-based robotics. London: The MIT press,1998.
    [109] Holland J H. Adaptation in natural and artificial systems: an Introductory analysis withapplications to biology, Control, and Aritificial Intelligence. MA: The MIT Press,1992.1-158.
    [110] Sedighi K H, Ashenayi K, et al. Autonomous local path planning for a mobile robotusing a genetic algorithm. Congress on Evolutionary Computation,2004,2:1338~1345.
    [111] Ismail AL T, A Sheta, AI-Weshah M. A mobile robot path planning using geneticalgorithm in static environment. Journal of Computer Science,2008,4(4):341~344.
    [112] Colorni A, Dorigo M, Maniezzo V. Distributed optimization by ant colonies. InProceedings of the First European Conference on Artificial Life,1992:134-142.
    [113] Dorigo M, Maniezzo V, Colorni A. Ant system: optimization by a colony ofcooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics,1996,26(1):29-41.
    [114]朱庆保,张玉兰.基于栅格法的机器人路径规划蚁群算法.机器人,2005,27(2):132~136.
    [115]王沛栋,冯祖洪,黄新.一种改进的机器人路径规划蚁群算法.机器人,2008,30(6):554~560.
    [116] Wang P D, Tang G Y, Li Y, et al. Ant colony algorithm using endpoint approximationfor robot path planning. In Proceedings of the31st Chinese Control Conference,2012:4960-4965.
    [117] Kennedy J, Eberhart R. Particle swarm optimization. IEEE International Conferenceon Neural Networks, Perth,1995. USA: IEEE,1995:1942-1948.
    [118] Eberhart R, Kennedy J. A new optimizer using particle swarm theory. Proceedings ofthe16th International Symposium on Micro Machine and Human Science, Nagoya,Japan,1995.1995:39-43.
    [119]吴宪祥,郭宝龙,王娟.基于粒子群三次样条优化的移动机器人路径规划算法.机器人,2009,31(6):556-560.
    [120] Han S, Choi B S, Lee J M. A precise curved motion planning for a differential drivingmobile robot. Mechatronics,2008,18(9):486-494.
    [121] Wilde D K. Computing clothoid segments for trajectory generation. In Proceedings ofthe IEEE/RSJ International Conference on Intelligent Robots and Systems,2009:2440-2445.
    [122] Shanmugavel M, Tsourdos A, et al. Co-operative path planning of multiple UAVs usingDubins paths with clothoid arcs. Control Engineering Practice,2010,18(9):1084-1092.
    [123] Howard T M, Kelly A. trajectory and spline generation for all-wheel steering mobilerobots. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robotsand Systems,2006:4827-4832.
    [124] Lepetic M, Klancar G, et al. Time optimal path planning considering accelerationLimits, Robotics and Autonomous Systems.2003,45(3-4):199-210.
    [125] Jolly K G, Sreerama K R, Vijayakumar R. A Bezier curve based path planning in amulti-agent robot soccer system without violating the acceleration limits. Robotics andAutonomous Systems.2009,57(1):23-33.
    [126] Skrjanc I, Klancar G. Optimal cooperative collision avoidance between multiple robotsbased on Bernstein Bézier curves. Robotics and Autonomous Systems.2010,58(1):1-9.
    [127]马海涛.非完整轮式移动机器人的运动控制.合肥:中国科学技术大学,2009.
    [128]王牛,李祖枢,李永龙,等.带驱动直流电机两轮机器人运动系统仿真.系统仿真学报,2008,20(17):4633-4638.
    [129]吴克河,李为,柳长安,等.双轮驱动式移动机器人动力学控制.宇航学报,2006,27(2):272-275.
    [130] Sederberg T W. Computer aided geometric design. CAGD Course Notes, BrighamYoung University, Provo, UT,84602, April2007.
    [131] Lee H, Tomizuka M. Robust adaptive control using a universal approximator for SISOnonlinear systems. IEEE Transactions on Fuzzy Systems,2000,8(1):95–106.
    [132] Wang L X. Fuzzy systems are universal approximators. In Proceedings of IEEEInternational Conference on Fuzzy Systems,1992:1163-1170.
    [133] Wang L X, Mendel J M. Fuzzy basis functions, universal approximation, andorthogonal least-squares learning. IEEE Transactions on Neural Networks,1992,3(5):807-814.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700