智能车辆运动轨迹规划方法的研究
|
摘要
智能车辆是智能交通系统的重要组成部分，能显著提高行驶安全性和降低交通事故发生率，因而在军、民领域均具有重要的理论探索意义和工程实践价值。轨迹规划系统是智能车辆规划与控制系统的重要组成部分，与路径规划不同，路径规划生成的是静态无碰路径，而轨迹规划则是在路径规划的基础上考虑时间因素和车辆状态，不仅生成无碰路径还生成包括车辆行驶的各种状态参数和控制参数，如速度、加速度和行驶时间等。本文对智能车辆运动轨迹规划所涉及的相关内容：基于状态空间的轨迹规划方法，基于理性思维的自适应控制(Adaptive Control ofThought-Rational, ACT-R)认知模型的运动轨迹规划方法以及基于改进遗传算法的运动轨迹优化方法进行了较为系统和深入地研究。具体工作归纳如下：
(1)考虑时间因素，建立了一种基于状态空间的曲率连续轨迹规划方法。为了能够生成适应于不同行驶环境且曲率连续的行驶轨迹，本文在最优控制理论的基础上进行了改进，并建立了多目标动态评价函数；通过不同权重对轨迹特征影响规律的研究，获得了权重调整的有效方法。仿真结果表明：本文提出的基于状态空间的轨迹规划方法能够针对不同道路环境动态生成满足多种约束条件曲率连续的行驶轨迹。
(2)基于ACT-R认知模型，提出了一种考虑人的行为特性的轨迹规划方法。该方法将基于状态空间的轨迹规划方法和ACT-R认知模型结合起来，并以ACT-R认知模型为核心。首先由初始化模块生成初始权重集；然后用基于状态空间的轨迹规划方法生成轨迹，并将轨迹的特征值提取出来传给估计模块，在估计模块中对轨迹的特征值进行估计，判断轨迹的特征值是否满足约束条件，如果满足约束条件则将生成的轨迹返回给决策层；如果不满足，则通过权重调整模块对相关的权重进行调整，最终生成符合人的行为特性的行驶轨迹。仿真结果及模型车的试验验证表明：提出的方法能够生成具有人的行为特性，且满足多个约束条件的行驶轨迹，验证了本文方法的可行性。
(3)根据车辆动力学约束条件，发展了一种将B样条曲线和改进的遗传算法相结合对运动轨迹进行优化的方法。首先利用B样条曲线曲率连续和局部支撑性的性质对生成的轨迹参数化，利用已知轨迹上型值点位置反求控制点，确定B样条曲线的形状；然后基于改进遗传算法对参数化轨迹进行多目标优化，来实现B样条曲线参数的确定。具体优化目标包括：每段B样条曲线的始末型值点位置，相应型值点位置的车辆速度、加速度和相邻型值点之间的时间间隔。仿真结果表明：优化后的轨迹较优化前的轨迹能够更好地满足动力学约束条件。
(4)基于CarSim仿真软件，设计了一种对本文方法进行验证和分析的方法。在Matlab和ACT-R软件中由运动轨迹规划算法在采样周期内根据车辆将要行驶的环境信息对轨迹进行规划，并将规划的轨迹及轨迹的参数提供给CarSim理想控制器，用作动力学仿真软件中的路径和参数输入，理想控制器控制车辆模型在虚拟场景中沿着规划轨迹行驶，并将车辆行驶的轨迹和行驶过程中的各种响应参数反馈给运动轨迹规划算法，再由运动轨迹规划算法判断规划的轨迹及其参数是否满足约束条件，并对不满足约束条件的轨迹修正后重新输出轨迹，如此循环至仿真结束。分别进行了三组比较研究：本文方法和两种文献中路径规划方法的仿真比较；车辆以不同速度沿着规划出的稳态圆周轨迹、蛇形线轨迹和双移线轨迹行驶的仿真比较；以及仿真结果与文献实车试验结果的比较分析。结果表明：本文方法比文献路径规划方法更加有效可行，本文方法规划的车速比其它车速更能满足约束条件且仿真数据与文献实车试验数据吻合。
综上所述，本文研究了智能车辆的运动轨迹规划方法，并结合仿真软件CarSim和模型车试验等方法对提出的运动轨迹规划方法的可行性和有效性进行了验证，其研究结果可以为后续跟踪控制器提供连续有效的运动轨迹，便于控制器实现对车辆的自动控制。
Intelligent Vehicle (IV) is an important constituent of the Intelligent Transportation System (ITS).Intelligent Vehicle can improve the road traffic safety and reduce the traffic accidents remarkably, so ithas important theory searching significance and engineering practice value in military and civilterritory．The trajectory planning system is an important constituent of Intelligent Vehicle’s planningand control system. The trajectory planning is different from the path planning, the path planning onlygenerate the static collision-free path, while the trajectory planning not only generate collision-freepath but also relevant state parameters and control parameters, such as velocity, acceleration anddriving time, etc. The relevant contents of Intelligent Vehicle’s motion trajectory planning are studiedin this paper. The research contents include state space based trajectory planning method,ACT-R(Adaptive Control of Thought-Rational) based motion trajectory planning method and motiontrajectory optimized method. Concrete contents of the research have been summarized as follows:
(1) Considering the time factor, a state space based trajectory planning method is presented. Inorder to generate curvature-continous and dynamic suitable different driving environment’ trajectory,based on the optimal control theory an improved trajectory planning method is presented in this paper.The multi-objective dynamic cost function is built in the improved method. The influence law ofdifferent weights to trajectory’s features is studied and the effective weights regulation method isobtained. The simulation results showed that the state spaced based trajectory planning method couldgenerate dynamically adaptive different road environments’ driving trajectory. The trajectory iscurvature-continous with several constraints are obtained.
(2) Based on ACT-R cognitive model, a trajectory planning method with human’s behavioralcharacteristic is introduced. The state space based trajectory planning method and the ACT-Rcognitive model are contacted, the ACT-R cognitive model is the core of the method. Firstly, theinitialization module initializes the weight value set. Secondly, the trajectory is generated by the statespace based trajectory planning method and the trajectories’ feature values are extracted. Lastly, theevaluation module evaluates the trajectory’s feature values, make sure of the feature values underconstraints and return the trajectory. If the constraints are not obtained, the weight regulation moduleis used to regulate the weight value to generate the driving trajectory with human’s behavioralcharacteristic. The simulation results and model vehicle’s experiment results showed that the methodwas feasible. The driving trajectory generated by the method has human’s behavioral characteristicand several constraints are obtained.
(3) On the basis of the vehicle’s dynamic constraints, a motion trajectory optimized method combined the B-spline curve and improved genetic algorithm is presented. First, the B-spline curve’sproperties of curvature-continous and local support character are used to parameterize the generatedtrajecroy. In order to make sure of the B-spline curve’s shape, the data point positions are known tocalculate the control points. Then, the improved genetic algorithm is used to multiobjective optimizethe parameterized trajectory. The parameters that can confirm the B-spline curve’s shape areoptimized to satisfy the dynamic constraints. The optimized objectives include data points and relativedate points’ velocity, acceleration and the time interval between the adjacent data points. Thesimulation results showed that the optimized trajectories’ dynamic constraints was obtained andoptimized trajectory was better than the unoptimized trajectory.
(4) Based on the CarSim simulation software, a test method of this paper’s method is designed.The motion trajectory planning method generates the motion trajectory and the trajectories’parameters within the sampling period in the Matlab and ACT-R sofware. The generated trajectoryand trajectories’ parameters as CarSim’s path and parameter inputs are supplied to CarSim’s optimalcontroller. The optimal controller controls the vehicle model to drive on the planned trajectory invirtual scene simulation. The vehicle’s response parameters are extracted and reponsed to the motiontrajectory planning algorithm when the vehicle model driving on the planned trajectory. The trajectoryplanning algorithm estimates the parameters if under the constraints, if the trajectory is not under theconstraints, the trajectory planning algorithm modifies the trajectory and output the new trajectory.The cycle is done until the end of the simulation. Three group comparation studies were done to testthis paper’s method. Three couple comparation studies include the simulation comparation betweenthis paper’s method and two article’s path planning methods, the comparation of vehicle driving withdifferent velocities on the planned steady circular trajectory, serpentine curve trajectory and doubleshift line trajectory, the comparation between simulation results and other article’s experiment resultsof the real vehicle. The research results showed that this paper’s approach was more effective andfeasible than other article’s path planning method, the vehicle driving at the velocity planned by thispaper’s method could satisfy several constraints while the vehicle driving at other velocities could notsatisfy several constraints, the simulation datas were equal to the article’s real experiment datas.
In summary, this paper focus on the sudy of Intelligent Vehicle’s motion trajectory planningmethod, the CarSim simulation software and the model vehicle’s experiment are used to test thevalidity and feasibility of the motion trajectory planning method. The studied results can providecurvature-continous and effective motion trajectory for the controller, so that the controller couldauto-control the vehicle driving on the road.
(1)考虑时间因素，建立了一种基于状态空间的曲率连续轨迹规划方法。为了能够生成适应于不同行驶环境且曲率连续的行驶轨迹，本文在最优控制理论的基础上进行了改进，并建立了多目标动态评价函数；通过不同权重对轨迹特征影响规律的研究，获得了权重调整的有效方法。仿真结果表明：本文提出的基于状态空间的轨迹规划方法能够针对不同道路环境动态生成满足多种约束条件曲率连续的行驶轨迹。
(2)基于ACT-R认知模型，提出了一种考虑人的行为特性的轨迹规划方法。该方法将基于状态空间的轨迹规划方法和ACT-R认知模型结合起来，并以ACT-R认知模型为核心。首先由初始化模块生成初始权重集；然后用基于状态空间的轨迹规划方法生成轨迹，并将轨迹的特征值提取出来传给估计模块，在估计模块中对轨迹的特征值进行估计，判断轨迹的特征值是否满足约束条件，如果满足约束条件则将生成的轨迹返回给决策层；如果不满足，则通过权重调整模块对相关的权重进行调整，最终生成符合人的行为特性的行驶轨迹。仿真结果及模型车的试验验证表明：提出的方法能够生成具有人的行为特性，且满足多个约束条件的行驶轨迹，验证了本文方法的可行性。
(3)根据车辆动力学约束条件，发展了一种将B样条曲线和改进的遗传算法相结合对运动轨迹进行优化的方法。首先利用B样条曲线曲率连续和局部支撑性的性质对生成的轨迹参数化，利用已知轨迹上型值点位置反求控制点，确定B样条曲线的形状；然后基于改进遗传算法对参数化轨迹进行多目标优化，来实现B样条曲线参数的确定。具体优化目标包括：每段B样条曲线的始末型值点位置，相应型值点位置的车辆速度、加速度和相邻型值点之间的时间间隔。仿真结果表明：优化后的轨迹较优化前的轨迹能够更好地满足动力学约束条件。
(4)基于CarSim仿真软件，设计了一种对本文方法进行验证和分析的方法。在Matlab和ACT-R软件中由运动轨迹规划算法在采样周期内根据车辆将要行驶的环境信息对轨迹进行规划，并将规划的轨迹及轨迹的参数提供给CarSim理想控制器，用作动力学仿真软件中的路径和参数输入，理想控制器控制车辆模型在虚拟场景中沿着规划轨迹行驶，并将车辆行驶的轨迹和行驶过程中的各种响应参数反馈给运动轨迹规划算法，再由运动轨迹规划算法判断规划的轨迹及其参数是否满足约束条件，并对不满足约束条件的轨迹修正后重新输出轨迹，如此循环至仿真结束。分别进行了三组比较研究：本文方法和两种文献中路径规划方法的仿真比较；车辆以不同速度沿着规划出的稳态圆周轨迹、蛇形线轨迹和双移线轨迹行驶的仿真比较；以及仿真结果与文献实车试验结果的比较分析。结果表明：本文方法比文献路径规划方法更加有效可行，本文方法规划的车速比其它车速更能满足约束条件且仿真数据与文献实车试验数据吻合。
综上所述，本文研究了智能车辆的运动轨迹规划方法，并结合仿真软件CarSim和模型车试验等方法对提出的运动轨迹规划方法的可行性和有效性进行了验证，其研究结果可以为后续跟踪控制器提供连续有效的运动轨迹，便于控制器实现对车辆的自动控制。
Intelligent Vehicle (IV) is an important constituent of the Intelligent Transportation System (ITS).Intelligent Vehicle can improve the road traffic safety and reduce the traffic accidents remarkably, so ithas important theory searching significance and engineering practice value in military and civilterritory．The trajectory planning system is an important constituent of Intelligent Vehicle’s planningand control system. The trajectory planning is different from the path planning, the path planning onlygenerate the static collision-free path, while the trajectory planning not only generate collision-freepath but also relevant state parameters and control parameters, such as velocity, acceleration anddriving time, etc. The relevant contents of Intelligent Vehicle’s motion trajectory planning are studiedin this paper. The research contents include state space based trajectory planning method,ACT-R(Adaptive Control of Thought-Rational) based motion trajectory planning method and motiontrajectory optimized method. Concrete contents of the research have been summarized as follows:
(1) Considering the time factor, a state space based trajectory planning method is presented. Inorder to generate curvature-continous and dynamic suitable different driving environment’ trajectory,based on the optimal control theory an improved trajectory planning method is presented in this paper.The multi-objective dynamic cost function is built in the improved method. The influence law ofdifferent weights to trajectory’s features is studied and the effective weights regulation method isobtained. The simulation results showed that the state spaced based trajectory planning method couldgenerate dynamically adaptive different road environments’ driving trajectory. The trajectory iscurvature-continous with several constraints are obtained.
(2) Based on ACT-R cognitive model, a trajectory planning method with human’s behavioralcharacteristic is introduced. The state space based trajectory planning method and the ACT-Rcognitive model are contacted, the ACT-R cognitive model is the core of the method. Firstly, theinitialization module initializes the weight value set. Secondly, the trajectory is generated by the statespace based trajectory planning method and the trajectories’ feature values are extracted. Lastly, theevaluation module evaluates the trajectory’s feature values, make sure of the feature values underconstraints and return the trajectory. If the constraints are not obtained, the weight regulation moduleis used to regulate the weight value to generate the driving trajectory with human’s behavioralcharacteristic. The simulation results and model vehicle’s experiment results showed that the methodwas feasible. The driving trajectory generated by the method has human’s behavioral characteristicand several constraints are obtained.
(3) On the basis of the vehicle’s dynamic constraints, a motion trajectory optimized method combined the B-spline curve and improved genetic algorithm is presented. First, the B-spline curve’sproperties of curvature-continous and local support character are used to parameterize the generatedtrajecroy. In order to make sure of the B-spline curve’s shape, the data point positions are known tocalculate the control points. Then, the improved genetic algorithm is used to multiobjective optimizethe parameterized trajectory. The parameters that can confirm the B-spline curve’s shape areoptimized to satisfy the dynamic constraints. The optimized objectives include data points and relativedate points’ velocity, acceleration and the time interval between the adjacent data points. Thesimulation results showed that the optimized trajectories’ dynamic constraints was obtained andoptimized trajectory was better than the unoptimized trajectory.
(4) Based on the CarSim simulation software, a test method of this paper’s method is designed.The motion trajectory planning method generates the motion trajectory and the trajectories’parameters within the sampling period in the Matlab and ACT-R sofware. The generated trajectoryand trajectories’ parameters as CarSim’s path and parameter inputs are supplied to CarSim’s optimalcontroller. The optimal controller controls the vehicle model to drive on the planned trajectory invirtual scene simulation. The vehicle’s response parameters are extracted and reponsed to the motiontrajectory planning algorithm when the vehicle model driving on the planned trajectory. The trajectoryplanning algorithm estimates the parameters if under the constraints, if the trajectory is not under theconstraints, the trajectory planning algorithm modifies the trajectory and output the new trajectory.The cycle is done until the end of the simulation. Three group comparation studies were done to testthis paper’s method. Three couple comparation studies include the simulation comparation betweenthis paper’s method and two article’s path planning methods, the comparation of vehicle driving withdifferent velocities on the planned steady circular trajectory, serpentine curve trajectory and doubleshift line trajectory, the comparation between simulation results and other article’s experiment resultsof the real vehicle. The research results showed that this paper’s approach was more effective andfeasible than other article’s path planning method, the vehicle driving at the velocity planned by thispaper’s method could satisfy several constraints while the vehicle driving at other velocities could notsatisfy several constraints, the simulation datas were equal to the article’s real experiment datas.
In summary, this paper focus on the sudy of Intelligent Vehicle’s motion trajectory planningmethod, the CarSim simulation software and the model vehicle’s experiment are used to test thevalidity and feasibility of the motion trajectory planning method. The studied results can providecurvature-continous and effective motion trajectory for the controller, so that the controller couldauto-control the vehicle driving on the road.
引文
[1] Gharavi H, Prasad K. V, Oannou P. Scanning Advanced Automobile Technology[J].Proceedings of the IEEE,2007,95(2):328～333.
[2] Tsugawa S, Saito T, Hosaka A. Super Smart Vehicle System: Avcs Related Systems for theFuture[A]. In: IEEE Intelligence Vehicles Symposium[C]. Detroit, MI, USA,1992,132～137.
[3]李都厚,刘群,袁伟,等.疲劳驾驶与交通事故关系[J].交通运输工程学报,2010,10(2):104～109.
[4]李舜酩,沈峘,毛建国,等.智能车辆发展及其关键技术展望[J].传感器与微系统,2009,1(28):1～4.
[5] Li L, Wen D, Zheng N N, et al. Cognitive Cars: A New Frontier for ADAS Research[J]. IEEETransactions on Intelligent Transportation Systems,2011,99(7):1～13.
[6]马强.关于智能交通规划的若干问题[J].交通发展,2004,4:33～35.
[7]王荣本,李斌,储江伟,等.世界智能车辆行驶安全保障技术的研究进展[J].公路交通科技,2002,2(19):112～117.
[8]王笑京,沈鸿飞,汪林.中国智能交通系统发展战略研究[J].交通运输系统工程与信息,2006,4(6):9～12.
[9] Sheng W. H, Yang Q, Guo Y. Cooperative Driving Based On Inter-Vehicle Communications:Experimental Platform and Algorithm[A]. In: International Conference on Intelligent Robotsand Systems[C]. Beijing, China,2006,5073～5078.
[10]孙振平.自主驾驶汽车智能控制系统[D],[工学博士学位论文].长沙:国防科学技术大学,2004.
[11] Pappas G J. Hierarchically Consistent Control Systems[J]. IEEE Transactions on AutomaticControl,2000,45(6):1144～1160.
[12]欧青立,何克忠.室外智能移动机器人的发展及其关键技术研究[J].机器人,2000,22(6):519～526.
[13] Tan H S, Guldner J, Patwardhan S, et al. Development of an Automated Steering VehicleBased on Roadway Magnets-A Case Study of Mechatronic System Design[J]. IEEE/ASMETransactions on Mechatronics,1999,4(3):258～272.
[14] Farrell J, Tan H S, Law B K, et al. Carrier Phase GPS-aided INS Based Vehicle LateralControl[J]. ASME Journal of Dynamic Systems, Measurement, and Control,2003,125(3):339～353.
[15]丛岩峰.高速公路环境中自主驾驶车辆运动规划与控制[D],[工学博士学位论文].吉林:吉林大学,2011.
[16]席裕庚.动态不确定环境下广义控制问题的预测控制[J].控制理论与应用,2000,17(5):665～670.
[17]高振海,管欣,郭孔辉.预瞄跟随理论和驾驶员模型在汽车智能驾驶研究中的应用[J].交通运输工程学报,2002,2(2):63～66.
[18]辛江慧,李舜酩,廖庆斌,等.基于传感器信息的智能移动机器人导航评述[J].传感器与微系统,2008,27(4):4～7.
[19]李磊,叶涛.移动机器人技术现状研究与未来[J].机器人,2002,5:475～480.
[20] Defoort M, Palos J, Kokosy A, et al. Performance-based Reactive Navigation forNon-holonomic Mobile Robots[J]. Robotica,2009,27:281～290.
[21] Wang D, Qi F. Trajectory Planning for a Four-wheel-steering Vehicle[A]. In: Proceedings ofthe IEEE International Conference on Robotics&Automation[C]. Seoul, Korea,2001,4:3320～3325.
[22] Cheng P, Frazzoli E, LaValle S. Improving the Performance of Sampling-Based MotionPlanning with Symmetry-Based Gap Reduction[J]. IEEE Transactions on Robotics,2008,24(2):488～494.
[23] Shanmugavel M, Tsourdos A, White B, et al. Co-operative Path Planning of Multiple UAVsUsing Dubins Paths with Clothoid Arcs[J]. Control Engineering Practice,2010,18(9):1084～1092.
[24]曲道奎,杜振军,徐殿国,等.移动机器人路径规划方法研究[J].机器人,2008,30(2):97～101,106.
[25] Pozna C, Troester F, Precup R E, et al. On The Design of an Obstacle Avoiding Trajectory:Method and Simulation[J]. Mathematics and Computers in Simulation,2009,79(7):2211～2226.
[26] Dubins L E. On Curves of Minimal Length with a Constraint on Average Curvature and withPrescribed Initial and Terminal Positions and Tangents[J]. American Journal Of Mathematics,1957,79:497～516.
[27] Reeds J A, Shepp L A. From Dubins’ Car to Reeds and Shepp’s Mobile Robot[J]. Computingand Visualization in Science,12(7):345～364.
[28] Scheuer A. Continuous-curvature Path Planning for Car-like Vehicles[A]. In: Proceedings ofthe1997IEEE/RSJ International Conference on Intelligent Robots and Systems[C]. Grenoble,France,1997,2:997～1003.
[29] Fraichard T, Scheuer A. From Reeds and Shepp’s to Continuous-Curvature Paths[J]. IEEETransactions on Robotics,2004,20(6):1025～1035.
[30] Motlagh O R E, Hong T S, Ismail N. Development of a New Minimum Avoidance System for aBehavior-based Mobile Robot[J]. Fuzzy Sets and Systems,2009,160(13):1929～1946.
[31] Wang M, Liu J N K. Fuzzy logic-based Real-time Robot Navigation in Unknown Environmentwith Dead Ends[J]. Robotics and Autonomous Systems,2008,56(7):625～643.
[32]崔茂源,赵中祺.基于模糊逻辑的自主移动机器人实时滚动路径规划及控制[J].吉林工业大学学报,1999,29(1):58～63.
[33]苏治宝,陆际联.用模糊逻辑法对移动机器人进行路径规划的研究[J].北京理工大学学报,2003,23(3):290～293.
[34] Li S M, Xin J H, Shang W Y, et al. The Algorithm of Obstacle Avoidance Based on ImprovedFuzzy Neural Networks Fusion for Exploration Vehicle[J]. WSEAS Transactions on Systemsand Control,2009,4(3):140～150.
[35]鲍庆勇,李舜酩,沈峘,等.基于行为融合的移动机器人自主避障算法[J].传感器与微系统,2010,29(5):70～73,76.
[36] Ye C, Wang D. A Novel Navigation Method for Autonomous Mobile Vehicles[J]. Journal ofIntelligent and Robotics Systems,2001,32:361～388.
[37]毛勇,李实,王家廞,等.基于再励学习的被动动态步行机器人[J].中南大学学报(自然科学版),2008,48(1):92～96.
[38] Paszkowicz W. Genetic Algorithms, a Nature-inspired Tool: Survey of Applications inMaterials Science and Related Fields[J]. Materials and Manufacturing Processes,2009,24(2):174～197.
[39] Singh M K, Parhi D R. Path Optimisation of a Mobile Robot Using an Artificial NeuralNetwork Controller[J]. International Journal of Systems Science,2011,42(1):107～120.
[40] Hong Q, Yang S X, Willms A R, et al. Real-Time Robot Path Planning Based on a ModifiedPulse-Coupled Neural Network Model[J]. IEEE Transactions on Neural Networks,2009,20(11):1724～1739.
[41] Zhu Q B, Hu J, Cai W, et al. A New Robot Navigation Algorithm for Dynamic UnknownEnvironments Based on Dynamic Path Re-computation and an Improved Scout AntAlgorithm[J]. Applied Soft Computing,2011,11(8):4667～4676.
[42] Yu S L, Bo Y M, Chen Z M, et al. Vehicle Path Planning Method Based on Particle SwarmOptimization Algorithm[J]. Advanced Materials Research,2012,468-471:2745～2748.
[43] Bryson A E, Ho Y C. Applied Optimal Control[M]. Bristol PA: Hemisphere,1975:100～150.
[44] Dib W, Serrao L, Sciarretta A. Optimal Control to Minimize Trip Time and EnergyConsumption in Electric Vehicles[A]. In: IEEE Vehicle Power and Propulsion Conference[C].Chicago, IL, USA,2011,1～8.
[45]宋金泽,戴斌,单恩忠,等.融合动力学约束的自主平行泊车轨迹生成方法[J].中南大学学报(自然科学版),2009,40(Suppl.1):135～141.
[46] Pei Y, Wang G Y, Zhang Y. Microscopic Model of Automobile Lane-changing Virtual DesireTrajectory by Spline Curves[J]. Promet-Traffic&Transportation,2010,22(3):203～208.
[47] Gámez-Bravoa F, Cuestab F, Ollerob A, et al. Continuous Curvature Path Generation Based onβ-spline Curves for Parking Manoeuvres[J]. Robotics and Autonomous Systems,2008,56(4):360～372.
[48]游峰.智能车辆自动换道与自动超车控制方法的研究[D],[工学博士学位论文].吉林:吉林大学,2005.
[49]单恩忠.基于微分平坦与样条理论的自主泊车轨迹规划研究[D],[工学硕士学位论文].湖南:国防科技大学,2009.
[50] Monaco S, Normand-Cyrot D. An Introduction Tomotion Planning under Multi-rate DigitalControl[A]. In: IEEE International Conference on Decision and Control[C]. Tucson, AZ, USA,1992,1780～1785.
[51] Villagra J, D. Andréa-Novel B, Mounier H, et al. Flatness-Based Vehicle Steering ControlStrategy With SDRE Feedback Gains Tuned Via a Sensitivity Approach[J]. IEEE Transactionson Control Systems Technology,2007,15(3):554～565.
[52] Müller B, Deutscher J, Grodde S. Continuous Curvature Trajectory Design and FeedforwardControl for Parking a Car[J]. IEEE Transactions on Control Systems Technology,2007,15(3):541～553.
[53]杨世强,傅卫平,张鹏飞.四轮全方位轮式移动机器人的运动学模型研究[J].机械科学与技术,2009,28(3):412～420.
[54]沈峘,李舜酩,魏民祥,等.智能探测车避障转向控制[J].重庆工学院学报(自然科学版),2009,23(5):1～6.
[55] Xun Y Z, Xia F P, Jie H Z, et al. Mobile Robot Path Planning Based on Local EnvironmentModeling and Adaptive Window[J]. Applied Mechanics and Materials,2011,48-49:679～683.
[56]陈杨.基于轨迹片段的运动规划方法及其在自主车辆中的应用[D],[工学博士学位论文].湖南:国防科学技术大学,2006.
[57] Aderson J R, Bower G H. Human Associative Memory[M]. New York: Halsted Press,1973:55～70.
[58] Anderson J R. A Theory for the Recognition of Items from Short Memorized Lists[J].Psychological Review,1973,85:249～277.
[59] Newell A. Production Systems: Models of Control Structures[J]. Visual Information Processing,1973:527～546.
[60] Anderson J R. Language, Memory and Thought[M]. Hillsdale, NJ: Erlbaum,1976:33～45.
[61] Anderson J R. The Architecture of Cognition[M]. Cambridge, MA: Harvard University Press,1983:10～35.
[62] Anderson J R. Rules of the Mind[M]. Hillsdale, NJ: Erlbaum,1993:60～70.
[63] Anderson J R. The Atomic Components of Thought[M]. Mahwah, NJ: Erlbaum,1998:297～343.
[64] Meyer D E, Kieras D E. A Computational Theory of Executive Cognitive Processes andMultiple-task Performance: Part1. Basic Mechanisms[J]. Psychological Review,1997,104(1):3～65.
[65] Anderson J R, Taatgen N A, Byrne M D. Learning to Achieve Perfect Time Sharing:Architectual Implications of Hazeltine, Teague and Ivry[J]. Journal of ExperimentalPsychology: Human Perception and Performance,2005,31(4):749～761.
[66] Mai N T T, Hai T T T, Son N V. Wizard of Oz for Designing Hand Gesture Vocabulary inHuman-Robot Interaction[A]. In:2011Third International Conference on Knowledge andSystems Engineering[C]. Hanoi, vietnam,2011,232～238.
[67]刘雁飞,吴朝晖.驾驶ACT-R认知行为建模[J].浙江大学学报(工学版),2006,40(10):1657～1662.
[68] Belavkin R V. Conflict Resolution by Random Estimated Costs[A]. In: In Proceedings of the17th European Simulation Multiconference[C]. Nottingham, UK,2003,105～110.
[69] Belavkin R V, Ritter F E. Optimist: A New Conflict Resolution Algorithm for ACT-R[A]. In:Proceedings of the Sixth International Conference on Cognitive Modelling[C]. Mahwah, NJ,2004,40～45.
[70]王新鹏.认知模型的研究和应用[D],[工学硕士学位].兰州:兰州理工大学,2007.
[71] Nash A, Daniel K, Koenig S, et al. Theta*: Any-angle Path Planning on Grids[J]. Journal ofArtificial Intelligence Research,2010,39:533～579.
[72] Choi J W. Curvature-Continuous Trajectory Generation with Corridor Constraint forAutonomous Ground Vehicles[A]. In:49th IEEE Conference on Decision and Control(CDC)[C]. Atlanta, GA, USA,2010,7166～7171.
[73]沈峘,凌锐,李舜酩.考虑轮胎无滑移的智能车辆连续曲率轨迹规划方法[J].中国机械工程,2012,23(18):2263～2267.
[74] Fleury S, Soueres P. Primitives for Smoothing Mobile Robot Trajectories[J]. IEEETransactions on Robotics and Automation,1995,11(3):441～448.
[75] Nelson W L. Continuous Steering-Function Control of Robot Carts[J]. IEEE Transactions OnIndustrial Electronics,1989,36(3):330～337.
[76]李玮,王晶,段建民.基于多项式的智能车辆换道轨迹规划[J].计算机工程与应用,2012,48(3):242～245.
[77] Nelson W. Continuous-curvature Paths for Autonomous Vehicles[A]. In: Proceedings of IEEEInternational Conference on Robotics and Automation[C]. Scottsdale, AZ, USA,1989,3:1260～1264.
[78]李玮,高德芝,段建民.智能车辆自由换道模型研究[J].公路交通科技,2010,27(2):119～123.
[79] Betts J T. Survey of Numerical Methods for Trajectory Optimization[J]. AIAA J. Guidance,Control and Dynamics,1998,21(2):193～207.
[80]魏庆来.基于近似动态规划的非线性系统最优控制研究[D],[工学博士学位论文].沈阳:东北大学,2009.
[81] Henshaw C G. A Variational Technique for Spacecraft Trajectory Planning[D],[Doctor ofPhilosophy]. Columbia: University of Maryland,2003.
[82] Roberts S M, Shipman J S. Two-point Boundary Value Problems: Shooting Methods[M]. NewYork: Elsevier Publishing Company,1972:
[83] Xiu D, Hesthaven J. High-Order Collocation Methods for Differential Equations with RandomInputs[J]. SIAM Journal on Scientific Computing,2005,27(3):1118～1139.
[84] Kirk D E. Optimal Control Theory-An Introduction[M]. Mineola, New York: DoverPublications, Inc.,2004:329～408.
[85] Milam M B. Real-Time Optimal Trajectory Generation for Constrained Dynamical Systems[D],
[Doctor of Philosophy]. Pasadena: California Institute of Technology,2003.
[86] Maanen L V, Rijn H V. Accounting for Subliminal Priming in ACT-R[A]. In:8th InternationalConference on Cognitive Modeling[C]. Ann Arbor, Michigan, USA,2007,1～6.
[87] Jones R M, Lebiere C, Crossman J A. Comparing Modeling Idioms in ACT-R and Soar[A]. In:8th International Conference on Cognitive Modeling[C]. Ann Arbor, Michigan, USA,2007,49～54.
[88] Williams J R, Singh V P. The EPIC model[J]. Computer models of watershed hydrology,1995:909～1000.
[89] Belavkin R V. On Emotion, Learning and Uncertainty: A Cognitive Modelling Approach[D],[Doctor of Philosophy]. Nottingham, UK: The University of Nottingham,2003.
[90] Belavkin R. Conflict Resolution by Random Estimated Costs[A]. In: Proceedings of the17thEuropean Simulation Multiconference[C]. Nottingham, UK,2003,105～110.
[91] Reitter D, Lebiere C. A Cognitive Model of Spatial Path-planning[J]. Comput Math OrganTheory,2010,16:220～245.
[92] Costello J. Integrating Optimal Control into a Cognitive Architecture[A]. In: Proceedings ofDepaul CDM School of Computing Research Symposium[C]. Chicago, Lllinois, USA,2011,14～23.
[93] Aderson J R, Bothell D, Byrne M D, et al. An Integrated Theory of the Mind[J]. PsychologicalReview,2004,111(4):1036～1060.
[94]刘雁飞,吴朝晖.驾驶ACT-R认知行为建模[J].浙江大学学报(工学版),2006,40(10):1657～1662.
[95] Aderson J R, Qin Y, Stenger V A, et al. The Relationship of Three Cortical Regions to anInformation-Processing Model[J]. Journal of Cognitive Neuroscience,2004,16(4):637～653.
[96] Anderson J R. How Can the Human Mind Occur in the Physical Universe?[M]. New York:Oxford University Press,2007:237～249.
[97] Peebles D, Banks A P. Modelling Dynamic Decision Making with the ACT-R CognitiveArchitecture[J]. Journal of Artificial General Intelligence,2010,2(2):52～68.
[98] Salvucci D D. Modeling Driver Behavior in a Cognitive Architecture[J]. Human Factors: TheJournal of the Human Factors and Ergonomics Society,2006,48(2):362～380.
[99] Anzai Y. Cognitive Control of Real-time Event Driven Systems[J]. Cognitive Science,1984,8:221～254.
[100] Schlenoff C, Madhavan R, Albus J, et al. Fusing Disparate Information within the4D/RCSArchitecture[A]. In:7th Intenational Conference on Information Fusion[C]. Gaithersburg, MD,USA,2005,2:1123～1130.
[101] Fleetwood M D, Byrne M D. Modeling the Visual Search of Displays: A Revised ACT-R/PMModel of Icon Search Based on Eye Tracking Data[J]. Human Computer Interaction,2006,21:153～197.
[102]马东扬.基于滚动优化的轮式移动机器人轨迹规划[D],[工学硕士学位].吉林:吉林大学,2009.
[103]周苑.基于改进性遗传算法的Bezier曲线笛卡尔空间轨迹规划[J].上海电机学院学报,2012,15(4):237～240.
[104] Yamamoto M, Ozaki H, Mohri A. Planning of Manipulator Joint Trajectories by anIterative Method[J]. Robotica,2008,6:101～105.
[105]赵彤,吕强,张辉,等.三次均匀B样条曲线高速实时插补研究[J].计算机集成制造系统,2008,14(9):1830～1836.
[106]张勇,巩敦卫,张婉秋.一种基于单纯形法的改进微粒群优化算法及其收敛性分析[J].自动化学报,2009,35(3):289～298.
[107]赵蔚.两层多目标规划罚函数法[J].自动化学报,1998,24(3):331～337.
[108]张火明,陆萍蓝,吴剑国.离散复合形法的改进及应用研究[J].中国计量学院学报,2007,17(4):300～304.
[109]徐仲安,王天保,李常英,等.正交试验设计法简介[J].科技情报开发与经济,2002,12(5):148～150.
[110] Shiller Z, Dubowsky S. Robot Path Planning with Obstacles, Actuator, Gripper andPayload Constraints[J]. International Journal of Robotics Research,1989,6:3～18.
[111]袁丽华.基于物种进化的遗传算法[D],[工学博士学位论文].南京:南京航空航天大学,2009.
[112]皮佑国,范德和.数控加工中连续微线段轨迹的B样条曲线拟合术[J].华南理工大学学报(自然科学版),2012,40(1):53～57.
[113]杜宗宗.基于遗传算法的移动机器人路径规划研究[D],[工学硕士学位].无锡:江南大学,2009.
[114] Srinivas N, Deb K. Multi-Objective Function Optimization using Non-dominated SortingGenetic Algorithms[J]. Evolutionary Computation,1995,2(3):221～248.
[115]王英勋,陈宗基.基于遗传算法(GA)的具有约束的飞行轨迹规划[J].北京航空航天大学学报,1999,25(3):365～368.
[116] Yanrong H, Yang S X. A Knowledge Based Genetic A1gorithm for Path Planning of a MobileRobot[A]. In: Proceedings of the2004IEEE international Conference on Robotics&Automation [C]. New Orleans, LA, USA,2004,5:4350～4355.
[117]辉彭,李治龙,碧曾.基于自适应遗传算法的无人机轨迹优化[J].科学技术与工程,2012,12(18):4451～4454,4460.
[118] Duckett T A. Genetic Algorithm for Simultaneous Localization and Mapping[A]. In: IEEEInternational Conference on Robotics and Automation[C].2003,1:434～439.
[119]宗长富,郭孔辉.汽车操纵稳定性的客观定量评价指标[J].吉林工业大学自然科学学报,2000,130(1):1～6.
[120]张浩.客车操纵稳定性分析及其控制策略研究[D],[工学博士学位论文].吉林:吉林大学,2012.
[2] Tsugawa S, Saito T, Hosaka A. Super Smart Vehicle System: Avcs Related Systems for theFuture[A]. In: IEEE Intelligence Vehicles Symposium[C]. Detroit, MI, USA,1992,132～137.
[3]李都厚,刘群,袁伟,等.疲劳驾驶与交通事故关系[J].交通运输工程学报,2010,10(2):104～109.
[4]李舜酩,沈峘,毛建国,等.智能车辆发展及其关键技术展望[J].传感器与微系统,2009,1(28):1～4.
[5] Li L, Wen D, Zheng N N, et al. Cognitive Cars: A New Frontier for ADAS Research[J]. IEEETransactions on Intelligent Transportation Systems,2011,99(7):1～13.
[6]马强.关于智能交通规划的若干问题[J].交通发展,2004,4:33～35.
[7]王荣本,李斌,储江伟,等.世界智能车辆行驶安全保障技术的研究进展[J].公路交通科技,2002,2(19):112～117.
[8]王笑京,沈鸿飞,汪林.中国智能交通系统发展战略研究[J].交通运输系统工程与信息,2006,4(6):9～12.
[9] Sheng W. H, Yang Q, Guo Y. Cooperative Driving Based On Inter-Vehicle Communications:Experimental Platform and Algorithm[A]. In: International Conference on Intelligent Robotsand Systems[C]. Beijing, China,2006,5073～5078.
[10]孙振平.自主驾驶汽车智能控制系统[D],[工学博士学位论文].长沙:国防科学技术大学,2004.
[11] Pappas G J. Hierarchically Consistent Control Systems[J]. IEEE Transactions on AutomaticControl,2000,45(6):1144～1160.
[12]欧青立,何克忠.室外智能移动机器人的发展及其关键技术研究[J].机器人,2000,22(6):519～526.
[13] Tan H S, Guldner J, Patwardhan S, et al. Development of an Automated Steering VehicleBased on Roadway Magnets-A Case Study of Mechatronic System Design[J]. IEEE/ASMETransactions on Mechatronics,1999,4(3):258～272.
[14] Farrell J, Tan H S, Law B K, et al. Carrier Phase GPS-aided INS Based Vehicle LateralControl[J]. ASME Journal of Dynamic Systems, Measurement, and Control,2003,125(3):339～353.
[15]丛岩峰.高速公路环境中自主驾驶车辆运动规划与控制[D],[工学博士学位论文].吉林:吉林大学,2011.
[16]席裕庚.动态不确定环境下广义控制问题的预测控制[J].控制理论与应用,2000,17(5):665～670.
[17]高振海,管欣,郭孔辉.预瞄跟随理论和驾驶员模型在汽车智能驾驶研究中的应用[J].交通运输工程学报,2002,2(2):63～66.
[18]辛江慧,李舜酩,廖庆斌,等.基于传感器信息的智能移动机器人导航评述[J].传感器与微系统,2008,27(4):4～7.
[19]李磊,叶涛.移动机器人技术现状研究与未来[J].机器人,2002,5:475～480.
[20] Defoort M, Palos J, Kokosy A, et al. Performance-based Reactive Navigation forNon-holonomic Mobile Robots[J]. Robotica,2009,27:281～290.
[21] Wang D, Qi F. Trajectory Planning for a Four-wheel-steering Vehicle[A]. In: Proceedings ofthe IEEE International Conference on Robotics&Automation[C]. Seoul, Korea,2001,4:3320～3325.
[22] Cheng P, Frazzoli E, LaValle S. Improving the Performance of Sampling-Based MotionPlanning with Symmetry-Based Gap Reduction[J]. IEEE Transactions on Robotics,2008,24(2):488～494.
[23] Shanmugavel M, Tsourdos A, White B, et al. Co-operative Path Planning of Multiple UAVsUsing Dubins Paths with Clothoid Arcs[J]. Control Engineering Practice,2010,18(9):1084～1092.
[24]曲道奎,杜振军,徐殿国,等.移动机器人路径规划方法研究[J].机器人,2008,30(2):97～101,106.
[25] Pozna C, Troester F, Precup R E, et al. On The Design of an Obstacle Avoiding Trajectory:Method and Simulation[J]. Mathematics and Computers in Simulation,2009,79(7):2211～2226.
[26] Dubins L E. On Curves of Minimal Length with a Constraint on Average Curvature and withPrescribed Initial and Terminal Positions and Tangents[J]. American Journal Of Mathematics,1957,79:497～516.
[27] Reeds J A, Shepp L A. From Dubins’ Car to Reeds and Shepp’s Mobile Robot[J]. Computingand Visualization in Science,12(7):345～364.
[28] Scheuer A. Continuous-curvature Path Planning for Car-like Vehicles[A]. In: Proceedings ofthe1997IEEE/RSJ International Conference on Intelligent Robots and Systems[C]. Grenoble,France,1997,2:997～1003.
[29] Fraichard T, Scheuer A. From Reeds and Shepp’s to Continuous-Curvature Paths[J]. IEEETransactions on Robotics,2004,20(6):1025～1035.
[30] Motlagh O R E, Hong T S, Ismail N. Development of a New Minimum Avoidance System for aBehavior-based Mobile Robot[J]. Fuzzy Sets and Systems,2009,160(13):1929～1946.
[31] Wang M, Liu J N K. Fuzzy logic-based Real-time Robot Navigation in Unknown Environmentwith Dead Ends[J]. Robotics and Autonomous Systems,2008,56(7):625～643.
[32]崔茂源,赵中祺.基于模糊逻辑的自主移动机器人实时滚动路径规划及控制[J].吉林工业大学学报,1999,29(1):58～63.
[33]苏治宝,陆际联.用模糊逻辑法对移动机器人进行路径规划的研究[J].北京理工大学学报,2003,23(3):290～293.
[34] Li S M, Xin J H, Shang W Y, et al. The Algorithm of Obstacle Avoidance Based on ImprovedFuzzy Neural Networks Fusion for Exploration Vehicle[J]. WSEAS Transactions on Systemsand Control,2009,4(3):140～150.
[35]鲍庆勇,李舜酩,沈峘,等.基于行为融合的移动机器人自主避障算法[J].传感器与微系统,2010,29(5):70～73,76.
[36] Ye C, Wang D. A Novel Navigation Method for Autonomous Mobile Vehicles[J]. Journal ofIntelligent and Robotics Systems,2001,32:361～388.
[37]毛勇,李实,王家廞,等.基于再励学习的被动动态步行机器人[J].中南大学学报(自然科学版),2008,48(1):92～96.
[38] Paszkowicz W. Genetic Algorithms, a Nature-inspired Tool: Survey of Applications inMaterials Science and Related Fields[J]. Materials and Manufacturing Processes,2009,24(2):174～197.
[39] Singh M K, Parhi D R. Path Optimisation of a Mobile Robot Using an Artificial NeuralNetwork Controller[J]. International Journal of Systems Science,2011,42(1):107～120.
[40] Hong Q, Yang S X, Willms A R, et al. Real-Time Robot Path Planning Based on a ModifiedPulse-Coupled Neural Network Model[J]. IEEE Transactions on Neural Networks,2009,20(11):1724～1739.
[41] Zhu Q B, Hu J, Cai W, et al. A New Robot Navigation Algorithm for Dynamic UnknownEnvironments Based on Dynamic Path Re-computation and an Improved Scout AntAlgorithm[J]. Applied Soft Computing,2011,11(8):4667～4676.
[42] Yu S L, Bo Y M, Chen Z M, et al. Vehicle Path Planning Method Based on Particle SwarmOptimization Algorithm[J]. Advanced Materials Research,2012,468-471:2745～2748.
[43] Bryson A E, Ho Y C. Applied Optimal Control[M]. Bristol PA: Hemisphere,1975:100～150.
[44] Dib W, Serrao L, Sciarretta A. Optimal Control to Minimize Trip Time and EnergyConsumption in Electric Vehicles[A]. In: IEEE Vehicle Power and Propulsion Conference[C].Chicago, IL, USA,2011,1～8.
[45]宋金泽,戴斌,单恩忠,等.融合动力学约束的自主平行泊车轨迹生成方法[J].中南大学学报(自然科学版),2009,40(Suppl.1):135～141.
[46] Pei Y, Wang G Y, Zhang Y. Microscopic Model of Automobile Lane-changing Virtual DesireTrajectory by Spline Curves[J]. Promet-Traffic&Transportation,2010,22(3):203～208.
[47] Gámez-Bravoa F, Cuestab F, Ollerob A, et al. Continuous Curvature Path Generation Based onβ-spline Curves for Parking Manoeuvres[J]. Robotics and Autonomous Systems,2008,56(4):360～372.
[48]游峰.智能车辆自动换道与自动超车控制方法的研究[D],[工学博士学位论文].吉林:吉林大学,2005.
[49]单恩忠.基于微分平坦与样条理论的自主泊车轨迹规划研究[D],[工学硕士学位论文].湖南:国防科技大学,2009.
[50] Monaco S, Normand-Cyrot D. An Introduction Tomotion Planning under Multi-rate DigitalControl[A]. In: IEEE International Conference on Decision and Control[C]. Tucson, AZ, USA,1992,1780～1785.
[51] Villagra J, D. Andréa-Novel B, Mounier H, et al. Flatness-Based Vehicle Steering ControlStrategy With SDRE Feedback Gains Tuned Via a Sensitivity Approach[J]. IEEE Transactionson Control Systems Technology,2007,15(3):554～565.
[52] Müller B, Deutscher J, Grodde S. Continuous Curvature Trajectory Design and FeedforwardControl for Parking a Car[J]. IEEE Transactions on Control Systems Technology,2007,15(3):541～553.
[53]杨世强,傅卫平,张鹏飞.四轮全方位轮式移动机器人的运动学模型研究[J].机械科学与技术,2009,28(3):412～420.
[54]沈峘,李舜酩,魏民祥,等.智能探测车避障转向控制[J].重庆工学院学报(自然科学版),2009,23(5):1～6.
[55] Xun Y Z, Xia F P, Jie H Z, et al. Mobile Robot Path Planning Based on Local EnvironmentModeling and Adaptive Window[J]. Applied Mechanics and Materials,2011,48-49:679～683.
[56]陈杨.基于轨迹片段的运动规划方法及其在自主车辆中的应用[D],[工学博士学位论文].湖南:国防科学技术大学,2006.
[57] Aderson J R, Bower G H. Human Associative Memory[M]. New York: Halsted Press,1973:55～70.
[58] Anderson J R. A Theory for the Recognition of Items from Short Memorized Lists[J].Psychological Review,1973,85:249～277.
[59] Newell A. Production Systems: Models of Control Structures[J]. Visual Information Processing,1973:527～546.
[60] Anderson J R. Language, Memory and Thought[M]. Hillsdale, NJ: Erlbaum,1976:33～45.
[61] Anderson J R. The Architecture of Cognition[M]. Cambridge, MA: Harvard University Press,1983:10～35.
[62] Anderson J R. Rules of the Mind[M]. Hillsdale, NJ: Erlbaum,1993:60～70.
[63] Anderson J R. The Atomic Components of Thought[M]. Mahwah, NJ: Erlbaum,1998:297～343.
[64] Meyer D E, Kieras D E. A Computational Theory of Executive Cognitive Processes andMultiple-task Performance: Part1. Basic Mechanisms[J]. Psychological Review,1997,104(1):3～65.
[65] Anderson J R, Taatgen N A, Byrne M D. Learning to Achieve Perfect Time Sharing:Architectual Implications of Hazeltine, Teague and Ivry[J]. Journal of ExperimentalPsychology: Human Perception and Performance,2005,31(4):749～761.
[66] Mai N T T, Hai T T T, Son N V. Wizard of Oz for Designing Hand Gesture Vocabulary inHuman-Robot Interaction[A]. In:2011Third International Conference on Knowledge andSystems Engineering[C]. Hanoi, vietnam,2011,232～238.
[67]刘雁飞,吴朝晖.驾驶ACT-R认知行为建模[J].浙江大学学报(工学版),2006,40(10):1657～1662.
[68] Belavkin R V. Conflict Resolution by Random Estimated Costs[A]. In: In Proceedings of the17th European Simulation Multiconference[C]. Nottingham, UK,2003,105～110.
[69] Belavkin R V, Ritter F E. Optimist: A New Conflict Resolution Algorithm for ACT-R[A]. In:Proceedings of the Sixth International Conference on Cognitive Modelling[C]. Mahwah, NJ,2004,40～45.
[70]王新鹏.认知模型的研究和应用[D],[工学硕士学位].兰州:兰州理工大学,2007.
[71] Nash A, Daniel K, Koenig S, et al. Theta*: Any-angle Path Planning on Grids[J]. Journal ofArtificial Intelligence Research,2010,39:533～579.
[72] Choi J W. Curvature-Continuous Trajectory Generation with Corridor Constraint forAutonomous Ground Vehicles[A]. In:49th IEEE Conference on Decision and Control(CDC)[C]. Atlanta, GA, USA,2010,7166～7171.
[73]沈峘,凌锐,李舜酩.考虑轮胎无滑移的智能车辆连续曲率轨迹规划方法[J].中国机械工程,2012,23(18):2263～2267.
[74] Fleury S, Soueres P. Primitives for Smoothing Mobile Robot Trajectories[J]. IEEETransactions on Robotics and Automation,1995,11(3):441～448.
[75] Nelson W L. Continuous Steering-Function Control of Robot Carts[J]. IEEE Transactions OnIndustrial Electronics,1989,36(3):330～337.
[76]李玮,王晶,段建民.基于多项式的智能车辆换道轨迹规划[J].计算机工程与应用,2012,48(3):242～245.
[77] Nelson W. Continuous-curvature Paths for Autonomous Vehicles[A]. In: Proceedings of IEEEInternational Conference on Robotics and Automation[C]. Scottsdale, AZ, USA,1989,3:1260～1264.
[78]李玮,高德芝,段建民.智能车辆自由换道模型研究[J].公路交通科技,2010,27(2):119～123.
[79] Betts J T. Survey of Numerical Methods for Trajectory Optimization[J]. AIAA J. Guidance,Control and Dynamics,1998,21(2):193～207.
[80]魏庆来.基于近似动态规划的非线性系统最优控制研究[D],[工学博士学位论文].沈阳:东北大学,2009.
[81] Henshaw C G. A Variational Technique for Spacecraft Trajectory Planning[D],[Doctor ofPhilosophy]. Columbia: University of Maryland,2003.
[82] Roberts S M, Shipman J S. Two-point Boundary Value Problems: Shooting Methods[M]. NewYork: Elsevier Publishing Company,1972:
[83] Xiu D, Hesthaven J. High-Order Collocation Methods for Differential Equations with RandomInputs[J]. SIAM Journal on Scientific Computing,2005,27(3):1118～1139.
[84] Kirk D E. Optimal Control Theory-An Introduction[M]. Mineola, New York: DoverPublications, Inc.,2004:329～408.
[85] Milam M B. Real-Time Optimal Trajectory Generation for Constrained Dynamical Systems[D],
[Doctor of Philosophy]. Pasadena: California Institute of Technology,2003.
[86] Maanen L V, Rijn H V. Accounting for Subliminal Priming in ACT-R[A]. In:8th InternationalConference on Cognitive Modeling[C]. Ann Arbor, Michigan, USA,2007,1～6.
[87] Jones R M, Lebiere C, Crossman J A. Comparing Modeling Idioms in ACT-R and Soar[A]. In:8th International Conference on Cognitive Modeling[C]. Ann Arbor, Michigan, USA,2007,49～54.
[88] Williams J R, Singh V P. The EPIC model[J]. Computer models of watershed hydrology,1995:909～1000.
[89] Belavkin R V. On Emotion, Learning and Uncertainty: A Cognitive Modelling Approach[D],[Doctor of Philosophy]. Nottingham, UK: The University of Nottingham,2003.
[90] Belavkin R. Conflict Resolution by Random Estimated Costs[A]. In: Proceedings of the17thEuropean Simulation Multiconference[C]. Nottingham, UK,2003,105～110.
[91] Reitter D, Lebiere C. A Cognitive Model of Spatial Path-planning[J]. Comput Math OrganTheory,2010,16:220～245.
[92] Costello J. Integrating Optimal Control into a Cognitive Architecture[A]. In: Proceedings ofDepaul CDM School of Computing Research Symposium[C]. Chicago, Lllinois, USA,2011,14～23.
[93] Aderson J R, Bothell D, Byrne M D, et al. An Integrated Theory of the Mind[J]. PsychologicalReview,2004,111(4):1036～1060.
[94]刘雁飞,吴朝晖.驾驶ACT-R认知行为建模[J].浙江大学学报(工学版),2006,40(10):1657～1662.
[95] Aderson J R, Qin Y, Stenger V A, et al. The Relationship of Three Cortical Regions to anInformation-Processing Model[J]. Journal of Cognitive Neuroscience,2004,16(4):637～653.
[96] Anderson J R. How Can the Human Mind Occur in the Physical Universe?[M]. New York:Oxford University Press,2007:237～249.
[97] Peebles D, Banks A P. Modelling Dynamic Decision Making with the ACT-R CognitiveArchitecture[J]. Journal of Artificial General Intelligence,2010,2(2):52～68.
[98] Salvucci D D. Modeling Driver Behavior in a Cognitive Architecture[J]. Human Factors: TheJournal of the Human Factors and Ergonomics Society,2006,48(2):362～380.
[99] Anzai Y. Cognitive Control of Real-time Event Driven Systems[J]. Cognitive Science,1984,8:221～254.
[100] Schlenoff C, Madhavan R, Albus J, et al. Fusing Disparate Information within the4D/RCSArchitecture[A]. In:7th Intenational Conference on Information Fusion[C]. Gaithersburg, MD,USA,2005,2:1123～1130.
[101] Fleetwood M D, Byrne M D. Modeling the Visual Search of Displays: A Revised ACT-R/PMModel of Icon Search Based on Eye Tracking Data[J]. Human Computer Interaction,2006,21:153～197.
[102]马东扬.基于滚动优化的轮式移动机器人轨迹规划[D],[工学硕士学位].吉林:吉林大学,2009.
[103]周苑.基于改进性遗传算法的Bezier曲线笛卡尔空间轨迹规划[J].上海电机学院学报,2012,15(4):237～240.
[104] Yamamoto M, Ozaki H, Mohri A. Planning of Manipulator Joint Trajectories by anIterative Method[J]. Robotica,2008,6:101～105.
[105]赵彤,吕强,张辉,等.三次均匀B样条曲线高速实时插补研究[J].计算机集成制造系统,2008,14(9):1830～1836.
[106]张勇,巩敦卫,张婉秋.一种基于单纯形法的改进微粒群优化算法及其收敛性分析[J].自动化学报,2009,35(3):289～298.
[107]赵蔚.两层多目标规划罚函数法[J].自动化学报,1998,24(3):331～337.
[108]张火明,陆萍蓝,吴剑国.离散复合形法的改进及应用研究[J].中国计量学院学报,2007,17(4):300～304.
[109]徐仲安,王天保,李常英,等.正交试验设计法简介[J].科技情报开发与经济,2002,12(5):148～150.
[110] Shiller Z, Dubowsky S. Robot Path Planning with Obstacles, Actuator, Gripper andPayload Constraints[J]. International Journal of Robotics Research,1989,6:3～18.
[111]袁丽华.基于物种进化的遗传算法[D],[工学博士学位论文].南京:南京航空航天大学,2009.
[112]皮佑国,范德和.数控加工中连续微线段轨迹的B样条曲线拟合术[J].华南理工大学学报(自然科学版),2012,40(1):53～57.
[113]杜宗宗.基于遗传算法的移动机器人路径规划研究[D],[工学硕士学位].无锡:江南大学,2009.
[114] Srinivas N, Deb K. Multi-Objective Function Optimization using Non-dominated SortingGenetic Algorithms[J]. Evolutionary Computation,1995,2(3):221～248.
[115]王英勋,陈宗基.基于遗传算法(GA)的具有约束的飞行轨迹规划[J].北京航空航天大学学报,1999,25(3):365～368.
[116] Yanrong H, Yang S X. A Knowledge Based Genetic A1gorithm for Path Planning of a MobileRobot[A]. In: Proceedings of the2004IEEE international Conference on Robotics&Automation [C]. New Orleans, LA, USA,2004,5:4350～4355.
[117]辉彭,李治龙,碧曾.基于自适应遗传算法的无人机轨迹优化[J].科学技术与工程,2012,12(18):4451～4454,4460.
[118] Duckett T A. Genetic Algorithm for Simultaneous Localization and Mapping[A]. In: IEEEInternational Conference on Robotics and Automation[C].2003,1:434～439.
[119]宗长富,郭孔辉.汽车操纵稳定性的客观定量评价指标[J].吉林工业大学自然科学学报,2000,130(1):1～6.
[120]张浩.客车操纵稳定性分析及其控制策略研究[D],[工学博士学位论文].吉林:吉林大学,2012.