• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Grid-Based Path Planner Using Multivariant Optimization Algorithm

    2015-07-24 17:34:42DanjvLvXinlingShiYufengZhangandJianhuaChen

    Danjv LvXinling ShiYufeng Zhangand Jianhua Chen

    (1.School of Information Science and Engineering,Yunnan University,Kunming 650091,China;2.Oil Equipment Intelligent Control Engineering Laboratory of Henan Provice,Physics&Electronic Engineering College,Nanyang Normal University,Nanyang Henan 473061,China;3.School of Information Technology and Engineering,Yuxi Normal University,Yuxi 653100,China)

    Grid-Based Path Planner Using Multivariant Optimization Algorithm

    Baolei Li1,2,Danjv Lv1,Xinling Shi1?,Zhenzhou An3,Yufeng Zhang1and Jianhua Chen1

    (1.School of Information Science and Engineering,Yunnan University,Kunming 650091,China;2.Oil Equipment Intelligent Control Engineering Laboratory of Henan Provice,Physics&Electronic Engineering College,Nanyang Normal University,Nanyang Henan 473061,China;3.School of Information Technology and Engineering,Yuxi Normal University,Yuxi 653100,China)

    To solve the shortest path planning problems on grid-based map efficiently,a novel heuristic path planning approach based on an intelligent swarm optimization method called Multivariant Optimization Algorithm(MOA)and a modified indirect encoding scheme are proposed.In MOA,the solution space is iteratively searched through global exploration and local exploitation by intelligent searching individuals,who are named as atoms.MOA is employed to locate the shortest path through iterations of global path planning and local path refinements in the proposed path planning approach.In each iteration,a group of global atoms are employed to perform the global path planning aiming at finding some candidate paths rapidly and then a group of local atoms are allotted to each candidate path for refinement.Further,the traditional indirect encoding scheme is modified to reduce the possibility of constructing an infeasible path from an array.Comparative experiments against two other frequently use intelligent optimization approaches:Genetic Algorithm(GA)and Particle Swarm Optimization(PSO)are conducted on benchmark test problems of varying complexity to evaluate the performance of MOA.The results demonstrate that MOA outperforms GA and PSO in terms of optimality indicated by the length of the located path.

    multivariant optimization algorithm;shortest path planning;heuristic search;grid map;optimality of algorithm

    1 Introduction

    Path planning on grid-based maps is an essential task in mini-unmanned aerial vehicle[1],mobile robot navigation[2],and video games[3]and so on.The objective of the shortest path planning(SPP)is to find out the shortest feasible path(optimal solution)from a starting position to a destination position among all paths(solution space)between these two positions whilst avoiding all obstacles.Thus,the shortest path planning is a constrained optimization problem[4]. When the complexity of the environment increases,traditional methods such as methods based on potential field require excessive computational time[5-6],which makes them suffer from serious performance bottlenecks[7].Given that the intelligent optimization algorithms are quickly convergent[8],robust and flexible[9],many intelligent approaches such as the ant colony algorithm[10],genetic algorithm(GA)[11-12]and particle swarm optimization(PSO)[13]have been applied to solve the path planning problems.These intelligent optimization algorithms have represented a great improvement over traditional approaches[14]. However,there is room for improvement because they are likely to fall into local optima[15-16].

    To improve the quality of solution through enhancing the ability of escaping from local traps,a novel intelligent path planner based on Multivariant Optimization Algorithm(MOA)is proposed in this paper.In MOA,the solution space is iteratively searched through alternating global exploration and local exploitation by intelligent agents named as atoms. The global exploration is carried out by global atoms which are generated in the whole solution space;the local exploitation is implemented by the local atoms which are generated in each niche of better global atoms.MOA is characterized by that global atoms have the global view ability while local atoms have local refinement ability[17],which makes it suitable for solving SPP problems.The global and local atoms have variety of characters in responsibility and the global atoms in different areas represent multiple solutions,thus the proposed algorithm is named as Multivariant Optimization Algorithm.In each iteration of the MOA-based shortest path planning approach,candidate paths(intermediate solution)are located by global atoms through exploring the whole map and then the candidate paths are refined by multiple local atom groups with different numbers of local atoms.The motivations of using local atom groups with different population size are that more local atoms should be allotted to the shorter candidate path for a high level refinement and vice versa.From the searching strategy of MOA,it can be seen that the shortest feasible path(optimal solution)can be obtained through improving the intermediate solution iteratively.

    A key issue of applying optimization algorithms in path planning is how to construct a path from an array efficiently.Previously,two encoding techniques have been widely used in the construction of a path,i.e. direct and indirect encoding,which had been described in detail in Ref.[18].In this study,a modified version of the indirect encoding technique is presented to improve the efficiency through reducing the possibility of constructing infeasible path.

    To evaluate the efficiency of the proposed MOA-based path planning method,experiments on benchmark problem sets[19]in a commercial video games named Dragon Age:Origins(DAO),are conducted by using MOA,GA and PSO.The performance of investigated methods is observed and evaluated based on path length carves against the number of iterations.It is observed that the solution quality in MOA gradually grows and surpasses those in GA and PSO as the number of iterations increases,which shows the superior performance of the proposed method in optimality.

    2 Multivariant Optimization Algorithm

    To locate the optimal solution,the solution space is searched through the alternating global exploration and local exploitation iterations based on a data structure which is made up of a queue and some stacks. In each iteration of MOA,the global atoms are generated in the whole solution space for a global exploration firstly.Secondly,a group of local atoms are generated in each neighborhood of the global atoms in the queue for local exploitations.Thirdly,the new generated atoms are evaluated according to the handling problem.Fourthly,the queue of the structure is updated to record the potential areas,which is to be exploited in the next iteration.The pseudo of MOA is given in Algorithm 1 whereDsis the data structure used for storing and managing the atoms’information,andDs(i,j).Ais an atom stored in rowicolumnjandDs(i,j).Fvis its fitness value which is calculated in the step of evaluating new generated atoms,andTqis a temp queue used to temporarily store the information of the prior generation global atoms.Details about the atom and data structure are provided in the following sections.

    2.1 Atoms

    In MOA,an intelligent agent represented by an array is named as an atom.To give consideration to both the global exploration and the local exploitation,atoms are divided into two types:global exploration atoms and local exploitation atoms.The global exploration atoms are generated uniformly at random in the whole solution space for a global view of the environment and the local exploitation atoms are generated in the neighborhood of the global atom for local exploitation.In ann-dimensional search space,the global search atomAgis generated according to Eq.(1).

    wherelianduiare the lower and the upper bounds of theithdimension of the search space;the function unifrnd(li,ui)returns a random number which is uniformly distributed on the interval fromlitoui.

    A local search atomAlin the neighborhood of a global atomAgis generated according to Eq.(2).

    wherehi(i=1,2…,n)are random numbers uniformly distributed on[-1,1],and the neighborhood is a circle in a fixed radiusraround the centerAg.

    The fitness value of an atom is the quality of the solution achieved from the atom according to an encode scheme.In the proposed method,the fitness value of an atom is just the length of the path constructed by it according to the encode scheme.

    From Algorithm 1,it can be seen that the atom in the queue is the center of a potential area,which will be exploited in the next iteration and the position of it determines the exploitation level.

    2.2 Data Structure of MOA

    The iteration of MOA is managed by a data structure illustrated in Fig.1.It can be seen that there is a horizontal sorted doubly-linked list and each of its nodes points to a vertical sorted doubly-linked list in the data structure.To simplify the description,the horizontal sorted doubly-linked list and the vertical sorted doubly-linked list are named as the queue and the stack,respectively.

    Fig.1 Data structure of MOA

    The update of the queue is the core of MOA,because it determines the search strategies in the next iteration.The pseudo of updating of the queue is given in Algorithm 2 whereDsis the data structure.Tlis a temple list used to temporarily store the global atoms.Tlfv(i)records the fitness values of the atom:Tl(i).A.the function sort(Tlfv)returns an index vector of values inTlfvin ascending order.Tqis a temp queue used to temporarily store the information of the prior generation global atoms.

    From Algorithm 2,it can be seen that atoms compete to be stored in the queue according to their fitness values.After some alternating global-local search iterations,the better solutions are all remembered in the queue.

    3 Problem Formulation and Path Encoding Scheme

    3.1 Problem Formulation

    In this paper,the path planning problem is to be implemented on grid-based maps where tiles are indexed by their row and column numbers.On the map,diagonal and cardinal movements to free tiles are allowed.To calculate the path length,it is assumed that the step length of per diagonal and cardinal movement is2and 1,respectively.The length of a path is calculated by adding up the lengths for all steps needed to move from the start to the end along the path.To simplify the description,the following map illustrated in Fig.2 is used as a demo through our description.Where the black tiles are obstacles and the white tiles are free tiles,“S”and“D”stand for the starting and ending positions,respectively.

    Fig.2 A demo map with 6 rows and 6 columns

    3.2 Modified Indirect Encoding Scheme

    Recently,two typical encoding techniques,which are direct and indirect,have been used for path encoding in solving the SPP problems by optimization algorithms.In the direct representation scheme,the chromosome in the GA or the particle position in the PSO is represented by the sequence of the tiles’positions in a path.In the indirect encoding scheme,some guiding information“priorities”about the tiles are used to represent the path.The path is constructed by keeping on adding the available tiles with the highest priority into the path until the destination tile is reached.Details about these two types of encoding techniques can be found in Ref.[18].

    In MOA-based shortest path planning algorithm,the atom is represented by randomly selected tiles and the path is constructed by a visibility graphic of the selected tiles.An atom containingntiles is encoded into a 2nlength integer string as illustrated in Fig.3,whereRiandCiare the row number and column number of theithtile in an atom,respectively.

    Fig.3 Representation scheme for an atom

    To construct a path from an atom,the atom should be decoded to get some guiding information“priorities”.The steps of constructing a path for the demo atom shown in Fig.4 are as follows:

    Step 1Set up the visibility graphic(VG).AVGis a graph(V,E)whereVis the set of tiles including starting tile,destination tile and tiles in the atom andEare the set of all edges connecting any two tiles inVthat do not pass through any obstacles.The weight of an edge connecting two tilest(x1,y1)andt(x2,y2)is their distance which is calculated by Eq.(3).

    wherexiandyiare the row and column number of the tailt(xi,yi)(i=1,2),respectively.The visibility graphic of the demo is shown in Fig.5.

    Step 2Construct the shortest path according to the visibility graphic generated in Step 1.The pseudocode for constructing the shortest path of the demo is shown in Algorithm 3 whereLis a list storing the tiles having a chance to be expanded in the next loop,andhis an array whereh(i)records the shortest distance from theithtile to the destination,andNextis an array whereNext(i)records the optimal tile from theithtile,andNis the index number of the selected tile to be expanded in the next loop,andcListis an array recording the visible tiles of the selected tile indexed byN,Precords the constructed path,the starting tile and the destination tile are indexed by 4 and 5,respectively.After the search of visibility graphic in Step 2,the path can be yielded by going on adding the optimal tile(recorded inNext)of each tile into the path from the starting tile until the destination tile is reached or the number inNextis 0.The path is infeasible if it cannot lead us to the destination tile.

    Fig.4 Encoding scheme of the demo atom

    Fig.5 Visibility graphic of the demo

    4 Path Planner Based on MOA

    Based on the aforementioned problem formulation and path encoding scheme,MOA is employed to locate the shortest path through iterations of global path planning and local path refinement.Specifically,rough feasible paths are located after the global atoms view the whole map and then multiple local groups with different population are allotted to them for different levels of local exploitations with the aim of improving them efficiently.In each iteration,firstly,global atoms are generated in the whole map for global path planning.Secondly,local atoms are generated in the neighborhoods of global atoms for local path refinement. Thirdly,new exploration information is achieved by the evaluations of new generated atoms.Fourthly,the queue is updated according to the historical and new exploration information to guide the local path refinements in the next iteration.The algorithm stops when a predefined maximum number of iteration is reached.

    The pseudo code of MOA based shortest path planning is shown in Algorithm 4.Details of global path planning,local path refinement,fitness evaluation and queue update are provided as follows:

    The details of main operations in Algorithm 4 are as follows:

    1)Global Path Planning:the global path planning is implemented by generating global atoms in the whole environment.The tiles in global atoms are selected at random and they have a uniform distribution in the whole map.Rough paths can be constructed from the global atoms which have a global view of the environment.

    2)Local Path Refinement:a group of local atoms are generated in the neighborhood of a global atom in the queue for local exploitation aiming at refining the path constructed by the global atom.The neighborhood is a circle area with center at the global atom and a fixed radius which determines the scope of the neighborhood.The number of local atoms in a group is determined by the depth of the stack where they will be stored.It is obviously that the group with larger number of local atom can carry out a higher level local exploitation.

    3)Fitness Evaluation:the length of a path constructed by an atom serves as the fitness value of the atom.The shorter the path is the better the atom is.The length of a constructed path is calculated by Eq.(4).

    wherePis the path;P(i)is theithtile inP,Pis considered infeasible if it can not guide us to the destination.

    4)Queue Update:as shown in Algorithm 2,the update of the queue is a two stage operation.Firstly,theithglobal atom in the queue will be replaced by the atom in theithstack if its fitness value is larger than the fitness value of the atom in theithstack.Secondly,the new generated atoms and atoms in the queue are sorted according to their fitness values and then the better ones are inserted into the queue in order.

    From the process of MOA,it can be seen that the management of multiple search groups increases the complexity of MOA compared with GA and PSO. However,the increased complexity is negligible because of the strong computational power of recent computers.

    5 Experiment

    5.1 Experimental Environment

    In this paper,tests on six 0-1 grid-based benchmark game(Dragon Age:Origins)maps are carried out by using Matlab 7.8(The Mathworks,Inc.,Natick,MA,USA)on the PC with a 1.6-GHz Intel Processor and 2.0-GB RAM.For each map,one of the benchmark test set problems given in Ref.[19]is chosen as the testing problem where there exists a shortest path with the length from 50 to 60.Details of these testing problems are listed in Table 1.

    Table 1 Testing problems

    These testing maps are illustrated in Fig.6 where astraight dash is drawn from the starting tile to the destination tile.From Fig.6,it can be seen that the problems can be roughly classified into two types according to their complexity.Problems in arena,den204d and den901d are considered as simple problems where the optimal paths are a little curve and the others are treated as complex problems where the optimal paths are zigzag.

    5.2 Parameters Setup

    To illustrate the strength of MOA,GA and PSO are chosen as the comparison methods implemented through using PSO toolbox and GA toolbox.These algorithms stop when a maximum of 100 iterations is reached.All the investigative methods have the same population size and dimension which are 20 and 12,respectively.They also use the same encoding scheme and fitness function.The used parameters of GA are from Ref.[20]where single point uniform cross-over with the rate of 0.8,ranking selection with the elite retention strategy and Gaussian mutation with the rate of 0.01.The best parameters of PSO suggested in the conclusion of Ref.[21]are as follows:the additional inertia weight is 0.729.The learning factors are set to 1.494.

    To compare the performance of these algorithms fairly,50 independent tests are carried out on each problem and the averaged path length curves against the number of iterations are plotted.The performance of all compared algorithms is measured in terms of the optimality of the located optimal solution which is indicated by the path length.The algorithm obtaining the shortest path length is considered to be the best in optimality.Further,the reason behind the result is also discussed according to the averaged path length curves against the number of iterations.

    Fig.6 Benchmark maps and problems

    6 Results and Discussion

    6.1 Iterative Process of MOA

    In order to make the whole process of MOA clearer,the paths encoded by global atoms at the first,33rd,66thand 100thiteration are illustrated in Fig.7. The path planning problem in the map named arena is chosen because there are multiple global optima in this problem.Comparison between the results at the first iteration and that at the 33rditeration shows that there is no feasible path at the first iteration and some feasible paths are achieved after 33 iterations,which suggests that the path planner based on MOA is effective in planning multiple feasible paths after some iterations of global exploration and local exploitation. Comparison between the results at the 66rditeration and that at the 100thiteration shows that the paths achieved at the 66rditeration are improved during 44 iterations. Obviously,the path planner based on MOA can movetoward the global optima through improving the optimality of intermediate solutions.It is worth noting that the path planner based on MOA has the ability to provide multiple solutions,thus,the name of MOA matches the reality.

    6.2 Comparison Among GA PSO and MOA

    To show the good performance of MOA,the curves of average path length against iteration number achieved by using GA,PSO and MOA are illustrated in Fig.8.

    Fig.7 Paths planed at the first,33rd,66thand 100thiteration

    Fig.8 Average path length versus number of iterations

    On simple problems including arena,den204d and den901d,after 100 iterations,the paths located by MOA and PSO are shorter than those obtained by GA,which shows the optimality of the solution located in MOA.From the averaged path length curve in the first column of Fig.8,it can be seen that MOA and PSO keep on improving the located path but GA stops improving the path.This suggests that GA is more likely to be trapped into local optima and cause premature convergence than MOA and PSO.The reasons are as follows:the diversity of chromosomes in GA decreases when the iteration number increases,which enhances its local exploitation at the cost of reducing global exploration,as a result,GA can hardly jump out of local traps only by means of mutation operator at the end of search;the global exploration in MOA is carried out by global atoms at the beginning of each iteration,thus,the global exploration will not be influenced by the local exploitation and will not decrease when the iteration number increases.

    On complex problems including den009d,den202d and den403d,the paths located by MOA are shorter than those obtained by GA and PSO;this demonstrates that MOA outperforms GA and PSO in the optimality of the located solution.From the averaged path length curve in the second column of Fig.8,it can be seen that both GA and PSO stop improving the historical located path after about 20 iterations but MOA gradually improves it.As a result,MOA obtains the shortest path after 100 iterations.The reason is that GA and PSO are easy to be trapped into local optima and cause premature convergence,so the increase ofiterations dose not contribute to improving the historical located path.However,MOA explores the solution space not at the beginning of the algorithm but at the beginning of each iteration,which prevents the global exploration ability from degeneration in the wake of the decrease of population diversity.To be specific,once the global atoms find better areas than local traps,the atoms in the local traps will be eliminated from the structure in the update queue step and new atoms will be generated in these better areas in the local path refinement step.

    7 Conclusions

    The grid-based path planner using multivariant optimization algorithm is proposed to solve the shortest planning problems in this paper.It is characterized by powerful global and local search ability and new solution encoding scheme.The proposed algorithm is tested on benchmark test sets and compared with two other popular swarm optimization algorithms,and the experimental results show that MOA has the ability to provide multiple solutions and outperforms GA and PSO in optimality indexed by the obtained path length especially on complex problems.MOA is a promising candidate method for shortest path planning problems on grid-based maps.

    Applying the proposed method to solve SSP problems in dynamic environments,it needs more steps to extend the current work.The multiple paths obtained by MOA-based planner will be users’alternative solutions when the optimal path becomes infeasible in dynamic environments.

    [1]Hong Ye,F(xiàn)ang Jiancheng.Development of ground control station and path planning for autonomous MUAV.Journal of Harbin Institute of Technology(New Series),2010,17(6):789-794.

    [2]Raja P,Pugazhenthi S.Optimal path planning of mobile robots:a review.International Journal of Physical Sciences,2012,7(9):1314-1320.

    [3]Mocholi J A,Jaen J,Catala A,et al.An emotionally biased ant colony algorithm for pathfinding in games.Expert Systems with Applications,2010,37(7):4921-4927.

    [4]Liang J J,Song H,Qu B Y.Performance evaluation of dynamic multi-swarm particle swarm optimizer with different constraint handling methods on path planning problems.Proceedings of IEEE Workshop on Memetic Computing.Piscataway:IEEE,2013.65-71.

    [5]Barraquand J,Langlois B,Latombe J C.Numerical potential field techniques for robot path planning.IEEE Transactions on Systems,Man and Cybernetics,1992,22(2):224-241.

    [6]Yu Zhenzhong,Yan Jihong,Zhao Jie,et al.Mobile robot path planning based on improved artificial potential field method.Journal of Harbin Institute of Technology,2011,43(1):50-55.(in Chinese)

    [7]Botea A,Müller M,Schaeffer J.Near optimal hierarchical path-finding.Journal of Game Development,2004,1(1):7-28.

    [8]Gao Fang,Zhao Qiang,Li Guixian.Path planning of continuum robot based on a new improved particle swarm optimization algorithm.Journal of Harbin Institute of Technology(New Series),2013,20(4):78-84.

    [9]Gong Yuejiao,Shen M,Zhang Jun,et al.Optimizing RFID network planning by using a particle swarm optimization algorithm with redundant reader elimination.IEEE Transactions on Industrial Informatics,2012,8(4):900-912.

    [10]Jin Feihu,Gao Huijun,Zhong Xiaojian.Research on path planning of robot using adaptive ant colony system.Journal of Harbin Institute of Technology,2010,42(7):1014-1018.(in Chinese)

    [11]Piao Songhao,Hong Bingrong.Robot path planning using genetic algorithms.Journal of Harbin Institute of Technology(New Series),2001,8(3):215-317.

    [12]Gemeinder M,Gerke M.GA-based path planning for mobile robot systems employing an active search algorithm. Applied Soft Computing,2003,3(2):149-158.

    [13]Qin Yuanqing,Sun Debao,Li Ning,et al.Path planning for mobile robot using the particle swarm optimization with mutation operator,2004.Proceedings of the IEEE Internat ional Conference on Machine Learning and Cybernetics. Piscataway:IEEE,2004.2473-2478.

    [14]Sooda K,Nair T R.A comparative analysis for determining the optimal path using PSO and GA.International Journal of Computer Applications,2011,32(4):8-12.

    [15]Qi Yong,Wei Zhiqiang,Yin Bo,et al.Path planning optimization based on reinforcement of artificial potential field.Journal of Harbin Institute of Technology,2009,41(3):130-133.(in Chinese)

    [16]Zhan Zhihui,Zhang Jun.Discrete particle swarm optimization for multiple destination routing problems. Applications of Evolutionary Computing.Heidelberg:Springer,2009.117-122.

    [17]Li Baolei,Shi Xinling,Gou Changxing,et al.Multivariant optimization algorithm for multimodal optimization. Proceedings of the International Conference on Mechanical Engineering,Materials and Energy.Switzerland:Trans Tech Publications Ltd.,2014.453-457.

    [18]Mohemmed A W,Sahoo N C,Geok T K.Solving shortest path problem using particle swarm optimization.Applied Soft Computing,2008,8(4):1643-1653.

    [19]Sturtevant N R.Benchmarks for grid-based path finding. IEEE Transactions on Computational Intelligence and AI in Games,2012,4(2):144-148.

    [20]Sugihara K,Smith J.Genetic algorithms for adaptive motion planning of an autonomous mobile robot. Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Piscataway:IEEE,1997.138-143.

    [21]Eberhart R C,Shi Y.Comparing inertia weights and constriction factors in particle swarm optimization. Proceedings of the IEEE Congress on Evolutionary Computation.Piscataway:IEEE,2000.84-88.

    TP24

    :1005-9113(2015)05-0089-08

    10.11916/j.issn.1005-9113.2015.05.014

    2014-05-24.

    Sponsored by the National Natural Science Foundation of China(Grant No.61261007,61002049),and the Key Program of Yunnan Natural Science Foundation(Grant No.2013FA008).

    ?Corresponding author.E-mail:xlshi@ynu.edu.cn.

    一级毛片黄色毛片免费观看视频| 大香蕉久久成人网| av黄色大香蕉| 亚洲国产成人一精品久久久| 成年av动漫网址| 精品亚洲成国产av| 丁香六月天网| 夜夜爽夜夜爽视频| 少妇精品久久久久久久| 巨乳人妻的诱惑在线观看| 国产午夜精品一二区理论片| 精品人妻熟女毛片av久久网站| 免费大片黄手机在线观看| 国产精品一区二区在线观看99| 在线天堂最新版资源| 纵有疾风起免费观看全集完整版| 伊人亚洲综合成人网| 亚洲av中文av极速乱| 国产 一区精品| 色婷婷av一区二区三区视频| 日本猛色少妇xxxxx猛交久久| 久久午夜综合久久蜜桃| 91精品伊人久久大香线蕉| 男女下面插进去视频免费观看 | 亚洲,一卡二卡三卡| 一级毛片我不卡| 性色avwww在线观看| a 毛片基地| 黄网站色视频无遮挡免费观看| 一级黄片播放器| 国产深夜福利视频在线观看| 80岁老熟妇乱子伦牲交| 欧美老熟妇乱子伦牲交| 免费看不卡的av| 老司机影院毛片| 最近最新中文字幕大全免费视频 | 男女国产视频网站| 日日撸夜夜添| 日韩熟女老妇一区二区性免费视频| 麻豆乱淫一区二区| 亚洲人与动物交配视频| 久久久久国产精品人妻一区二区| 亚洲欧美中文字幕日韩二区| 少妇 在线观看| 久久精品久久久久久久性| 51国产日韩欧美| 久久久久人妻精品一区果冻| 久久人人爽人人爽人人片va| 91在线精品国自产拍蜜月| 一本大道久久a久久精品| 飞空精品影院首页| 亚洲av在线观看美女高潮| 亚洲,欧美精品.| 永久网站在线| 日本与韩国留学比较| 国产一区亚洲一区在线观看| 久久国产亚洲av麻豆专区| 天堂俺去俺来也www色官网| 亚洲图色成人| 国产一区有黄有色的免费视频| 尾随美女入室| 少妇高潮的动态图| 看十八女毛片水多多多| 免费女性裸体啪啪无遮挡网站| 男女啪啪激烈高潮av片| 大香蕉久久成人网| 一本—道久久a久久精品蜜桃钙片| 欧美性感艳星| 久久韩国三级中文字幕| 日韩欧美精品免费久久| av国产久精品久网站免费入址| 人人澡人人妻人| 青青草视频在线视频观看| 中文字幕av电影在线播放| 欧美日韩国产mv在线观看视频| 大香蕉97超碰在线| 国内精品宾馆在线| 美女中出高潮动态图| 大话2 男鬼变身卡| 久久久久久久久久成人| 精品一区二区三区四区五区乱码 | 国产欧美日韩一区二区三区在线| 欧美老熟妇乱子伦牲交| 婷婷色综合大香蕉| 国语对白做爰xxxⅹ性视频网站| 丝袜喷水一区| 男女边摸边吃奶| 午夜久久久在线观看| 曰老女人黄片| 人人妻人人爽人人添夜夜欢视频| 亚洲欧美成人综合另类久久久| 毛片一级片免费看久久久久| 一个人免费看片子| 亚洲精品aⅴ在线观看| 黑人猛操日本美女一级片| 51国产日韩欧美| 宅男免费午夜| 日本av免费视频播放| 久久97久久精品| 国产欧美日韩一区二区三区在线| 日本黄大片高清| 少妇的逼好多水| 免费黄色在线免费观看| 美女中出高潮动态图| 捣出白浆h1v1| 午夜免费男女啪啪视频观看| 亚洲精品美女久久av网站| 亚洲中文av在线| 亚洲精品日韩在线中文字幕| 在线观看三级黄色| 丰满迷人的少妇在线观看| 美女福利国产在线| 熟女人妻精品中文字幕| 丝瓜视频免费看黄片| 欧美激情 高清一区二区三区| 久久女婷五月综合色啪小说| 1024视频免费在线观看| 国产欧美日韩综合在线一区二区| 七月丁香在线播放| 男女国产视频网站| 大片免费播放器 马上看| 超碰97精品在线观看| 男女无遮挡免费网站观看| 国产一区二区激情短视频 | 热99久久久久精品小说推荐| 插逼视频在线观看| 亚洲色图 男人天堂 中文字幕 | 最近的中文字幕免费完整| 亚洲av在线观看美女高潮| 亚洲成av片中文字幕在线观看 | 国产探花极品一区二区| 亚洲一码二码三码区别大吗| 免费高清在线观看视频在线观看| 精品久久久久久电影网| 欧美另类一区| 啦啦啦中文免费视频观看日本| 亚洲国产精品成人久久小说| 天堂俺去俺来也www色官网| av在线老鸭窝| 涩涩av久久男人的天堂| 久久婷婷青草| 亚洲中文av在线| 天堂中文最新版在线下载| 国产黄频视频在线观看| 免费不卡的大黄色大毛片视频在线观看| av天堂久久9| 日韩免费高清中文字幕av| 春色校园在线视频观看| 国产精品久久久久久久久免| h视频一区二区三区| 女人久久www免费人成看片| 成年美女黄网站色视频大全免费| 日本av免费视频播放| 久久久国产一区二区| 亚洲欧美清纯卡通| 热99国产精品久久久久久7| 狠狠精品人妻久久久久久综合| 97超碰精品成人国产| 免费在线观看完整版高清| 午夜福利在线观看免费完整高清在| 亚洲av日韩在线播放| 又大又黄又爽视频免费| 搡老乐熟女国产| 免费看av在线观看网站| 成人亚洲精品一区在线观看| 久久人人爽人人爽人人片va| 999精品在线视频| 成年人免费黄色播放视频| 极品人妻少妇av视频| 中文字幕精品免费在线观看视频 | 欧美精品av麻豆av| 女的被弄到高潮叫床怎么办| 人成视频在线观看免费观看| 国产精品三级大全| 极品少妇高潮喷水抽搐| 国产精品一国产av| 最后的刺客免费高清国语| h视频一区二区三区| 男的添女的下面高潮视频| 亚洲av福利一区| 国产视频首页在线观看| 男男h啪啪无遮挡| 啦啦啦啦在线视频资源| 午夜免费观看性视频| 亚洲国产日韩一区二区| 欧美精品人与动牲交sv欧美| 欧美精品一区二区免费开放| 久久综合国产亚洲精品| 99热全是精品| 亚洲经典国产精华液单| 国产精品一区二区在线不卡| 国产在线一区二区三区精| 欧美精品一区二区大全| 国精品久久久久久国模美| 国产亚洲精品第一综合不卡 | 中文字幕免费在线视频6| 亚洲国产精品成人久久小说| 黄色配什么色好看| 国产精品欧美亚洲77777| 精品99又大又爽又粗少妇毛片| 亚洲欧美精品自产自拍| 日日爽夜夜爽网站| 久久毛片免费看一区二区三区| 国产一区二区在线观看日韩| 高清欧美精品videossex| 尾随美女入室| 亚洲欧洲日产国产| 乱码一卡2卡4卡精品| 日产精品乱码卡一卡2卡三| 天堂8中文在线网| 国产在线一区二区三区精| 亚洲精品视频女| 美女福利国产在线| 午夜视频国产福利| 十分钟在线观看高清视频www| 久久精品夜色国产| 久久女婷五月综合色啪小说| 黄色怎么调成土黄色| tube8黄色片| 成人手机av| 午夜91福利影院| 午夜免费观看性视频| 亚洲av成人精品一二三区| 中文乱码字字幕精品一区二区三区| 在线精品无人区一区二区三| xxx大片免费视频| 啦啦啦啦在线视频资源| 男女边摸边吃奶| 午夜老司机福利剧场| 人人妻人人爽人人添夜夜欢视频| 亚洲欧洲日产国产| 国产精品久久久av美女十八| 久久精品国产自在天天线| 美女国产高潮福利片在线看| 中国美白少妇内射xxxbb| 一本—道久久a久久精品蜜桃钙片| a级毛色黄片| 国产男女超爽视频在线观看| 久久97久久精品| 久热这里只有精品99| 成人黄色视频免费在线看| 大香蕉97超碰在线| 看免费成人av毛片| 在线观看国产h片| 丁香六月天网| 又大又黄又爽视频免费| 成人二区视频| 免费高清在线观看视频在线观看| 午夜老司机福利剧场| 亚洲精品一二三| 日本色播在线视频| 久久人人爽人人片av| 99精国产麻豆久久婷婷| 天美传媒精品一区二区| 亚洲,欧美精品.| 在线观看三级黄色| 久久久久久人妻| 亚洲精品自拍成人| 高清欧美精品videossex| 国产欧美亚洲国产| 91久久精品国产一区二区三区| 中文字幕制服av| 日韩av不卡免费在线播放| 国产免费一区二区三区四区乱码| 亚洲精品美女久久av网站| 黑人猛操日本美女一级片| 国产亚洲av片在线观看秒播厂| 亚洲天堂av无毛| 欧美日韩av久久| 九九在线视频观看精品| 午夜福利视频精品| 午夜福利,免费看| 国产男女内射视频| 少妇精品久久久久久久| 国产一区二区在线观看av| 国产精品国产三级国产专区5o| 夫妻性生交免费视频一级片| 亚洲av电影在线观看一区二区三区| 中文字幕制服av| 亚洲一区二区三区欧美精品| 亚洲av免费高清在线观看| www.av在线官网国产| 亚洲欧洲日产国产| 成年动漫av网址| 十分钟在线观看高清视频www| 久久99热6这里只有精品| 人人妻人人澡人人爽人人夜夜| 亚洲av电影在线观看一区二区三区| 亚洲国产毛片av蜜桃av| 亚洲综合色惰| 曰老女人黄片| 你懂的网址亚洲精品在线观看| 中文字幕av电影在线播放| 尾随美女入室| 久久ye,这里只有精品| 高清在线视频一区二区三区| 日韩制服骚丝袜av| 美女视频免费永久观看网站| 久久99蜜桃精品久久| 国产片特级美女逼逼视频| 亚洲经典国产精华液单| 99久久综合免费| 久久久久久久久久成人| 人人澡人人妻人| 另类亚洲欧美激情| 亚洲国产欧美在线一区| av在线app专区| 99香蕉大伊视频| 国产日韩欧美视频二区| 亚洲精品久久久久久婷婷小说| 热99国产精品久久久久久7| 国产高清不卡午夜福利| 热re99久久国产66热| 18禁国产床啪视频网站| 2018国产大陆天天弄谢| 成人综合一区亚洲| 草草在线视频免费看| 欧美3d第一页| 亚洲色图 男人天堂 中文字幕 | 大话2 男鬼变身卡| 午夜老司机福利剧场| 久久久久久久久久久免费av| 国产1区2区3区精品| 99re6热这里在线精品视频| 中文字幕最新亚洲高清| 中国美白少妇内射xxxbb| 激情五月婷婷亚洲| 蜜桃在线观看..| 中文精品一卡2卡3卡4更新| 亚洲精品456在线播放app| 亚洲精品日韩在线中文字幕| 国产精品国产三级国产专区5o| 日韩一本色道免费dvd| 久久热在线av| 精品人妻在线不人妻| 五月玫瑰六月丁香| 美女中出高潮动态图| 大码成人一级视频| 国产亚洲av片在线观看秒播厂| 精品一区二区免费观看| av黄色大香蕉| 少妇的丰满在线观看| 韩国精品一区二区三区 | 国产亚洲av片在线观看秒播厂| 五月伊人婷婷丁香| 亚洲国产最新在线播放| 欧美精品高潮呻吟av久久| 天天躁夜夜躁狠狠久久av| 一级a做视频免费观看| 亚洲精品第二区| 久久久久久久大尺度免费视频| 99re6热这里在线精品视频| 成人国语在线视频| 99久国产av精品国产电影| 成人午夜精彩视频在线观看| 交换朋友夫妻互换小说| 亚洲情色 制服丝袜| 日韩制服丝袜自拍偷拍| 在线看a的网站| 欧美精品国产亚洲| 最近的中文字幕免费完整| 久久亚洲国产成人精品v| 久久久久久伊人网av| 国产精品一二三区在线看| av在线老鸭窝| 欧美精品国产亚洲| 精品人妻在线不人妻| 夫妻午夜视频| 国产精品不卡视频一区二区| 午夜免费鲁丝| 性高湖久久久久久久久免费观看| 精品少妇内射三级| 日韩熟女老妇一区二区性免费视频| 国产av国产精品国产| 久久久久精品人妻al黑| 日日爽夜夜爽网站| 久久久久久久久久久免费av| av电影中文网址| 熟女电影av网| 亚洲精品av麻豆狂野| 午夜福利,免费看| 久久久久人妻精品一区果冻| 亚洲精品美女久久久久99蜜臀 | 精品酒店卫生间| av在线播放精品| 精品酒店卫生间| 毛片一级片免费看久久久久| 国产熟女欧美一区二区| 国产片特级美女逼逼视频| 成人毛片a级毛片在线播放| 国产成人aa在线观看| 久久久久久久久久久久大奶| 侵犯人妻中文字幕一二三四区| 久久久亚洲精品成人影院| 97人妻天天添夜夜摸| 1024视频免费在线观看| 超碰97精品在线观看| 五月伊人婷婷丁香| 日日啪夜夜爽| 国产精品人妻久久久影院| 又黄又爽又刺激的免费视频.| 亚洲国产欧美在线一区| 亚洲人成网站在线观看播放| 日韩成人av中文字幕在线观看| 亚洲精品美女久久久久99蜜臀 | 纵有疾风起免费观看全集完整版| 亚洲五月色婷婷综合| 丝袜人妻中文字幕| 亚洲精品456在线播放app| 欧美bdsm另类| 精品亚洲乱码少妇综合久久| 中文精品一卡2卡3卡4更新| 午夜免费观看性视频| 国产精品一区二区在线不卡| 国产欧美亚洲国产| 性色avwww在线观看| 大话2 男鬼变身卡| 99热全是精品| av福利片在线| 97在线人人人人妻| 亚洲av福利一区| 人妻 亚洲 视频| 亚洲精品国产色婷婷电影| 欧美+日韩+精品| 久久青草综合色| 国产精品.久久久| 人妻人人澡人人爽人人| 亚洲av欧美aⅴ国产| 少妇 在线观看| 香蕉国产在线看| 男人舔女人的私密视频| 久久久久视频综合| 日韩欧美一区视频在线观看| 久久热在线av| 人人妻人人澡人人爽人人夜夜| 中文字幕精品免费在线观看视频 | 国产精品久久久久久久久免| 国产成人91sexporn| 亚洲第一av免费看| 久久ye,这里只有精品| 久久精品国产亚洲av涩爱| 男男h啪啪无遮挡| 免费观看av网站的网址| 水蜜桃什么品种好| 日本色播在线视频| 精品亚洲成国产av| 一级毛片电影观看| 免费大片黄手机在线观看| 大片免费播放器 马上看| 成年美女黄网站色视频大全免费| 久久久久国产精品人妻一区二区| 欧美日韩精品成人综合77777| 高清在线视频一区二区三区| 国产亚洲午夜精品一区二区久久| 中国美白少妇内射xxxbb| 精品少妇久久久久久888优播| 免费av不卡在线播放| 国产亚洲精品久久久com| a级毛片在线看网站| 蜜桃国产av成人99| av免费在线看不卡| 久久精品aⅴ一区二区三区四区 | 日韩免费高清中文字幕av| 26uuu在线亚洲综合色| 黄网站色视频无遮挡免费观看| 黄片无遮挡物在线观看| 九九爱精品视频在线观看| 亚洲综合色惰| 夜夜爽夜夜爽视频| 久久影院123| 99国产精品免费福利视频| 国产免费视频播放在线视频| 高清黄色对白视频在线免费看| 久久人人爽人人片av| 欧美bdsm另类| 日韩视频在线欧美| 人人妻人人添人人爽欧美一区卜| 国产白丝娇喘喷水9色精品| 国产精品熟女久久久久浪| 久久人妻熟女aⅴ| 少妇熟女欧美另类| av播播在线观看一区| 亚洲精品一区蜜桃| 亚洲成国产人片在线观看| 青春草视频在线免费观看| 搡老乐熟女国产| 国产免费现黄频在线看| 丝袜脚勾引网站| 成人黄色视频免费在线看| 精品亚洲成a人片在线观看| 一本色道久久久久久精品综合| 热99久久久久精品小说推荐| 制服人妻中文乱码| 看十八女毛片水多多多| 国产精品一区www在线观看| 国产精品成人在线| 狠狠精品人妻久久久久久综合| 精品午夜福利在线看| 国产不卡av网站在线观看| 高清不卡的av网站| 男女下面插进去视频免费观看 | 亚洲av.av天堂| 考比视频在线观看| 热re99久久国产66热| av网站免费在线观看视频| 亚洲成人一二三区av| 精品一区二区免费观看| 国产又色又爽无遮挡免| 卡戴珊不雅视频在线播放| 精品国产一区二区三区久久久樱花| www日本在线高清视频| 2022亚洲国产成人精品| 韩国精品一区二区三区 | 亚洲性久久影院| 91精品国产国语对白视频| 制服丝袜香蕉在线| 在线观看www视频免费| 一级毛片 在线播放| 97超碰精品成人国产| 王馨瑶露胸无遮挡在线观看| 国产精品久久久久久久电影| 亚洲欧美清纯卡通| 寂寞人妻少妇视频99o| 一级毛片我不卡| 国产有黄有色有爽视频| 丝袜美足系列| 精品酒店卫生间| 91精品国产国语对白视频| 亚洲成色77777| 国产精品久久久久久av不卡| 波野结衣二区三区在线| 丝袜美足系列| 波野结衣二区三区在线| 王馨瑶露胸无遮挡在线观看| 久久国产精品大桥未久av| 亚洲高清免费不卡视频| 黄色一级大片看看| 免费观看性生交大片5| 赤兔流量卡办理| 伦理电影免费视频| 亚洲精华国产精华液的使用体验| 免费不卡的大黄色大毛片视频在线观看| 在线观看人妻少妇| 一本色道久久久久久精品综合| 国产色爽女视频免费观看| 99热国产这里只有精品6| 女的被弄到高潮叫床怎么办| 一区二区av电影网| 国产日韩欧美亚洲二区| 搡老乐熟女国产| 国产av码专区亚洲av| 啦啦啦啦在线视频资源| 亚洲国产欧美在线一区| 久久久久国产网址| av不卡在线播放| 国产熟女午夜一区二区三区| 老女人水多毛片| 精品一区在线观看国产| 免费高清在线观看日韩| 韩国av在线不卡| 18禁裸乳无遮挡动漫免费视频| 日韩欧美一区视频在线观看| 精品少妇久久久久久888优播| 欧美xxⅹ黑人| 午夜精品国产一区二区电影| 免费久久久久久久精品成人欧美视频 | 人妻 亚洲 视频| 午夜91福利影院| av播播在线观看一区| 国产xxxxx性猛交| 中文字幕av电影在线播放| 91午夜精品亚洲一区二区三区| 97在线人人人人妻| 人人澡人人妻人| 国产成人精品无人区| 纯流量卡能插随身wifi吗| 黑人猛操日本美女一级片| 国产av精品麻豆| 激情视频va一区二区三区| 777米奇影视久久| 女人久久www免费人成看片| 99热6这里只有精品| 18禁裸乳无遮挡动漫免费视频| 免费看光身美女| 最近2019中文字幕mv第一页| 国产一区二区在线观看av| 国产毛片在线视频| 亚洲四区av| 99热国产这里只有精品6| 免费看不卡的av| 国内精品宾馆在线| 国产国语露脸激情在线看| 宅男免费午夜| 亚洲精品日本国产第一区| 精品国产一区二区久久| 久久av网站| 免费日韩欧美在线观看| 国产一区二区在线观看日韩| 免费看不卡的av| 国产精品熟女久久久久浪| 日韩不卡一区二区三区视频在线| 亚洲,欧美精品.| 亚洲国产色片| av片东京热男人的天堂| 久久久久久久久久成人| 水蜜桃什么品种好| 中国三级夫妇交换| 国产在线视频一区二区| 最近的中文字幕免费完整| 久久久久视频综合| 国产白丝娇喘喷水9色精品| 激情视频va一区二区三区| 午夜久久久在线观看| 最近中文字幕2019免费版| 999精品在线视频|