├── ACOPN_fixed.m ├── ACOPN_original.m ├── README.md ├── Reference ├── Referance │ ├── Huang2010_Article_AHybridHeuristicSearchAlgorith.pdf │ ├── Petri网融合蚁群算法的物流配送路径规划.pdf │ ├── Scheuling flexible manufacturing systems using Petri nets.pdf │ ├── 一种基于Petri网的多目标无死锁蚁群调度算法.pdf │ ├── 基于Petri网的扫地机器人路径设计分析_胡源.pdf │ ├── 基于蚁群算法的Petri网最优路径序列寻找.pdf │ ├── 基于蚁群算法的随机Petri网最优路径序列寻找.pdf │ ├── 蚁群算法的随机Petri网分析与路径寻优.pdf │ ├── 论文翻译上.pdf │ ├── 论文翻译下.pdf │ └── 面向地形勘察使命的AUV群体协同使命控制方法研究.pdf └── 参考资料 ├── Screenshots of Result ├── ACOTNP.png ├── ACOTPN1.fig ├── ACOTPN1.png ├── ACOTPN10.png ├── ACOTPN2.png ├── ACOTPN3.png ├── ACOTPN4.png ├── ACOTPN5.png ├── ACOTPN6.png ├── ACOTPN7.png ├── ACOTPN8.png ├── ACOTPN9.png ├── G=200.png ├── G=500.png ├── Petri net 1.png ├── Petri net 2.png ├── Q=1.png ├── Q=10.png ├── QQ截图20210525203719.jpg ├── ant1.png ├── m=10.png ├── m=50.png ├── untitled1.fig ├── untitled1.png ├── 捕获1.PNG ├── 捕获2.PNG ├── 捕获3.PNG ├── 时延库所.jpg ├── 时延库所Petri网.jpg ├── 流程图.jpg └── 绪论图片.jpg ├── 基于Petri网的蚁群算法代码.txt └── 蚁群算法代码.txt /ACOPN_fixed.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DavidLee233/PetriNet-Path-Planning-Based-on-Robot-Path-Planning/342d52b1a5da6fd21ec474d29cf53123e17d1e4a/ACOPN_fixed.m -------------------------------------------------------------------------------- /ACOPN_original.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DavidLee233/PetriNet-Path-Planning-Based-on-Robot-Path-Planning/342d52b1a5da6fd21ec474d29cf53123e17d1e4a/ACOPN_original.m -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # PetriNet-Path-Planning-Based-on-Robot-Path-Planning 2 | 主要介绍了一种基于蚁群算法的时延Petri网（ACOTPN）路径规划算法，它是根据蚁群算法的原理和时延库所Petri网的路径规划原理合成的一种新算法。当ACOTPN运行时，蚂蚁在网中的变迁行走并在变迁与变迁之间留下信息素，在遍历同时不仅更新变迁序列，而且会更新网标识，反过来通过判断网标识是否处于终止标识来决定蚂蚁遍历是否结束。 3 | 4 | 5 | 两种版本的代码都能运行，不过这是毕设里的代码，如果你也是写毕业设计最好还是大改一下会对降重有很大帮助！！ 6 | Petri网建模软件我用的是PIPE，很好的一款软件，但是有个局限性就是该软件必须在JAVA环境下运行，下载解压后双击 launch.bat即可打开！！ 7 | JAVA环境的配置很简单，直接百度即可。 8 | 关于PIPE软件我在这里直接分享 链接：https://pan.baidu.com/s/18_W59-H53pSKjhwIntNbKA 提取码：na89 9 | -------------------------------------------------------------------------------- /Reference/Referance/Huang2010_Article_AHybridHeuristicSearchAlgorith.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DavidLee233/PetriNet-Path-Planning-Based-on-Robot-Path-Planning/342d52b1a5da6fd21ec474d29cf53123e17d1e4a/Reference/Referance/Huang2010_Article_AHybridHeuristicSearchAlgorith.pdf -------------------------------------------------------------------------------- /Reference/Referance/Petri网融合蚁群算法的物流配送路径规划.pdf: 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M_1=[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]; %网中25个标识对应的符号 7 | 6. M_2=[0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]; 8 | 7. M_3=[0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]; 9 | 8. M_4=[0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]; 10 | 9. M_5=[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]; 11 | 10. M_6=[0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]; 12 | 11. M_7=[0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]; 13 | 12. M_8=[0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]; 14 | 13. M_9=[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]; 15 | 14. M_10=[0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]; 16 | 15. M_11=[0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]; 17 | 16. M_12=[0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]; 18 | 17. M_13=[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]; 19 | 18. M_14=[0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]; 20 | 19. M_15=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]; 21 | 20. M_16=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]; 22 | 21. M_17=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]; 23 | 22. M_18=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0]; 24 | 23. M_19=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]; 25 | 24. M_20=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0]; 26 | 25. M_21=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0]; 27 | 26. M_22=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]; 28 | 27. M_23=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]; 29 | 28. M_24=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0]; 30 | 29. M_25=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]; 31 | 30. nodes_data(1,:)={1,[2,3],[1,1],[M_2;M_3]}; %根据变迁的邻近变迁的发生权构造变迁元胞组（其中第二列代表此变迁发生后哪些变迁会发生，第三列代表此变迁到下一个变迁需要走过的路径距离，第四列代表此变迁到下一个变迁之后标识会更新到什么状态） 32 | 31. nodes_data(2,:)={2,[4,5],[1,sqrt(2)],[M_4;M_5]}; 33 | 32. nodes_data(3,:)={3,[6],[1],[M_6]}; 34 | 33. nodes_data(4,:)={4,[7,8],[sqrt(2),1],[M_7;M_5]}; 35 | 34. nodes_data(5,:)={5,[9,10],[sqrt(2),1],[M_8;M_7]}; 36 | 35. nodes_data(6,:)={6,[11,12],[1,sqrt(2)],[M_9;M_10]}; 37 | 36. nodes_data(7,:)={7,[13,14],[1,1],[M_8;M_13]}; 38 | 37. nodes_data(8,:)={8,[9,10],[sqrt(2),1],[M_8;M_7]}; 39 | 38. nodes_data(9,:)={9,[15],[1],[M_18]}; 40 | 39. nodes_data(10,:)={10,[13,14],[1,1],[M_8;M_13]}; 41 | 40. nodes_data(11,:)={11,[16,17],[1,1],[M_10;M_11]}; 42 | 41. nodes_data(12,:)={12,[18],[1],[M_12]}; 43 | 42. nodes_data(13,:)={13,[15],[1],[M_18]}; 44 | 43. nodes_data(14,:)={14,[23,24],[sqrt(2),1],[M_14;M_15]}; 45 | 44. nodes_data(15,:)={15,[29],[1],[M_19]}; 46 | 45. nodes_data(16,:)={16,[18],[1],[M_12]}; 47 | 46. nodes_data(17,:)={17,[19,20],[1,sqrt(2)],[M_16;M_17]}; 48 | 47. nodes_data(18,:)={18,[21,22],[1,sqrt(2)],[M_18;M_19]}; 49 | 48. nodes_data(19,:)={19,[27],[1],[M_17]}; 50 | 49. nodes_data(20,:)={20,[28],[1],[M_20]}; 51 | 50. nodes_data(21,:)={21,[29],[1],[M_19]}; 52 | 51. nodes_data(22,:)={22,[30,31],[1,sqrt(2)],[M_23;M_24]}; 53 | 52. nodes_data(23,:)={23,[25],[1],[M_21]}; 54 | 53. nodes_data(24,:)={24,[26],[1],[M_14]}; 55 | 54. nodes_data(25,:)={25,[33],[1],[M_22]}; 56 | 55. nodes_data(26,:)={26,[25],[1],[M_21]}; 57 | 56. nodes_data(27,:)={27,[28],[1],[M_20]}; 58 | 57. nodes_data(28,:)={28,[32],[1],[M_23]}; 59 | 58. nodes_data(29,:)={29,[30,31],[1,sqrt(2)],[M_23;M_24]}; 60 | 59. nodes_data(30,:)={30,[35],[1],[M_24]}; 61 | 60. nodes_data(31,:)={31,[36],[1],[M_25]}; 62 | 61. nodes_data(32,:)={32,[35],[1],[M_24]}; 63 | 62. nodes_data(33,:)={33,[34],[1],[M_25]}; 64 | 63. nodes_data(35,:)={35,[36],[1],[M_25]}; 65 | 64. %% 始末节点%% 66 | 65. node_start=1; 67 | 66. node_end=[33,36]; 68 | 67. %%% 蚁群定义%%%%% 69 | 68. m=50; % 蚂蚁数量 70 | 69. n=size(nodes_data,1); % 节点数量 71 | 70. alpha=1; % 信息素重要程度因子 72 | 71. beta=5; % 启发函数重要程度因子 73 | 72. Rho=0.5; % 信息素挥发因子 74 | 73. Q=1; % 信息素增加强度系数 75 | 74. %%迭代过程初始化定义%%%% 76 | 75. iter=1; % 迭代次数初值 77 | 76. iter_max=100; % 最大迭代次数 78 | 77. Route_best=cell(iter_max,1); % 各代最佳路径 79 | 78. Length_best=zeros(iter_max,1); % 各代最佳路径长度 80 | 79. Length_ave=zeros(iter_max,1); % 各代路径平均长度 81 | 80. Place_best=cell(iter_max,1); % 各代最佳路径访问的库所 82 | 81. %%将信息素、挥发因子一并放入nodes_data中%%%%% 83 | 82. Delta_Tau_initial=nodes_data(:,1:2); 84 | 83. for i=1:size(nodes_data,1) 85 | 84. nodes_data{i,5}=ones(1,length(nodes_data{i,3})); % 初始信息素均设置为1 86 | 85. nodes_data{i,6}=1./nodes_data{i,3}; % 启发函数设置为距离的倒数 87 | 86. Delta_Tau_initial{i,3}=zeros(1,length(nodes_data{i,3})); % 信息素变化量均为0 88 | 87. end 89 | 88. %% 迭代寻找最佳路径%%% 90 | 89. while iterPc(k)&&randnum=1 69 | 68. %%%%%%%%转轮赌法选择下一步怎么走%%%%%%%%%%% 70 | 69. PP=zeros(Len_LJD); 71 | 70. for i=1:Len_LJD 72 | 71. PP(i)=(Tau(W,LJD(i))^Alpha)*((Eta(LJD(i)))^Beta); 73 | 72. end 74 | 73. sumpp=sum(PP); 75 | 74. PP=PP/sumpp; %建立概率分布 76 | 75. Pcum(1)=PP(1); 77 | 76. for i=2:Len_LJD 78 | 77. Pcum(i)=Pcum(i-1)+PP(i); 79 | 78. end 80 | 79. Select=find(Pcum>=rand); 81 | 80. to_visit=LJD(Select(1)); 82 | 81. %%%%%%%%%%%%状态更新和记录%%%%%%%%%%%% 83 | 82. Path=[Path,to_visit]; %路径增加 84 | 83. PLkm=PLkm+DD(W,to_visit); %路径长度增加 85 | 84. W=to_visit; %蚂蚁移到下一个节点 86 | 85. for kk=1:N 87 | 86. if TABUkm(kk)==0 88 | 87. DD(W,kk)=0; 89 | 88. DD(kk,W)=0; 90 | 89. end 91 | 90. end 92 | 91. TABUkm(W)=0; %已访问过的节点从禁忌表中删除 93 | 92. DW=DD(W,:); 94 | 93. DW1=find(DW); 95 | 94. for j=1:length(DW1) 96 | 95. if TABUkm(DW1(j))==0 97 | 96. DW(j)=0; 98 | 97. end 99 | 98. end 100 | 99. LJD=find(DW); 101 | 100. Len_LJD=length(LJD); %可选节点的个数 102 | 101. end 103 | 102. %%%%记下每一代每一只蚂蚁的觅食路线和路线长度%%%%% 104 | 103. ROUTES{k,m}=Path; 105 | 104. if Path(end)==E 106 | 105. PL(k,m)=PLkm; 107 | 106. if PLkm