[RFEDA原创]基于遗传算法的机器人路径规划MATLAB源码

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算法的思路如下：取各障碍物顶点连线的中点为路径点，相互连接各路径点，将机器人移动的起点和终点限制在各路径点上，利用Dijkstra算法来求网络图的最短路径，找到从起点P1到终点Pn的最短路径，由于上述算法使用了连接线中点的条件，不是整个规划空间的最优路径，然后利用遗传算法对找到的最短路径各个路径点Pi (i=1,2,…n)调整，让各路径点在相应障碍物端点连线上滑动，利用Pi= Pi1+ti×(Pi2-Pi1)（ti∈[0,1] i=1,2,…n）即可确定相应的Pi，即为新的路径点，连接此路径点为最优路径。本源码由GreenSim团队原创，转载请注明，有意购买源码或代写相关程序，请与GreenSim团队联系QQ:761222791 nx{_^sK  
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function [L1,XY1,L2,XY2]=JQRLJGH(XX,YY) *12,MO>go  
%% 基于Dijkstra和遗传算法的机器人路径规划演示程序 7{<:g!  
% GreenSim团队原创作品，转载请注明 cp D=9k!*K  
% Email:greensim@163.com Ky"]L~8$  
%输入参数在函数体内部定义 -L%J,f[&,  
%输出参数为 M"QT(u+  
%  L1    由Dijkstra算法得出的最短路径长度 &'%b1CbE  
%  XY1   由Dijkstra算法得出的最短路径经过节点的坐标 _dQVundH  
%  L2    由遗传算法得出的最短路径长度 q\+khy,k  
%  XY2   由遗传算法得出的最短路径经过节点的坐标 hcWYz  
%程序输出的图片有 JjBG9Rp{  
%  Fig1  环境地图（包括：边界、障碍物、障碍物顶点之间的连线、Dijkstra的网络图结构） SN w3xO!;&  
%  Fig2  由Dijkstra算法得到的最短路径 I+nKaN+8i  
%  Fig3  由遗传算法得到的最短路径 <}e2\x  
%  Fig4  遗传算法的收敛曲线（迄今为止找到的最优解、种群平均适应值） j7LuN  
%% 画Fig1 Ik{[BRzUgt  
figure(1); hf5yTs  
PlotGraph; h SGI  
title('地形图及网络拓扑结构') +um Ua  
PD=inf*ones(26,26); "},0Cs  
for i=1:26 zadn`B#2  
    for j=1:26 >K]s)VuWR  
        if D(i,j)==1 dnRS$$9#  
            x1=XY(i,5); 5:3%RTLG  
            y1=XY(i,6); yGGQ;!/  
            x2=XY(j,5); QuG=am?l`  
            y2=XY(j,6); 7G,{BBB  
            dist=((x1-x2)^2+(y1-y2)^2)^0.5; &T7|f!y  
            PD(i,j)=dist; /u hA\m(  
        end <cC0l-=  
    end s?qRy 2  
end Fh2$,$ 2  
%% 调用最短路算法求最短路 P-B3<~*i!  
s=1;%出发点 Q[g>ee  
t=26;%目标点 yb 7  
[L,R]=ZuiDuanLu(PD,s,t); gz{~\0y  
L1=L(end); )ro3yq4??  
XY1=XY(R,5:6); LOm*=MVex  
%% 绘制由最短路算法得到的最短路径 ,&+"|,m  
figure(2); Kob i!  
PlotGraph; rHS;wT  
hold on oW0A8_|9  
for i=1:(length(R)-1) y2"PKBK\_  
    x1=XY1(i,1); #jY\l&E  
    y1=XY1(i,2); 3O{*~D&n  
    x2=XY1(i+1,1); ;ZnSWIF2  
    y2=XY1(i+1,2); 1oD1ia#  
    plot([x1,x2],[y1,y2],'k'); 82w;}(!  
    hold on syuW>Z8s
  
end SVr3OyzI  
title('由Dijkstra算法得到的初始路径') SZ/}2_;  
%% 使用遗传算法进一步寻找最短路 .I~#o$6  
%第一步：变量初始化 $bf&ct*$h  
M=50;%进化代数设置 OnD!*jy  
N=20;%种群规模设置 4<`Qyul-  
Pm=0.3;%变异概率设置 PwF 1Pr`r  
LC1=zeros(1,M); jg2UX  
LC2=zeros(1,M); R$[nYw  
Yp=L1; %BdQ.\4DS  
%第二步：随机产生初始种群 uJBs3X  
X1=XY(R,1); '.Ww*N  
Y1=XY(R,2); PKDzIA~T  
X2=XY(R,3); x3./  
Y2=XY(R,4); n-HQk7=mQ  
for i=1:N r
L{3O4O  
    farm{i}=rand(1,aaa); G8]DK3#  
end $!~R'N c  
% 以下是进化迭代过程 N?Wx-pK  
counter=0;%设置迭代计数器 YH_m
WN\Wu  
while counter<M%停止条件为达到最大迭代次数     `2}Frw+?  
    %% 第三步：交叉 OR{<)L  
    %交叉采用双亲双子单点交叉 h6Femis  
    newfarm=cell(1,2*N);%用于存储子代的细胞结构 rE:"8d}z  
    Ser=randperm(N);%两两随机配对的配对表 ^]x%z*6  
    A=farm{Ser(1)};%取出父代A 4!%@{H`3  
    B=farm{Ser(2)};%取出父代B 96L-bBtyY  
    P0=unidrnd(aaa-1);%随机选择交叉点 1|]IWX|  
    a=[A(:,1:P0),B(:,(P0+1):end)];%产生子代a Vjv~RNGF  
    b=[B(:,1:P0),A(:,(P0+1):end)];%产生子代b r>!$eqX_  
    newfarm{2*N-1}=a;%加入子代种群 _G$SA-W(  
    newfarm{2*N}=b; pN\YAc*@:  
    for i=1:(N-1) <K^{36h  
        A=farm{Ser(i)}; HC%tJ:G  
        B=farm{Ser(i+1)}; $0uh8RB  
        newfarm{2*i}=b; B=0U^wL  
    end Z%h
_g-C  
    FARM=[farm,newfarm];%新旧种群合并     " Bz\<e&u  
    %% 第四步：选择复制 [w0@7p"7
 
    SER=randperm(2*N); ny(`An  
    FITNESS=zeros(1,2*N); ;$`5L"I5$  
    fitness=zeros(1,N); '7lHWqN<  
    for i=1:(2*N) Se0!-NUK0  
        PP=FARM{i}; 2kP0//  
        FITNESS(i)=MinFun(PP,X1,X2,Y1,Y2);%调用目标函数 & XS2q0-x  
    end o'oA.'ul  
    for i=1:N sj;n1t}$S  
        f1=FITNESS(SER(2*i-1)); )K=%s%3h<  
        f2=FITNESS(SER(2*i)); {P'_s]B)  
        if f1<=f2 }01c7/DRP<  
        else Yf=an`"  
            farm{i}=FARM{SER(2*i)}; (d> M/x?W  
            fitness(i)=FITNESS(SER(2*i)); goE \C  
        end #hp7@ Tu  
    end     SJseP_-  
    %记录最佳个体和收敛曲线 W0zRV9"P  
    minfitness=min(fitness); kIC$ai6.  
    meanfitness=mean(fitness); 7H$I9e  
    if minfitness<Yp e8WuAI86  
        pos=find(fitness==minfitness); \&{a/e2:S  
        Xp=farm{pos(1)}; h/)kd3$*'  
        Yp=minfitness; )[t zAaP7  
    end y4@zi"G  
    if counter==10 >i6sJ)2?>  
        PPP=[0.5,Xp,0.5]'; fX ^hO+f
 
        PPPP=1-PPP; r JvtE}x1  
        X=PPP.*X1+PPPP.*X2; ^{Vt  
        Y=PPP.*Y1+PPPP.*Y2; $nkvp`A  
        XY2=[X,Y];
9.:]eL  
        figure(3) :,@"I$>*/  
        PlotGraph; d8)ps,  
        hold on e;|:
W A  
        for i=1:(length(R)-1) kp6{QKDj&  
            x1=XY2(i,1); R6(sWN-  
            y1=XY2(i,2); -Kas9\VWEw  
            x2=XY2(i+1,1); mJ_5Vt=  
            y2=XY2(i+1,2); ~CtLSyB  
            plot([x1,x2],[y1,y2],'k'); \?$`dA[  
            hold on 0XcH  
        end 1g{-DIOmn  
        title('遗传算法第10代') cEdf&*_-'I  
        hold on }@)r\t4m  
        for i=1:(length(R)-1) ;S&PLgZ  
            x1=XY1(i,1); }?\8%hK"a7  
            y1=XY1(i,2); H^VNw1.   
            x2=XY1(i+1,1); S'%|40U  
            y2=XY1(i+1,2); xj ?#
]GR  
            plot([x1,x2],[y1,y2],'k','LineWidth',1); 6`c5\G+  
            hold on Mr0<b?I  
        end 2`|gnVw  
    end     K@f@vyw]  
    if counter==20 gwA+%]  
        PPP=[0.5,Xp,0.5]'; VW$a(G_h  
        PPPP=1-PPP; +jEtu[ ;  
        X=PPP.*X1+PPPP.*X2; 65HP9`5Tm  
        Y=PPP.*Y1+PPPP.*Y2; NsPAWI|4  
        XY2=[X,Y]; 7J[DD5
  
        figure(4) {Vw\#/,  
        PlotGraph; Jw4#u5$$Z  
        hold on Jbv[Ql#  
        for i=1:(length(R)-1) 8[k:FGp>  
            x1=XY2(i,1); N4[`pXM6
 
            y2=XY2(i+1,2); <UEt
a>jj  
            plot([x1,x2],[y1,y2],'k'); rexNsKRK_  
            hold on 8gHOs#\  
        end &H4Y`xV^=  
        title('遗传算法第20代') RXi/&'+H  
        hold on iBM;$0Y  
        for i=1:(length(R)-1) H|.cD)&eYy  
            x1=XY1(i,1); FI(iqSJ6  
            y1=XY1(i,2); 8opd0'SNaB  
            x2=XY1(i+1,1); 5LF&C0v  
            y2=XY1(i+1,2); #VvU8"u  
            plot([x1,x2],[y1,y2],'k','LineWidth',1); kS7`g A  
            hold on n@JZ2K4  
        end eT'Z;ZO  
    end r6b;v2!8  
    if counter==30 9hTzi+'S  
        PPP=[0.5,Xp,0.5]'; |zkZF|-  
        PPPP=1-PPP; _.{I1*6Y2  
        X=PPP.*X1+PPPP.*X2; 'vX:)ZDi  
        Y=PPP.*Y1+PPPP.*Y2; 8PB 8h  
        XY2=[X,Y]; A_4.>g  
        figure(5) u_Wftb?9  
        PlotGraph; t=;P1d?E;  
        hold on > u!# 4  
        for i=1:(length(R)-1) ppP0W`p  
            x1=XY2(i,1); v
G{lxPIj  
            y1=XY2(i,2); ?mA%`*=q  
            x2=XY2(i+1,1); @Y&UP  
            y2=XY2(i+1,2); hr/H vB  
            plot([x1,x2],[y1,y2],'k'); KLBX2H2^0  
            hold on $(r/N"6)O2  
        end \) FFV-k5  
        title('遗传算法第30代') q:/3uC7   
        hold on /$U<S"  
        for i=1:(length(R)-1) J#*%r)  
            x1=XY1(i,1); L25v7U  
            y2=XY1(i+1,2); 6
IPQ}/l  
            plot([x1,x2],[y1,y2],'k','LineWidth',1); +F4SU(T  
            hold on +T/T\[  
        end 2.2G79U,  
    end 4Zn"K}q  
    if counter==40 FK+`K<  
        PPP=[0.5,Xp,0.5]'; n/6qc3\5i  
        PPPP=1-PPP; MS~+P'  
        X=PPP.*X1+PPPP.*X2; W^k|*Y|  
        Y=PPP.*Y1+PPPP.*Y2; WAUgbImc{  
        XY2=[X,Y]; E]0}&YG  
        figure(6) iD${7 _  
        PlotGraph; W7` fI*lc  
        hold on $<s;YhM:u)  
        for i=1:(length(R)-1) '@{Mq%`  
            x1=XY2(i,1); c gOkm}h  
            y1=XY2(i,2); V{{Xz:  
            x2=XY2(i+1,1); -<{;.~nI.  
            y2=XY2(i+1,2); YAsE,M+  
            plot([x1,x2],[y1,y2],'k'); R_zQiSwG<  
            hold on 69?wZfj'  
        end s
L=}d[  
        title('遗传算法第40代') Bln($lOz  
        hold on (MGYX_rD  
        for i=1:(length(R)-1) T!%J x.^  
            x1=XY1(i,1); >+FaPym  
            y1=XY1(i,2); KNG7$icG  
            x2=XY1(i+1,1); /wX5>^  
            y2=XY1(i+1,2); nJhaI  
            plot([x1,x2],[y1,y2],'k','LineWidth',1); MJM<  
            hold on >X_5o^s2s  
        end TY`t3  
    end \0fk^  
    if counter==50 VsQ~Y,7  
        PPP=[0.5,Xp,0.5]'; )gHfbUYS  
        PPPP=1-PPP; p9-s'F|@i  
        X=PPP.*X1+PPPP.*X2; mHF?t.y  
        Y=PPP.*Y1+PPPP.*Y2; \\)9QP?  
        XY2=[X,Y]; #8/pYQ;  
        figure(7) ^1-Vd5g  
        PlotGraph; .Y5o&at6s  
        hold on DLH|y%"  
        for i=1:(length(R)-1) fc<~R  
            x1=XY2(i,1); wZ%a:Z4TcM  
            y1=XY2(i,2); ' 4.T1i,  
            x2=XY2(i+1,1); "j/jhe6  
            y2=XY2(i+1,2); mv{bX|.  
            plot([x1,x2],[y1,y2],'k'); C0}@0c  
            hold on Db K(Rh_ K  
        end })yB2Q0  
        title('遗传算法第50代') $]<wQ
H/?_  
        hold on ".7\>8A#a  
        for i=1:(length(R)-1) IwVdx^9  
            x1=XY1(i,1); [J8;V|v  
            y1=XY1(i,2); x(+H1D\W  
            x2=XY1(i+1,1); ?`/DFI'_G  
            y2=XY1(i+1,2); ^N&@7s  
            plot([x1,x2],[y1,y2],'k','LineWidth',1); sXwa`_{  
            hold on \~> .NH-  
        end OsV'&@+G>  
    end     phi9/tO\u  
    LC2(counter+1)=Yp;  \RS ,Y  
    LC1(counter+1)=meanfitness;     Q;?rqi ,  
    %% 第五步：变异 2_GbK-  
    for i=1:N A&S n^mw  
        if Pm>rand&&pos(1)~=i 6hiWgbE  
            AA=farm{i}; ;J,`v5z0:  
            AA(POS)=rand; s3[\&zt  
            farm{i}=AA; :pdl2#5H^  
        end A4daIhP (  
    end     LaX<2]Tx:  
    counter=counter+1; dT7!+)s5-  
    disp(co .. a7}O.NDf  
>zo_}A!  

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