[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
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function [L1,XY1,L2,XY2]=JQRLJGH(XX,YY) {e0aH `me  
%% 基于Dijkstra和遗传算法的机器人路径规划演示程序 !thFayq  
% GreenSim团队原创作品，转载请注明 U2\k7I  
% Email:greensim@163.com [DTe
  
%输入参数在函数体内部定义 F#qc#s  
%输出参数为 9,"gXsvx(  
%  L1    由Dijkstra算法得出的最短路径长度 &[yYgfs
p  
%  XY1   由Dijkstra算法得出的最短路径经过节点的坐标 twaH20  
%  L2    由遗传算法得出的最短路径长度 <UGM/+aO  
%  XY2   由遗传算法得出的最短路径经过节点的坐标 ~i>'3j0@k  
%程序输出的图片有 m+ #G*  
%  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） aFh'KPhe  
%  Fig2  由Dijkstra算法得到的最短路径 =OKUSHu@V  
%  Fig3  由遗传算法得到的最短路径 <N=ow"rD  
%  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） sp0_f;bC  
%% 画Fig1 ZOx;]D"s  
figure(1); UM0#S}  
PlotGraph; B>cx[.#!  
title('地形图及网络拓扑结构') \D#+0  
PD=inf*ones(26,26); >:J1Gc  
for i=1:26 p-7?S^!l  
    for j=1:26 gs~u8"B  
        if D(i,j)==1 jX t5.9 t  
            x1=XY(i,5); `1FNs?j  
            y1=XY(i,6); B\;fC's+  
            x2=XY(j,5); 4(,X.GVY/  
            y2=XY(j,6); ";n%^I}  
            dist=((x1-x2)^2+(y1-y2)^2)^0.5; v)*eLX$  
            PD(i,j)=dist; >9<rc[  
        end Ie8K[ >  
    end -YipPo"a  
end Vu<mOuh
 
%% 调用最短路算法求最短路 @Qqf4h  
s=1;%出发点 ~BBh4t&  
t=26;%目标点 C"k]U[%{  
[L,R]=ZuiDuanLu(PD,s,t); kkj_k:Eah  
L1=L(end); Nvd(Tad  
XY1=XY(R,5:6); PK_2  
%% 绘制由最短路算法得到的最短路径 z34+1d  
figure(2); ;\T~Hc}&;  
PlotGraph; B5;94YIN  
hold on ZCfd<NS?  
for i=1:(length(R)-1) b}hQU~,E  
    x1=XY1(i,1); fECmELd  
    y1=XY1(i,2); c&`]O\D-c  
    x2=XY1(i+1,1); QX.U:p5C  
    y2=XY1(i+1,2); g+C~}M_7  
    plot([x1,x2],[y1,y2],'k'); owO&[D/  
    hold on FGpV ]p  
end J]Q-#g'Z  
title('由Dijkstra算法得到的初始路径') !~-@sq  
%% 使用遗传算法进一步寻找最短路 ^)3=WD'!  
%第一步：变量初始化 O@LUM{\  
M=50;%进化代数设置 a]I~.$G   
N=20;%种群规模设置 MLmv+  
Pm=0.3;%变异概率设置 Jd33QL}Hj  
LC1=zeros(1,M); _Vr}ipx-k  
LC2=zeros(1,M); >vuR:4B  
Yp=L1; W9A F}  
%第二步：随机产生初始种群 @ZcI]G%  
X1=XY(R,1); Vz y )jf  
Y1=XY(R,2); #Jfmt~ks'  
X2=XY(R,3); sWP_fb1  
Y2=XY(R,4); U(<~("ocN  
for i=1:N \6/!{D,  
    farm{i}=rand(1,aaa); EDA6b]  
end ip*UujmNyR  
% 以下是进化迭代过程 u>2opI
~m  
counter=0;%设置迭代计数器 }&EdA;/o_  
while counter<M%停止条件为达到最大迭代次数     5N|hsfkx  
    %% 第三步：交叉 lhF)$M  
    %交叉采用双亲双子单点交叉 #5^S@}e  
    newfarm=cell(1,2*N);%用于存储子代的细胞结构 qqu]r  
    Ser=randperm(N);%两两随机配对的配对表 -TyBb]  
    A=farm{Ser(1)};%取出父代A F Zk[w>{  
    B=farm{Ser(2)};%取出父代B p.vxrk`c  
    P0=unidrnd(aaa-1);%随机选择交叉点 1kh()IrA  
    a=[A(:,1:P0),B(:,(P0+1):end)];%产生子代a $,1KD3;+]  
    b=[B(:,1:P0),A(:,(P0+1):end)];%产生子代b \Iz-<:gA'  
    newfarm{2*N-1}=a;%加入子代种群 ZVCa0Km  
    newfarm{2*N}=b; Dh9C9<Ta:  
    for i=1:(N-1) :Z3]Dk;y  
        A=farm{Ser(i)}; H*&!$s.  
        B=farm{Ser(i+1)}; 2:6lr4{uY  
        newfarm{2*i}=b; q9(hn_X@/  
    end k| >zauK  
    FARM=[farm,newfarm];%新旧种群合并     JvtbGPz  
    %% 第四步：选择复制 |b|bL 7nx  
    SER=randperm(2*N);  '5P
:;zw  
    FITNESS=zeros(1,2*N); 8oP"?ew#  
    fitness=zeros(1,N); S$nEflcz  
    for i=1:(2*N) OUm,;WNLf  
        PP=FARM{i}; WAb@d=H{+>  
        FITNESS(i)=MinFun(PP,X1,X2,Y1,Y2);%调用目标函数 AD"L
>7  
    end WAGU|t#."  
    for i=1:N sTECNY=l  
        f1=FITNESS(SER(2*i-1)); :j;_Xw  
        f2=FITNESS(SER(2*i)); &t74T"(d  
        if f1<=f2 .wcKG9u  
        else ezr'"1Ba}  
            farm{i}=FARM{SER(2*i)}; 6W N(Tw  
            fitness(i)=FITNESS(SER(2*i)); 257q%"  
        end y~rtYI  
    end     )`<7qT_BM  
    %记录最佳个体和收敛曲线 xx[l#+:c  
    minfitness=min(fitness); H`jvT]  
    meanfitness=mean(fitness); xGK"`\V  
    if minfitness<Yp \Jr7
Hy1;  
        pos=find(fitness==minfitness); ?"T *{8  
        Xp=farm{pos(1)}; x
)e(g}n  
        Yp=minfitness; U5H5QW+  
    end bbFzmS1  
    if counter==10 }9Awv#+  
        PPP=[0.5,Xp,0.5]'; jp#/]>(9Z  
        PPPP=1-PPP; ljk,R G  
        X=PPP.*X1+PPPP.*X2; >F;
yfv;  
        Y=PPP.*Y1+PPPP.*Y2; PKt;]T0  
        XY2=[X,Y]; /Au7X'}  
        figure(3) 4,7W*mr3(  
        PlotGraph; m%i!;K"{s  
        hold on mUwGr_)wj  
        for i=1:(length(R)-1) IlMst16q5  
            x1=XY2(i,1); .&n;S';"  
            y1=XY2(i,2); lBOxB/`  
            x2=XY2(i+1,1); ?xzDz  
            y2=XY2(i+1,2); 8>ODtKI*  
            plot([x1,x2],[y1,y2],'k'); 4 _Idf  
            hold on ROr| <  
        end EZ)GW%Bm2  
        title('遗传算法第10代') :&$WWv  
        hold on  =E:a\r  
        for i=1:(length(R)-1) 5`1p ?  
            x1=XY1(i,1); XM?C7/^k  
            y1=XY1(i,2); Xe<kd
B3  
            x2=XY1(i+1,1); Hr;\}  
            y2=XY1(i+1,2); Xa&0j&AH  
            plot([x1,x2],[y1,y2],'k','LineWidth',1); ]0myoWpi3  
            hold on r$;u4FR  
        end -y)g}D%  
    end     9lSs;
zm{Q  
    if counter==20 >SHW  
        PPP=[0.5,Xp,0.5]'; wy#5p]!u  
        PPPP=1-PPP; 8
7:V-*8  
        X=PPP.*X1+PPPP.*X2; ;%$wA5"2M  
        Y=PPP.*Y1+PPPP.*Y2; "$N 4S9U  
        XY2=[X,Y]; oJVpJA0IA  
        figure(4) 5t[7taLX\  
        PlotGraph; QhmOO
-Z?  
        hold on -^= JKd&p  
        for i=1:(length(R)-1) zirnur1  
            x1=XY2(i,1); ^97\TmzP{  
            y2=XY2(i+1,2); AR5)Uws  
            plot([x1,x2],[y1,y2],'k'); {@T<eb$d  
            hold on NLO&.Q]#  
        end cW\Y1=Gv|  
        title('遗传算法第20代') q|N4d9/b  
        hold on Xa/]} B  
        for i=1:(length(R)-1) <=PYu:]h  
            x1=XY1(i,1); \Gz 79VW  
            y1=XY1(i,2); hZeF? G)L'  
            x2=XY1(i+1,1); %scQP{%aD  
            y2=XY1(i+1,2); 0*8uo Wt&  
            plot([x1,x2],[y1,y2],'k','LineWidth',1); xs$-^FnD  
            hold on kDK0L3}nr]  
        end t[b@P<F  
    end t%]b`ad  
    if counter==30 E#mp
j~{-  
        PPP=[0.5,Xp,0.5]'; 1F94e)M)"  
        PPPP=1-PPP; A,! YXl[  
        X=PPP.*X1+PPPP.*X2; *Au[{sR  
        Y=PPP.*Y1+PPPP.*Y2; R'p- 4  
        XY2=[X,Y]; u_X(c'aE;  
        figure(5) PgwNEwG  
        PlotGraph; 55vI^SSA  
        hold on x_.}C%  
        for i=1:(length(R)-1) y_N h5  
            x1=XY2(i,1); RWINdJZ  
            y1=XY2(i,2); *aS[^iX?s  
            x2=XY2(i+1,1); nO .:f  
            y2=XY2(i+1,2); h9WyQl7  
            plot([x1,x2],[y1,y2],'k'); S]}W+BF3  
            hold on OK=ANQjs(  
        end )dZ1$MC[  
        title('遗传算法第30代') w.R2' WR  
        hold on t gHXIr}3  
        for i=1:(length(R)-1) uPBtR  
            x1=XY1(i,1); m$bDWxm#e  
            y2=XY1(i+1,2); ]5j1p6;(`  
            plot([x1,x2],[y1,y2],'k','LineWidth',1); vy1N,8a  
            hold on [,|;rt\o>  
        end glgXSOj  
    end u13v@<HGc  
    if counter==40 5r(Y,m"?  
        PPP=[0.5,Xp,0.5]'; 299uZz}Y  
        PPPP=1-PPP; 4+4C0/$Y  
        X=PPP.*X1+PPPP.*X2;
Ts 1  
        Y=PPP.*Y1+PPPP.*Y2; jK-usn  
        XY2=[X,Y]; H5?H{  
        figure(6) ]ppws3*Pa  
        PlotGraph; V.Qy4u7m  
        hold on z)XIA)i6  
        for i=1:(length(R)-1) fGMuml?[ e  
            x1=XY2(i,1); T96M=?wh!  
            y1=XY2(i,2); _"'0^F$I  
            x2=XY2(i+1,1); |n+ `t?L^  
            y2=XY2(i+1,2); F
@Cxjz  
            plot([x1,x2],[y1,y2],'k'); 8c0ugM  
            hold on -q}I; cH  
        end #wP$LKk  
        title('遗传算法第40代') SH#!Y  
        hold on W#lt_2!j  
        for i=1:(length(R)-1) "|W``&pM  
            x1=XY1(i,1); Q!v]njCIB7  
            y1=XY1(i,2); N"&qy3F  
            x2=XY1(i+1,1); vFgX]&bE  
            y2=XY1(i+1,2); j`ybzG^  
            plot([x1,x2],[y1,y2],'k','LineWidth',1); p
28=l5y+  
            hold on >'|Wrz67Z  
        end dEG1[QG  
    end $q
yST  
    if counter==50 +a}>cAj*  
        PPP=[0.5,Xp,0.5]'; Sx}61
?  
        PPPP=1-PPP; R\,qL-Br  
        X=PPP.*X1+PPPP.*X2; t6a$ZN;  
        Y=PPP.*Y1+PPPP.*Y2; S7WT`2  
        XY2=[X,Y]; h$rk]UM/Q  
        figure(7) o1]ZeF  
        PlotGraph; T~b6Zu6  
        hold on 8u4FagQ,  
        for i=1:(length(R)-1) pQ yH`  
            x1=XY2(i,1); 4&+lc*  
            y1=XY2(i,2); f~Q]"I8w  
            x2=XY2(i+1,1); I18<brZJ  
            y2=XY2(i+1,2); ZIikDih1  
            plot([x1,x2],[y1,y2],'k'); g.d~`R@v  
            hold on ^A' Bghy  
        end m.
 "T3K  
        title('遗传算法第50代') Nvj0MD{ X  
        hold on .[8g6:>  
        for i=1:(length(R)-1) BhCOT+i;c  
            x1=XY1(i,1); );oE^3]f  
            y1=XY1(i,2); J^)=8cy  
            x2=XY1(i+1,1); fs6% M]u  
            y2=XY1(i+1,2); ^muPjM+D  
            plot([x1,x2],[y1,y2],'k','LineWidth',1); z{ MO~d9  
            hold on j]bNOC2.L  
        end 4+'d">+|  
    end     w-?|6I}T  
    LC2(counter+1)=Yp; l"app]uVZ  
    LC1(counter+1)=meanfitness;     HA0Rv#p  
    %% 第五步：变异 Yi+$g  
    for i=1:N $61j_;WF`  
        if Pm>rand&&pos(1)~=i R"V^%z;8o  
            AA=farm{i}; wH N5H  
            AA(POS)=rand; ]iE)8X  
            farm{i}=AA; CwQRHi  
        end c&;Xjy  
    end     w!~85""  
    counter=counter+1; &JHqUVs^  
    disp(co .. 5;_&C=[  
HlC[Nu^6U  

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