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1、基于遺傳算法的機器人路徑規(guī)劃MATLAB源碼
算法的思路如下：取各障礙物頂點連線的中點為路徑點，相互連接各路徑點，將機器人移動的起點和終點限制在各路徑點上，利用Dijkstra算法來求網(wǎng)絡(luò)圖的最短路徑，找到從起點P1到終點Pn的最短路徑，由于上述算法使用了連接線中點的條件，不是整個規(guī)劃空間的最優(yōu)路徑，然后利用遺傳算法對找到的最短路徑各個路徑點Pi?(i=1,2,…n)調(diào)整，讓各路徑點在相應(yīng)障礙物端點連線上滑動，利用Pi=?Pi1+ti×(Pi2-Pi1)（ti∈[0,1]?i=1,2,…n）即可確定相應(yīng)的Pi，即為新的路徑點，連接此路徑點為最優(yōu)路徑。
function?[L1,XY
2、1,L2,XY2]=JQRLJGH(XX,YY)
%%?基于Dijkstra和遺傳算法的機器人路徑規(guī)劃演示程序
%輸入?yún)?shù)在函數(shù)體內(nèi)部定義
%輸出參數(shù)為
%??L1????由Dijkstra算法得出的最短路徑長度
%??XY1???由Dijkstra算法得出的最短路徑經(jīng)過節(jié)點的坐標
%??L2????由遺傳算法得出的最短路徑長度
%??XY2???由遺傳算法得出的最短路徑經(jīng)過節(jié)點的坐標
%程序輸出的圖片有
%??Fig1??環(huán)境地圖（包括：邊界、障礙物、障礙物頂點之間的連線、Dijkstra的網(wǎng)絡(luò)圖結(jié)構(gòu)）
%??Fig2??由Dijkstra算法得到的最短路徑
%??Fi
3、g3??由遺傳算法得到的最短路徑
%??Fig4??遺傳算法的收斂曲線（迄今為止找到的最優(yōu)解、種群平均適應(yīng)值）
%%?畫Fig1
figure(1);
PlotGraph;
title('地形圖及網(wǎng)絡(luò)拓撲結(jié)構(gòu)')
PD=inf*ones(26,26);
for?i=1:26
????for?j=1:26
????????if?D(i,j)==1
????????????x1=XY(i,5);
????????????y1=XY(i,6);
????????????x2=XY(j,5);
????????????y2=XY(j,6);
????????????dist
4、=((x1-x2)^2+(y1-y2)^2)^0.5;
????????????PD(i,j)=dist;
????????end
????end
end
%%?調(diào)用最短路算法求最短路
s=1;%出發(fā)點
t=26;%目標點
[L,R]=ZuiDuanLu(PD,s,t);
L1=L(end);
XY1=XY(R,5:6);
%%?繪制由最短路算法得到的最短路徑
figure(2);
PlotGraph;
hold?on
for?i=1:(length(R)-1)
????x1=XY1(i,1);
????y1=XY1(i,2);
????x2=XY1(i+1
5、,1);
????y2=XY1(i+1,2);
????plot([x1,x2],[y1,y2],'k');
????hold?on
end
title('由Dijkstra算法得到的初始路徑')
%%?使用遺傳算法進一步尋找最短路
%第一步：變量初始化
M=50;%進化代數(shù)設(shè)置
N=20;%種群規(guī)模設(shè)置
Pm=0.3;%變異概率設(shè)置
LC1=zeros(1,M);
LC2=zeros(1,M);
Yp=L1;
%第二步：隨機產(chǎn)生初始種群
X1=XY(R,1);
Y1=XY(R,2);
X2=XY(R,3);
Y2=XY(R,4);
for?i=1:N
6、????farm{i}=rand(1,aaa);
end
%?以下是進化迭代過程
counter=0;%設(shè)置迭代計數(shù)器
while?counter
7、,1:P0),B(:,(P0+1):end)];%產(chǎn)生子代a
????b=[B(:,1:P0),A(:,(P0+1):end)];%產(chǎn)生子代b
????newfarm{2*N-1}=a;%加入子代種群
????newfarm{2*N}=b;
????for?i=1:(N-1)
????????A=farm{Ser(i)};
????????B=farm{Ser(i+1)};
????????newfarm{2*i}=b;
????end
????FARM=[farm,newfarm];%新舊種群合并????
????%%?第四步：選擇復(fù)制
????SER=randperm
8、(2*N);
????FITNESS=zeros(1,2*N);
????fitness=zeros(1,N);
????for?i=1:(2*N)
????????PP=FARM{i};
????????FITNESS(i)=MinFun(PP,X1,X2,Y1,Y2);%調(diào)用目標函數(shù)
????end
????for?i=1:N
????????f1=FITNESS(SER(2*i-1));
????????f2=FITNESS(SER(2*i));
????????if?f1<=f2
????????else
????????????farm{i}=FARM{SER
9、(2*i)};
????????????fitness(i)=FITNESS(SER(2*i));
????????end
????end????
????%記錄最佳個體和收斂曲線
????minfitness=min(fitness);
????meanfitness=mean(fitness);
????if?minfitness
10、???????PPP=[0.5,Xp,0.5]';
????????PPPP=1-PPP;
????????X=PPP.*X1+PPPP.*X2;
????????Y=PPP.*Y1+PPPP.*Y2;
????????XY2=[X,Y];
????????figure(3)
????????PlotGraph;
????????hold?on
????????for?i=1:(length(R)-1)
????????????x1=XY2(i,1);
????????????y1=XY2(i,2);
????????????x2=XY2(i+1,1);
???????
11、?????y2=XY2(i+1,2);
????????????plot([x1,x2],[y1,y2],'k');
????????????hold?on
????????end
????????title('遺傳算法第10代')
????????hold?on
????????for?i=1:(length(R)-1)
????????????x1=XY1(i,1);
????????????y1=XY1(i,2);
????????????x2=XY1(i+1,1);
????????????y2=XY1(i+1,2);
????????????plot([x1,x
12、2],[y1,y2],'k','LineWidth',1);
????????????hold?on
????????end
????end????
????if?counter==20
????????PPP=[0.5,Xp,0.5]';
????????PPPP=1-PPP;
????????X=PPP.*X1+PPPP.*X2;
????????Y=PPP.*Y1+PPPP.*Y2;
????????XY2=[X,Y];
????????figure(4)
????????PlotGraph;
????????hold?on
????????for?i=1:(l
13、ength(R)-1)
????????????x1=XY2(i,1);
????????????y2=XY2(i+1,2);
????????????plot([x1,x2],[y1,y2],'k');
????????????hold?on
????????end
????????title('遺傳算法第20代')
????????hold?on
????????for?i=1:(length(R)-1)
????????????x1=XY1(i,1);
????????????y1=XY1(i,2);
????????????x2=XY1(i+1,1);
????
14、????????y2=XY1(i+1,2);
????????????plot([x1,x2],[y1,y2],'k','LineWidth',1);
????????????hold?on
????????end
????end
????if?counter==30
????????PPP=[0.5,Xp,0.5]';
????????PPPP=1-PPP;
????????X=PPP.*X1+PPPP.*X2;
????????Y=PPP.*Y1+PPPP.*Y2;
????????XY2=[X,Y];
????????figure(5)
????????PlotG
15、raph;
????????hold?on
????????for?i=1:(length(R)-1)
????????????x1=XY2(i,1);
????????????y1=XY2(i,2);
????????????x2=XY2(i+1,1);
????????????y2=XY2(i+1,2);
????????????plot([x1,x2],[y1,y2],'k');
????????????hold?on
????????end
????????title('遺傳算法第30代')
????????hold?on
????????for?i=1:(le
16、ngth(R)-1)
????????????x1=XY1(i,1);
????????????y2=XY1(i+1,2);
????????????plot([x1,x2],[y1,y2],'k','LineWidth',1);
????????????hold?on
????????end
????end
????if?counter==40
????????PPP=[0.5,Xp,0.5]';
????????PPPP=1-PPP;
????????X=PPP.*X1+PPPP.*X2;
????????Y=PPP.*Y1+PPPP.*Y2;
????????XY
17、2=[X,Y];
????????figure(6)
????????PlotGraph;
????????hold?on
????????for?i=1:(length(R)-1)
????????????x1=XY2(i,1);
????????????y1=XY2(i,2);
????????????x2=XY2(i+1,1);
????????????y2=XY2(i+1,2);
????????????plot([x1,x2],[y1,y2],'k');
????????????hold?on
????????end
????????title('遺傳算法第4
18、0代')
????????hold?on
????????for?i=1:(length(R)-1)
????????????x1=XY1(i,1);
????????????y1=XY1(i,2);
????????????x2=XY1(i+1,1);
????????????y2=XY1(i+1,2);
????????????plot([x1,x2],[y1,y2],'k','LineWidth',1);
????????????hold?on
????????end
????end
????if?counter==50
????????PPP=[0.5,Xp,0
19、.5]';
????????PPPP=1-PPP;
????????X=PPP.*X1+PPPP.*X2;
????????Y=PPP.*Y1+PPPP.*Y2;
????????XY2=[X,Y];
????????figure(7)
????????PlotGraph;
????????hold?on
????????for?i=1:(length(R)-1)
????????????x1=XY2(i,1);
????????????y1=XY2(i,2);
????????????x2=XY2(i+1,1);
????????????y2=XY2(i+1,2);
20、
????????????plot([x1,x2],[y1,y2],'k');
????????????hold?on
????????end
????????title('遺傳算法第50代')
????????hold?on
????????for?i=1:(length(R)-1)
????????????x1=XY1(i,1);
????????????y1=XY1(i,2);
????????????x2=XY1(i+1,1);
????????????y2=XY1(i+1,2);
????????????plot([x1,x2],[y1,y2],'k','Line
21、Width',1);
????????????hold?on
????????end
????end????
????LC2(counter+1)=Yp;
????LC1(counter+1)=meanfitness;????
????%%?第五步：變異
????for?i=1:N
????????if?Pm>rand&&pos(1)~=i
????????????AA=farm{i};
????????????AA(POS)=rand;
????????????farm{i}=AA;
????????end
????end????
????counter=coun
22、ter+1;
????disp(counter);
end
%%?輸出遺傳算法的優(yōu)化結(jié)果
PPP=[0.5,Xp,0.5]';
PPPP=1-PPP;
X=PPP.*X1+PPPP.*X2;
Y=PPP.*Y1+PPPP.*Y2;
XY2=[X,Y];
L2=Yp;
%%?繪制Fig3
figure(8)
PlotGraph;
hold?on
hold?on
for?i=1:(length(R)-1)
????x1=XY1(i,1);
????y1=XY1(i,2);
????x2=XY1(i+1,1);
????y2=XY1(i+1,2);
????p
23、lot([x1,x2],[y1,y2],'k','LineWidth',1);
????hold?on
end
for?i=1:(length(R)-1)
????x1=XY2(i,1);
????y1=XY2(i,2);
????x2=XY2(i+1,1);
????y2=XY2(i+1,2);
????plot([x1,x2],[y1,y2],'k');
????hold?on
end
title('遺傳算法最終結(jié)果')
figure(9)
PlotGraph;
hold?on
for?i=1:(length(R)-1)
????x1=XY1(i,1);
24、????y1=XY1(i,2);
????x2=XY1(i+1,1);
????y2=XY1(i+1,2);
????plot([x1,x2],[y1,y2],'k','LineWidth',1);
????hold?on
end
hold?on
for?i=1:(length(R)-1)
????x1=XY2(i,1);
????y1=XY2(i,2);
????x2=XY2(i+1,1);
????y2=XY2(i+1,2);
????plot([x1,x2],[y1,y2],'k','LineWidth',2);
????hold?on
end
title('遺傳算法優(yōu)化前后結(jié)果比較')
%%?繪制Fig4
figure(10);
plot(LC1);
hold?on
plot(LC2);
xlabel('迭代次數(shù)');
title('收斂曲線');
錯誤的基于遺傳算法的機器人路徑規(guī)劃MATLAB源碼,求高人指點
