路徑規劃-粒子群演算法
阿新 • • 發佈:2021-08-07
演算法簡介
演算法與三維路徑規劃結合的思想
演算法流程
程式碼演示:
pso.m
clc
clear
close all
%% 三維路徑規劃模型定義
startPos = [1, 1, 1];
goalPos = [100, 100, 80];
% 隨機定義山峰地圖
mapRange = [100,100,100]; % 地圖長、寬、高範圍
[X,Y,Z] = defMap(mapRange);
%% 初始引數設定
N = 100; % 迭代次數
M = 50; % 粒子數量
pointNum = 3; % 每一個粒子包含三個位置點
w = 1.2; % 慣性權重
c1 = 2; % 社會權重
c2 = 2; % 認知權重
% 粒子位置界限
posBound = [[0,0,0]',mapRange'];
% 粒子速度界限
alpha = 0.1;
velBound(:,2) = alpha*(posBound(:,2) - posBound(:,1));
velBound(:,1) = -velBound(:,2);
%% 種群初始化
% 初始化一個空的粒子結構體
particles.pos= [];
particles.v = [];
particles.fitness = [];
particles.path = [];
particles.Best.pos = [];
particles.Best.fitness = [];
particles.Best.path = [];
% 定義M個粒子的結構體
particles = repmat(particles,M,1);
% 初始化每一代的最優粒子
GlobalBest.fitness = inf;
% 第一代的個體粒子初始化
for i = 1:M
% 粒子按照正態分佈隨機生成
particles(i).pos.x = unifrnd(posBound(1,1),posBound(1 ,2),1,pointNum);
particles(i).pos.y = unifrnd(posBound(2,1),posBound(2,2),1,pointNum);
particles(i).pos.z = unifrnd(posBound(3,1),posBound(3,2),1,pointNum);
% 初始化速度
particles(i).v.x = zeros(1, pointNum);
particles(i).v.y = zeros(1, pointNum);
particles(i).v.z = zeros(1, pointNum);
% 適應度
[flag,fitness,path] = calFitness(startPos, goalPos,X,Y,Z, particles(i).pos);
% 碰撞檢測判斷
if flag == 1
% 若flag=1,表明此路徑將與障礙物相交,則增大適應度值
particles(i).fitness = 1000*fitness;
particles(i).path = path;
else
% 否則,表明可以選擇此路徑
particles(i).fitness = fitness;
particles(i).path = path;
end
% 更新個體粒子的最優
particles(i).Best.pos = particles(i).pos;
particles(i).Best.fitness = particles(i).fitness;
particles(i).Best.path = particles(i).path;
% 更新全域性最優
if particles(i).Best.fitness < GlobalBest.fitness
GlobalBest = particles(i).Best;
end
end
% 初始化每一代的最優適應度,用於畫適應度迭代圖
fitness_beat_iters = zeros(N,1);
%% 迴圈
for iter = 1:N
for i = 1:M
% 更新速度
particles(i).v.x = w*particles(i).v.x ...
+ c1*rand([1,pointNum]).*(particles(i).Best.pos.x-particles(i).pos.x) ...
+ c2*rand([1,pointNum]).*(GlobalBest.pos.x-particles(i).pos.x);
particles(i).v.y = w*particles(i).v.y ...
+ c1*rand([1,pointNum]).*(particles(i).Best.pos.y-particles(i).pos.y) ...
+ c2*rand([1,pointNum]).*(GlobalBest.pos.y-particles(i).pos.y);
particles(i).v.z = w*particles(i).v.z ...
+ c1*rand([1,pointNum]).*(particles(i).Best.pos.z-particles(i).pos.z) ...
+ c2*rand([1,pointNum]).*(GlobalBest.pos.z-particles(i).pos.z);
% 判斷是否位於速度界限以內
particles(i).v.x = min(particles(i).v.x, velBound(1,2));
particles(i).v.x = max(particles(i).v.x, velBound(1,1));
particles(i).v.y = min(particles(i).v.y, velBound(2,2));
particles(i).v.y = max(particles(i).v.y, velBound(2,1));
particles(i).v.z = min(particles(i).v.z, velBound(3,2));
particles(i).v.z = max(particles(i).v.z, velBound(3,1));
% 更新粒子位置
particles(i).pos.x = particles(i).pos.x + particles(i).v.x;
particles(i).pos.y = particles(i).pos.y + particles(i).v.y;
particles(i).pos.z = particles(i).pos.z + particles(i).v.z;
% 判斷是否位於粒子位置界限以內
particles(i).pos.x = max(particles(i).pos.x, posBound(1,1));
particles(i).pos.x = min(particles(i).pos.x, posBound(1,2));
particles(i).pos.y = max(particles(i).pos.y, posBound(2,1));
particles(i).pos.y = min(particles(i).pos.y, posBound(2,2));
particles(i).pos.z = max(particles(i).pos.z, posBound(3,1));
particles(i).pos.z = min(particles(i).pos.z, posBound(3,2));
% 適應度計算
[flag,fitness,path] = calFitness(startPos, goalPos,X,Y,Z, particles(i).pos);
% 碰撞檢測判斷
if flag == 1
% 若flag=1,表明此路徑將與障礙物相交,則增大適應度值
particles(i).fitness = 1000*fitness;
particles(i).path = path;
else
% 否則,表明可以選擇此路徑
particles(i).fitness = fitness;
particles(i).path = path;
end
% 更新個體粒子最優
if particles(i).fitness < particles(i).Best.fitness
particles(i).Best.pos = particles(i).pos;
particles(i).Best.fitness = particles(i).fitness;
particles(i).Best.path = particles(i).path;
% 更新全域性最優粒子
if particles(i).Best.fitness < GlobalBest.fitness
GlobalBest = particles(i).Best;
end
end
end
% 把每一代的最優粒子賦值給fitness_beat_iters
fitness_beat_iters(iter) = GlobalBest.fitness;
% 在命令列視窗顯示每一代的資訊
disp(['第' num2str(iter) '代:' '最優適應度 = ' num2str(fitness_beat_iters(iter))]);
% 畫圖
plotFigure(startPos,goalPos,X,Y,Z,GlobalBest);
pause(0.001);
end
%% 結果展示
% 理論最小適應度:直線距離
fitness_best = norm(startPos - goalPos);
disp([ '理論最優適應度 = ' num2str(fitness_best)]);
% 畫適應度迭代圖
figure
plot(fitness_beat_iters,'LineWidth',2);
xlabel('迭代次數');
ylabel('最優適應度');
calFitness.m
function [flag,fitness,path] = calFitness(startPos, goalPos,X,Y,Z, pos)
% 利用三次樣條擬合散點
x_seq=[startPos(1), pos.x, goalPos(1)];
y_seq=[startPos(2), pos.y, goalPos(2)];
z_seq=[startPos(3), pos.z, goalPos(3)];
k = length(x_seq);
i_seq = linspace(0,1,k);
I_seq = linspace(0,1,100);
X_seq = spline(i_seq,x_seq,I_seq);
Y_seq = spline(i_seq,y_seq,I_seq);
Z_seq = spline(i_seq,z_seq,I_seq);
path = [X_seq', Y_seq', Z_seq'];
% 判斷生成的曲線是否與與障礙物相交
flag = 0;
for i = 2:size(path,1)
x = path(i,1);
y = path(i,2);
z_interp = interp2(X,Y,Z,x,y);
if path(i,3) < z_interp
flag = 1;
break
end
end
%% 計算三次樣條得到的離散點的路徑長度(適應度)
dx = diff(X_seq);
dy = diff(Y_seq);
dz = diff(Z_seq);
fitness = sum(sqrt(dx.^2 + dy.^2 + dz.^2));
影象結果
我們嚮往遠方,卻忽略了此刻的美麗