【无人机三维路径规划】基于NSGA2 MPNDS MPNDS2 BPNNIA BPHEIA BPAIMA多种多目标优化算法实现考虑无人机性能的复杂城市地形路径规划附matlab代码

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智能优化算法神经网络预测雷达通信无线传感器电力系统

信号处理图像处理路径规划元胞自动机无人机

🔥 内容介绍

摘要

无人机三维路径规划是无人机自主飞行的关键技术之一。本文提出了一种基于 NSGA2、MPNDS、MPNDS2、BPNNIA、BPHEIA、BPAIMA 多种多目标优化算法实现考虑无人机性能的复杂城市地形路径规划方法。该方法首先建立了无人机三维路径规划模型，然后利用 NSGA2、MPNDS、MPNDS2、BPNNIA、BPHEIA、BPAIMA 等多目标优化算法对路径规划模型进行求解，最后对求解结果进行分析和比较。仿真结果表明，该方法能够有效地解决无人机三维路径规划问题，并能够满足无人机性能要求。

1. 引言

无人机三维路径规划是无人机自主飞行的关键技术之一。无人机三维路径规划是指在三维空间中为无人机规划一条从起点到终点的路径，该路径需要满足一定的约束条件，如避障、能量消耗、飞行时间等。无人机三维路径规划问题是一个复杂的多目标优化问题，需要考虑多个目标函数，如路径长度、飞行时间、能量消耗等。

近年来，无人机三维路径规划问题引起了广泛关注，并提出了多种求解方法。这些方法主要可以分为两类：传统方法和智能优化算法。传统方法包括贪婪算法、蚁群算法、粒子群算法等。智能优化算法包括遗传算法、进化算法、模拟退火算法等。

本文提出了一种基于 NSGA2、MPNDS、MPNDS2、BPNNIA、BPHEIA、BPAIMA 多种多目标优化算法实现考虑无人机性能的复杂城市地形路径规划方法。该方法首先建立了无人机三维路径规划模型，然后利用 NSGA2、MPNDS、MPNDS2、BPNNIA、BPHEIA、BPAIMA 等多目标优化算法对路径规划模型进行求解，最后对求解结果进行分析和比较。

2. 无人机三维路径规划模型

3. 多目标优化算法

多目标优化算法是一种用于求解多目标优化问题的算法。多目标优化算法可以分为两类：传统方法和智能优化算法。

传统方法包括贪婪算法、蚁群算法、粒子群算法等。智能优化算法包括遗传算法、进化算法、模拟退火算法等。

本文采用 NSGA2、MPNDS、MPNDS2、BPNNIA、BPHEIA、BPAIMA 等多目标优化算法对无人机三维路径规划模型进行求解。

📣 部分代码

clear;clc;close all;rng defaultrng(11)addpath(genpath(pwd));%%data_2();data.alpha_trace = 60/360*(2*pi); % 偏航角data.beta_trace = 45/360*(2*pi); % 仰俯角data.map_size=map_size;data.P_crash = 3.42 * 10e-4; % 失控概率data.S_hit=0.0188; % m^2 撞击面积data.R_I = 0.3;  % 阻力系数data.R_vf = 0.27; % 汽车风险data.alpha=10^6; % J 致死动能data.beta = 100; % J data.S_c = 0.5 ; % 遮蔽系数data.g = 9.8 ; % m/s^2data.IOT_pos=IOT_pos;data.m = 1380 ; % g (DJI Phantom4)data.rou_a = 1225 ; % g/m^3(大气密度)data.miu = miu; % 楼高分布参数data.sigma = sigma; % 楼高分布参数data.v = 20; % 20m/sS=[1 1];E=[49 49]; % 起点终点 data.S = S;data.E = E;data.minh=bulid_xyz;data.maxh=141;Bound = E(1)-S(1);dim = Bound*2;data.Bound = Bound;data.map_step=map_step;data.populations_risk=populations_risk;data.road_risk=road_risk;%% pre-calystep = 3;pbase = ystep+1;for i = 1:2*ystep+1    pi = i - pbase;    can=[];    for j = -ystep:1:ystep        if acos([1,pi]*[1,j]'/sqrt(1+pi^2)/sqrt(1+(j)^2))<=data.alpha_trace            can=[can j];        end    end    canselect{i}=can;enddata.canselect = canselect;data.canselectp = pbase;%%tiledlayout(2,2);for h = 30:30:120    nexttile;    Risk_map = zeros(map_size);    Riskproperty_map = zeros(map_size);    for i=1:map_size(1)        for j =1:map_size(2)            Risk_map(i,j)=Risk_map(i,j)+getC_Risk(getR_pf(getV(h,data),data),populations_risk(i,j),data);            Risk_map(i,j)=Risk_map(i,j)+getC_Risk(data.R_vf,road_risk(i,j),data);        end    end    colormap('jet')    contourf(Risk_map)    colorbar;    title(['h=' num2str(h) 'm,' ' Risk of property=' num2str(getC_rpd(h,data))]);end%%problemList={@MPUAV1,@MPUAV2,@MPUAV3,@MPUAV4,@MPUAV5,@MPUAV6};maxiterList={40000,40000,40000,40000,40000,40000};problemMean=zeros(numel(problemList),6);problemStd=zeros(numel(problemList),6);data.lb = [ones(1,dim/2-1).*-1 ones(1,dim/2+1).*0];data.ub = [ones(1,dim/2-1).*ystep ones(1,dim/2+1).*1];data.dim = dim;temp.dec=0;temp.obj=0;for problemIndex= 1:numel(problemList)    TT=30;score=zeros(TT,6);% res_nsga=repmat(temp,1,numel(problemList));% res_nsga2=repmat(temp,1,numel(problemList));% res_mpnds=repmat(temp,1,numel(problemList));% res_mpnds2=repmat(temp,1,numel(problemList));% MPNNIA=repmat(temp,1,numel(problemList));% MPHEIA=repmat(temp,1,numel(problemList));% MPAIMA=repmat(temp,1,numel(problemList));%RS=1:31;RS(30)=[];parfor testtimes = 10:TTclose all;RANDSEED=testtimes;testfit = problemList{problemIndex};popnum=105;maxiter=maxiterList{problemIndex};%% NSGA2rng default;rng(RANDSEED);test_case={@OptAll,testfit,popnum,1,1,maxiter,dim};for i =1:numel(test_case)/7var={'-algorithm',test_case{i,1},'-problem',test_case{i,2},'-N',test_case{i,3},'-save',test_case{i,4},'-run',test_case{i,5}, ...,                        '-evaluation',test_case{i,6},'-D',test_case{i,7},'-data',data};Global = GLOBAL(var{:});Global.Start();endPopulation = MPSELECT(Global.result{2},100,2);[Population2,FrontNo,~] = NDSELECT(Global.result{2},100);Population2=Population2(FrontNo==1);res_nsgadec=reshape([Population.dec],dim,[])';res_nsgaobj=reshape([Population.obj],size(Population(1).obj,2),[])';res_nsga2dec=reshape([Population2.dec],dim,[])';res_nsga2obj=reshape([Population2.obj],size(Population(1).obj,2),[])';%% MPNDSrng default;rng(RANDSEED);test_case={@OptMPNDS,testfit,popnum,1,1,maxiter,dim};for i =1:numel(test_case)/7var={'-algorithm',test_case{i,1},'-problem',test_case{i,2},'-N',test_case{i,3},'-save',test_case{i,4},'-run',test_case{i,5}, ...,                        '-evaluation',test_case{i,6},'-D',test_case{i,7},'-data',data};Global = GLOBAL(var{:});Global.Start();endPopulation= MPSELECT(Global.result{2},100,2);res_mpndsdec=reshape([Population.dec],dim,[])';res_mpndsobj=reshape([Population.obj],size(Population(1).obj,2),[])';%% MPNDS2 rng default;rng(RANDSEED);test_case={@OptMPNDS2,testfit,popnum,1,1,maxiter,dim};for i =1:numel(test_case)/7var={'-algorithm',test_case{i,1},'-problem',test_case{i,2},'-N',test_case{i,3},'-save',test_case{i,4},'-run',test_case{i,5}, ...,                        '-evaluation',test_case{i,6},'-D',test_case{i,7},'-data',data};Global = GLOBAL(var{:});Global.Start();endPopulation= MPSELECT(Global.result{2},100,2);res_mpnds2dec=reshape([Population.dec],dim,[])';res_mpnds2obj=reshape([Population.obj],size(Population(1).obj,2),[])';%% MPNNIArng default;rng(RANDSEED);%popnum = 210;%maxiter=20000;test_case={@MPNNIA,testfit,popnum,1,1,maxiter,dim};for i =1:numel(test_case)/7var={'-algorithm',test_case{i,1},'-problem',test_case{i,2},'-N',test_case{i,3},'-save',test_case{i,4},'-run',test_case{i,5}, ...,                        '-evaluation',test_case{i,6},'-D',test_case{i,7},'-data',data};Global = GLOBAL(var{:});Global.Start();endPopulation= MPSELECT(Global.result{2},100,2);MPNNIAdec=reshape([Population.dec],dim,[])';MPNNIAobj=reshape([Population.obj],size(Population(1).obj,2),[])';%% MPHEIArng default;rng(RANDSEED);%popnum = 210;%maxiter=20000;test_case={@MPHEIA,testfit,popnum,1,1,maxiter,dim};for i =1:numel(test_case)/7var={'-algorithm',test_case{i,1},'-problem',test_case{i,2},'-N',test_case{i,3},'-save',test_case{i,4},'-run',test_case{i,5}, ...,                        '-evaluation',test_case{i,6},'-D',test_case{i,7},'-data',data};Global = GLOBAL(var{:});Global.Start();endPopulation= MPSELECT(Global.result{2},100,2);MPHEIAdec=reshape([Population.dec],dim,[])';MPHEIAobj=reshape([Population.obj],size(Population(1).obj,2),[])';%% MPAIMArng default;rng(RANDSEED);%popnum = 210;%maxiter=20000;test_case={@MPAIMA,testfit,popnum,1,1,maxiter,dim};for i =1:numel(test_case)/7var={'-algorithm',test_case{i,1},'-problem',test_case{i,2},'-N',test_case{i,3},'-save',test_case{i,4},'-run',test_case{i,5}, ...,                        '-evaluation',test_case{i,6},'-D',test_case{i,7},'-data',data};Global = GLOBAL(var{:});Global.Start();endPopulation= MPSELECT(Global.result{2},100,2);MPAIMAdec=reshape([Population.dec],dim,[])';MPAIMAobj=reshape([Population.obj],size(Population(1).obj,2),[])';%%allsol = [res_nsgaobj;res_mpndsobj;res_mpnds2obj;MPNNIAobj;MPHEIAobj;MPAIMAobj];nsga_hv = MPHV(res_nsgaobj,allsol,2);mpnds_hv = MPHV(res_mpndsobj,allsol,2);mpnds2_hv = MPHV(res_mpnds2obj,allsol,2);MPNNIA_hv = MPHV(MPNNIAobj,allsol,2);MPHEIA_hv = MPHV(MPHEIAobj,allsol,2);MPAIMA_hv = MPHV(MPAIMAobj,allsol,2);score(testtimes,:)=[nsga_hv mpnds_hv mpnds2_hv MPNNIA_hv MPHEIA_hv MPAIMA_hv];endproblemMean(problemIndex,:)= mean(score)clear varproblemStd(problemIndex,:)=var(score)end% problemMean =% %     0.0483    0.0682    0.0696    0.0519    0.0673    0.0807%     0.0533    0.1022    0.1051    0.0726    0.1102    0.1106%     0.0519    0.0843    0.0785    0.0610    0.0899    0.0914%     0.0494    0.0589    0.0523    0.0424    0.0539    0.0663%     0.0439    0.0744    0.0771    0.0518    0.0775    0.0788%     0.0369    0.0615    0.0572    0.0403    0.0598    0.0640% % % problemStd =% %     0.0003    0.0002    0.0002    0.0003    0.0004    0.0004%     0.0009    0.0007    0.0006    0.0007    0.0009    0.0010%     0.0006    0.0004    0.0004    0.0005    0.0005    0.0006%     0.0007    0.0003    0.0004    0.0005    0.0005    0.0004%     0.0006    0.0007    0.0007    0.0009    0.0012    0.0008%     0.0004    0.0004    0.0003    0.0003    0.0003    0.0005% problemMean =% %     0.0485    0.0681    0.0689    0.0669    0.0698    0.0800%     0.0530    0.1021    0.1038    0.1042    0.1013    0.1108%     0.0505    0.0817    0.0757    0.0830    0.0795    0.0891%     0.0488    0.0580    0.0513    0.0445    0.0448    0.0654%     0.0451    0.0770    0.0793    0.0636    0.0657    0.0803%     0.0362    0.0610    0.0565    0.0539    0.0533    0.0641% % % problemStd =% %     0.0003    0.0002    0.0003    0.0003    0.0003    0.0004%     0.0009    0.0007    0.0006    0.0012    0.0008    0.0009%     0.0005    0.0004    0.0004    0.0004    0.0004    0.0006%     0.0007    0.0003    0.0004    0.0004    0.0005    0.0004%     0.0007    0.0006    0.0007    0.0009    0.0010    0.0008%     0.0004    0.0005    0.0004    0.0004    0.0004    0.0005