【路径规划】信息素驱动机器人群动态巡逻【含Matlab源码 3272期】

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⛄一、信息素驱动机器人群动态巡逻简介

1 信息素
信息素是指蚂蚁在寻找食物或者建立巢穴的过程中，通过释放一种特殊的化学物质来与其他蚂蚁进行通讯和协作的一种机制。在蚁群算法中，信息素是一种重要的因素，它可以被看作是一种启发式信息，用于指导蚂蚁在搜索过程中的行动方向和路径选择。信息素的含量会随着蚂蚁的行动而不断更新，这样可以使得蚂蚁更加聚集在一起，形成一个更加高效的蚁群搜索系统。

2 信息素步骤
根据提供的引用内容，信息素步骤可以分为以下几个部分：
（1）初始化信息素：在蚁群算法开始之前，需要初始化信息素矩阵，通常将所有路径上的信息素值初始化为一个较小的正数。
（2）蚂蚁构造路径：每只蚂蚁根据信息素矩阵和启发式函数选择下一个要走的节点，直到构造出一条完整的路径。
（3）更新信息素：每只蚂蚁走完路径后，需要根据路径长度更新信息素矩阵。其中，精英蚂蚁需要重复一遍信息素的更新过程，并将更新值乘上一个参数e，而有限级信息素蚁群算法使用路径等级作为信息素更新的依据。
（4）更新最优解：记录当前最优解和当前次迭代最优解，并在每次迭代结束后更新。

3 信息素驱动机器人群动态巡逻
信息素驱动机器人群动态巡逻是一种机器人协同行为的方法，每个机器人都能够感知环境并通过释放信息素来影响其他机器人的行为。这种方法可以使机器人群体适应环境的动态变化，例如工厂内的机器人巡逻、城市中的智能交通系统等。该方法还存在一些挑战，如信息素的更新策略、多机器人协同等问题，需要进一步的研究和改进。此外，该方法涉及到信号处理、图像处理、路径规划、元胞自动机、无人机等技术领域。

⛄二、部分源代码

tic

clc
clear
clear plot

%display settings
update1=1; %if one, update agent position plot in real time, which of course slows the code
updateevery=1; %update agent position on plot every so many time steps (used with update1)
displayoutputs=1; %if one, output plots will be displayed
exportcount=1; %if one, cells visited at each time step and frequency will be exported to excel
exportfrequency=1; %sets how often data will be sampled (must be limited due to excel column limit)
filename = ‘Periodic_MagicTorus_PointOne.xlsx’; %only needed if exportcount=1

%mode settings
mode=1; %0=gradient following with constant path length, 1=gradient following with Levy flight, 2=pure levy flight
constantlength=1; %only needed for mode=0
levyalpha=1.0; %only needed for mode=1 or mode=2 Levy flight parameter
maxpath=141; %if levy flight selects a path longer than

%general settings
deltat=.1; %time step
gamma=0.0001; %evaporation rate=percent pheromone/second
deposit=1; %deposition rate = units pheromone/second
travel=1; %travel speed = units distance/second
noise=0.1; %magnitude of noise, if gradient strong, noise will be neglible, if weak, noise will be significant
gamma_effective=gammadeltat; %adjusted to time step
deposit_effective=depositdeltat; %adjusted to time step
travel_effective=travel*deltat; %adjusted to time step
coverage=1.0; %program stops when this percent has been visited at least once.
time=10; %maximum time steps allowed
agents=10; %number of randomly initialized agents
magictorus=0; %if zero agents can’t pass through boundaries

%diffusion settings
D=0.001; %diffusion rate
resolution=4; %units per unit length
deltax=1/resolution;
deltay=1/resolution;
bc=0; %0 means constant value - set ‘edge’ value in loop, 1 means constant flux of zero, 2 means periodic (agents+pher)

%check if FTCS diffusion will be stable
if D>deltaxdeltax/(4deltat);
disp(‘diffusion is unstable due to deltax,deltat,diffusion constant’)
pause
end

%search area boundaries
xmin=0;
xmax=100;
ymin=0;
ymax=100;
m=2:(xmax/deltax-1); %used as indices for later diffusion calcs
n=2:(ymax/deltay-1);

%Pop Up Threat settings
popduration=10; %duration of popup threat
popdetection=5; %detection range of agent for pop-up threat
popquantity=round(time/popduration); %number of pop-up occurences

%create grid for pheromone, use pre-assigned resolution
xgv=linspace(xmin,xmax,(xmax-xmin)/deltax);%assume grid cell size of 1 unit
ygv=linspace(ymin,ymax,(ymax-ymin)/deltay);%assume grid cell size of 1 unit

%create grid for area coverage, resolution of one unit
xvv=linspace(xmin,xmax,(xmax-xmin));%assume grid cell size of 1 unit
yvv=linspace(ymin,ymax,(ymax-ymin));%assume grid cell size of 1 unit

% The following q, r, and s while loops are used if you want to loop
% through different parameter combinations, e.g. diffusion rate, evap rate,
% noise, using ‘q’ and ‘r’, or just want to perform multiple statistical runs,
% using ‘s’. By default, these are all set to one
q=1; %multiple set index
qmax=1; %number of sets
while q<=qmax

r=1; %multiple run index
rmax=1; %number of runs
while r<=rmax
    
    s=1; %statistical run, no variation
    smax=1; %number of statistical runs
    while s<=smax;
        %tracks pop-up threats detected and initializes pop-up threats
        smartpopdetected=zeros(1,popquantity); %will track if detected, if one, means detected
        smartpoplocationx=xmax.*rand(1,popquantity);
        smartpoplocationy=ymax.*rand(1,popquantity);
        simplepopdetected=zeros(1,popquantity); %will track if detected, if one, means detected
        simplepoplocationx=xmax.*rand(1,popquantity);
        simplepoplocationy=ymax.*rand(1,popquantity);
        
        %these diffusion constants inside of loops need if D value
        %being varied
        alpha=D*deltat/deltax^2;
        beta=D*deltat/deltay^2;
        
        %initialize agents
        Nx=xmax.*rand(1,agents); %initialize population randomly
        Ny=xmax.*rand(1,agents); %initialize population randomly
        
        pathremaining=zeros(1,agents); %will give remaining path length
        agentheading=zeros(1,agents); %will track agent headings
        pheromone=zeros(numel(xgv),numel(ygv)); %initial matrix of known/unknown cells
        deltapheromone=zeros(numel(xgv),numel(ygv)); %will be used for changes to pheromone in each time step
        visited=zeros(numel(xvv),numel(yvv)); %once a cell has been visited once, will turn to one
        frequency=zeros(numel(xvv),numel(yvv)); %will track how many times each cell has been visited
        
        %monitors cells that have not been visited
        for k=1:numel(Nx)
            currentvisitcellx=floor(Nx(1,k))+1; %finds current cell x
            currentvisitcelly=floor(Ny(1,k))+1; %finds current cell y
            visited(currentvisitcelly,currentvisitcellx)=1;
            frequency(currentvisitcelly,currentvisitcellx)=frequency(currentvisitcelly,currentvisitcellx)+1;
        end
        t=0; %time index
        
        %agent position graphing
        if displayoutputs==1;
            c = linspace(1,10,numel(Nx));
            clf(figure(2))
            clf(figure(3))
            clf(figure(4))
            figure(2); %display position
            scatter(Nx(1,:),Ny(1,:),[],c);
            xlim([xmin xmax])
            ylim([ymin ymax])
            hold on;
        end
        
        u=1; %update index
        loop=1; %loop index
        stray=0; %stray counter
        export=1; %will track when to export data
        
        count=ones(1,time/exportfrequency); %tracks percent of locations visited at least once
        
        while t<=time
            
            %% this section of code calculates the gradient if needed, and updates agent positions
            
            %now find gradient
            Nx_prev=Nx; %will store previous positions
            Ny_prev=Ny; %will store previous positions
            for k=1:numel(Nx); %now update agent positions
                currentcellx=floor(Nx(1,k)/deltax)+1; %finds current cell x %gives column number
                currentcelly=floor(Ny(1,k)/deltay)+1; %finds current cell y %gives row number
                %should never be outside of grid
                if Nx(1,k)<=xmin||Nx(1,k)>xmax||Ny(1,k)<=ymin||Ny(1,k)>ymax %if outside of grid, move back toward center of grid
                    agentheading(k)=atan2(-(Ny(1,k)-50),-(Nx(1,k)-50)); %heading is summation of noise and reverse gradient
                    pathremaining(k)=0;
                    stray=stray+1;
                else %if not outside of grid
                    if pathremaining(k)<=0 %if a new path needs calculated
                        if mode~=2 %if not pure levy flight, calculate gradient
                            up=currentcelly+1; %gives row number
                            down=currentcelly-1; %gives row number
                            left=currentcellx-1; %gives column number
                            right=currentcellx+1; %gives column number
                            %all set to one just to track for special circumstances
                            phero1=1;
                            phero3=1;
                            phero5=1;
                            phero7=1;
                            %look for special conditions at edge of grid
                            if up==ymax/deltay+1;
                                if magictorus==0
                                    phero1=0;
                                else
                                    up=1;
                                end
                            end

⛄三、运行结果

⛄四、matlab版本及参考文献

1 matlab版本
2014a

2 参考文献
[1]田疆,李二超.用于无人机三维航迹规划改进连接型快速扩展随机树算法[J].航空工程进展. 2018,9(04)

3 备注
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