This search **recipe ** is perfect for robots with memory leaks whose always forget where did they put the charger or the remote controller. You can impress your host by adding this simple functionality to find the family dog, the salt in the kitchen or even save some people in an emergency or disaster situation.

**Ingredients**

- Matlab
- Prior information of the potential locations to search

Here you have the code ready to play !

```
% SIMPLE SEARCH (1-step ahead)
% for lost targets 2D in a static world using range sensors
% Author: Pablo Lanillos ICS TUM
% If you use the code please cite
% Lanillos, P. (2013): Minimum time search of moving targets in uncertain environments. Ph.D. Dissertation, Universidad Complutense de Madrid.
% http://therobotdecision.com/papers/PhD_MinimumTimeSearch_2013_FinalElectronic_[PabloLanillos].pdf
%
% June 2016 www.therobotdecision.com
function simple_search()
fprintf('-------------------------------------------------\n');
fprintf('> Simple search 2D start (greedy 1-step ahead)...\n');
% sensor params
Pdmax = 0.9;
dmax = 2;
sigma = 0.7;
% agent params
delta = 0.5; % constant displacement
z = 10; % z is considered constant
s = [5,5,z]'; % initial location (x,y,z)
%% discretized prior map 2D, initial belief b0, Gaussian centered in the middle of the map
sz = [20,20];
% prepare grid for improve computation efficiency
I=repmat([1:sz(1)],sz(2),1);I=I(:)';
J = repmat([1:sz(2)],[1,sz(1)]);
COV =[2,0;0,4];
x_mu = [I-ceil(sz(1)/2);J-ceil(sz(2)/2)];
bk = zeros(sz(1)*sz(2),1);
for i = 1 : sz(1)*sz(2)
bk(i) = exp(- 1/2 * x_mu(:,i)'/COV * x_mu(:,i));
end
%% Movement params
nsamples = 9; % nomber of movements
mat = [-delta,-delta; ...% 1
-delta, 0; ...% 2
-delta, delta; ...% 3
0, -delta; ...% 4
0, 0; ...% 5
0, delta; ...% 6
delta,-delta; ...% 7
delta, 0; ...% 8
delta, delta ];% 9
track = [s]; % visualization purpose only
cont = 0; found = 0;
%% start search
while (~found && cont < 100)
%% compute discrete forward states
dirs = randperm(nsamples); % random selection
fs = repmat([s(1), s(2)],nsamples,1) + mat(dirs,:); % forward states
fs = [fs,ones(nsamples,1)*s(3)]; % heigth is constant
% check bounds
mask = fs(:,1) > 0 & fs(:,2) > 0 & fs(:,1) < sz(1) & fs(:,2) < sz(2);
fs = fs(mask,:);
%% compute cost funtion of the potential forward states
info = zeros(size(fs,1),1);
for i = 1 : size(fs,1)
cs = fs(i,:);
dk = sqrt((I(1,:)-cs(1)).^2 + (J(1,:)-cs(2)).^2); % distance
obs = Pdmax.* exp((-sigma.*(dk/dmax).^2)); % No detection range sensor
info(i) = 1-sum((1-obs').*bk); % information gain
end
[v,id] = max(info); % selection of action with max information gain
%% simulate movement
s = fs(id,:)';
%% update belief
% observation model
dk = sqrt((I(1,:)-s(1)).^2 + (J(1,:)-s(2)).^2); % distance
obs = Pdmax.* exp((-sigma.*(dk/dmax).^2)); % No detection range sensor
bk = (1-obs').*bk; % Bayesian update
bk = bk./sum(bk); % normalization
% No prediction step -> assuming static world
%% visualize
track = [track,s];
figure(11); clf; hold on; grid on;
surf(reshape(bk, sz)');
shading interp; colormap hot;
height = 0.1;
plot3(track(2,:),track(1,:),ones(size(track,2),1)*height, '-g', 'LineWidth',2);
plot3(track(2,end),track(1,end),height, 'xg', 'LineWidth',2);
plot3([track(2,end),track(2,end)],[track(1,end),track(1,end)],[height,0], 'o-y', 'LineWidth',2);
xlabel('x');
ylabel('y');
zlabel('P');
view(-6,62);
pause(0.5);
%%
cont = cont +1;
end
fprintf('> End search \n');
fprintf('----------------------------------------------\n');
end
```

Et voilá, everything is ready, now any robot can search for a lost object.

**by Orbhe**

src: http://www.mathworks.com/matlabcentral/fileexchange/58671-simple-lost-target-search