Distributed Multirobot Exploration and Mapping

Project for the final exam in this paper, we consider the problem of exploring an environment unknown with a team of robots. As in the exploration of single robots, the goal is to minimize the overall exploration time. The key problem to solve in the context of multiple robots is that of choose the appropriate destination points for the individual robots so that can explore different regions of the environment simultaneously. We present an approach for the coordination of multiple robots, which takes into account simultaneously of the cost of reaching a target point and its usefulness. We also describe how our algorithm can be extended to situations in which the communication range of the robots is limited. The filter was used to estimate the positions of the robots particle, assuming a communication with anchors Wi-Fi. The results show that our technique effectively distributes the robots on the environment and allows them to fulfill their mission quickly.

Installing

Depending on the machine you are using:

• Windows
• Mac
• Linux

First delete files with extensions *.mexarchitecturename, for example, for Mac the file will have extension: *.mexmaci64 you need to compile some mex files before starting:

• sens_model_noise.c
• potentialfiled.cpp
• mapgen.cpp

type in terminal:

>> mex -v -output Sens_model_noise sens_model_noise.c
>> mex -v COMPFLAGS='$COMPFLAGS -std=c++14' -output @Map/mapgen ./src/DungeonGenerator/*.cpp
>> mex -v COMPFLAGS='$COMPFLAGS -std=c++14' -output potentialfield ./src/*.cpp

Now you can run the script. If you encounter problems in compiling using the mex command, update Matlab to the latest available release or refer to the official site.

Run

To launch the script, open the Main.m and press "Run", you can customize the simulation by acting on the parameters as below (used in last simulation):

% define number of robots to use
numrobot = 4;
% define base time
basetime = 200;
% increment of time
k = 9;

For a longer simulation we recommend running the ruby script and following the instructions on the screen

$ ruby runsimulation.rb

Launch script, open the Demo.m and press "Run" to view plot and animation.

Report

The report is available here or in the "Report" folder.

How to use

Main.m

%% Main - Start multirobot
close all
clear
clc
addpath('Utility-Mapping')
compilemexlibrary
%% Generate Map
% build a new map with:
% map = Map("New", width, heigth);
% map = Map("New", width, heigth, #landmark, "auto");
% map = Map("New", width, heigth, #landmark, "manual");
% or load an existing one:
% map = Map("Load");
% map = Map("Load", #landamark);
% map = Map("Load", #landamark, "auto");
% map = Map("Load", #landamark, "manual");
map = Map('new', 100, 100);
figure('units','normalized','outerposition',[0 0 1 1]); axis equal
axis([0, 100, 0, 100])
map.plotMap();
print('map100x100','-depsc','-r0')
%% Set-up paramaters simulation
% define number of robots to use
numrobot = 4;
% define base time
basetime = 200;
% increment of time
k = 9;

parfor (k = 1:7, 4)
    for n = 1:5
        multirobot(n, k * 150, map, 1)
    end
end
%% analysis result
Result

Use of classes

Robot class

% set-up simulation parameters
% Sampling time
MdlInit.Ts = 0.05;
% Length of simulation
MdlInit.T = 25;
time = 0:MdlInit.Ts:MdlInit.T;

% Vehicle set-up initial conditions
robot = Robot(1, MdlInit.T, MdlInit.Ts, map.getAvailablePoints());
% set 1st target
robot.setpointtarget(map.getAvailablePoints());

% Perfrom simulation
for indextime = 1:1:length(time)
    if mod(indextime,2) == 0 % simualte laserscan @ 10Hz
        robot.scanenvironment(map.points, map.lines, indextime);
    end
    robot.UnicycleKinematicMatlab(indextime);
end

Map class

% Generate Map
% build a new map with:
% map = Map("New", width, heigth);
% map = Map("New", width, heigth, #landmark, "auto");
% map = Map("New", width, heigth, #landmark, "manual");
% or load an existing one:
% map = Map("Load");
% map = Map("Load", #landamark);
% map = Map("Load", #landamark, "auto");
% map = Map("Load", #landamark, "manual");
% example
map = Map('new', 100, 100);
map.plotMap();

Particle filter class

Within the main cycle after the calculation of the kinematic model insert

if indextime == 1
    pf = Particle_Filter(robot, map.landmark, indextime);
else
    pf{i} = pf{i}.update(robot, indextime);
    robot = robot.setParticleFilterxEst(pf.xEst);
end

Authors

• Argentieri Francesco
• Mazzaglia Giacomo