Autonomous Exploration and Mapping of Unknown Environments with Teams of Mobile Robots
Dr. Miguel Juliá Cristóbal

This thesis covers the topic of autonomous exploration of unknown environments
using mobile robots. It exposes the state of the art on this subject and classi es
the techniques in terms of cooperation and integration with the SLAM algorithm. 
A comparison of the multiple techniques was performed with a speci cally implemented
software application. The results indicate the most appropriate techniques
for di erent requirements. In addition, the thesis presents two new multi-robot
coordinated and integrated exploration techniques. The rst technique is, as far as
we know, the rst multi-robot behaviour-based reactive exploration method that is
integrated with SLAM. The way to integrate the SLAM is by means of returning
to previously explored zones when the uncertainty in the localization is high. The
robot saves its pose when the localization is precise and, thus, it is able to return
to these poses in order to reduce the uncertainty. However, we observe a loss in
the exploration time eciency due to the appearance of local minima in the potential
eld associated to the behaviour-based model. Furthermore, the technique
is not fully scalable to explore large areas, since the full map has to be processed
in real time. In this sense, a second technique that overcomes these problems was
developed. It consists in a hybrid reactive/deliberative architecture in which the
behaviour-based reactive control is limited to a local area. This new design avoids
local minima and, since it does not use a full map in the reactive control, it is
completely scalable. A deliberative planner is in charge of the long term planning.
To this end, the map is divided using a tree where nodes represent positions with
an associated area. This tree is used in order to decide if the robot should perform
a local reactive exploration or move towards a long term goal.
Finally, the thesis presents three application cases. The first case studies the problem of an automated
search in an unknown environment using a collaborative control scheme in
which the robots receive commands from an operator. In this way, the autonomous
exploration planned by the robot also considers the urgency to explore the area
indicated by the operator. In this way, it takes into account other factors, e.g.,
the localization uncertainty. The second application case studies the problem of
searching dynamic agents. In this sense, it is developed a grid Bayesian lter that
creates a map with the probability to nd a target at each cell of the map. An
adaptation of the deliberative planner used in the hybrid exploration model is used
here in order to plan paths with this new map, thus leading the robot to the areas
where it is likely to nd targets. The third application case studies the problem
of navigation in urban environments. Speci cally, it is focussed in the problem
of safely navigating in a pavement. We provide a solution to avoid falling in the
curbstone and a pavement navigation strategy consisting in a directed exploration
that plan straight paths in the pavement avoiding in this way the dropped kerbs,
and garages or shop entrances.