This thesis is about cooperation of multiple robots that have a common
task they should fulfill, i.e., how multi-robot systems behave in cooperative
scenarios. Cooperation is a very important aspect in robotics, because
multiple robots can solve a task more quickly or efficiently in many situations.
Specific points of interest are, how the effectiveness of the group of
robots completing a task can be improved and how the amount of communication
and computational requirements can be reduced. The importance
of this topic lies in applications like search and rescue scenarios, where
time can be a critical factor and a certain robustness and reliability are
required. Further the communication can be limited by various factors
and operating (multiple) robots can be a highly complicated task.
A typical search and rescue mission as considered in this thesis begins
with the deployment of the robot team in an unknown or partly known
environment. The team can be heterogeneous in the sense that it consists
of pairs of air and ground robots that assist each other. The air vehicle –
abbreviated as UAV – stays within vision range of the ground vehicle or
UGV. Therefrom, it provides sensing information with a camera or similar
sensor that might not be available to the UGV due to distance, perspective
or occlusion. A new approach to fully use the available movement range
is presented and analyzed theoretically and in simulations. The UAV
moves according to a dynamic coverage algorithm which is combined with
a tracking controller to guarantee the visibility limitation is kept.
Since the environment is at least partly unknown, an exploration method
is necessary to gather information about the situation and possible targets
or areas of interest. Exploring the unknown regions in a short amount
of time is solved by approaching points on the frontier between known
and unknown territory. To this end, a basic approach for single robot
exploration that uses the traveling salesman problem is extended to multirobot
exploration. The coordination, which is a central aspect of the
cooperative exploration process, is realized with a pairwise optimization
procedure. This new algorithm uses minimum spanning trees for cost
estimation and is inspired by one of the many multi-robot coordination
methods from the related literature. Again, theoretical and simulated as
well as statistical analysis are used as methods to evaluate the approach.
After the exploration is complete, a map of the environment with possible
regions of higher importance is known by the robot team. To stay
useful and ready for any further events, the robots now switch to a monitoring
state where they spread out to cover the area in an optimal manner.
The optimality is measured with a criterion that can be derived into a distributed
control law. This leads to splitting of the robots into areas of
Voronoi cells where each robot has a maximum distance to other robots
and can sense any events within its assigned cell. A new variant of these
Voronoi cells is introduced. They are limited by visibility and depend on
a delta-contraction of the environment, which leads to automatic collision
avoidance. The combination of these two aspects leads to a coverage
control algorithm that works in nonconvex environments and has advantageous
properties compared to related work
