| dc.description.abstract | The field of swarm robotics has been growing fast over the last few years. Using a swarm
of simple and cheap robots has advantages in various tasks. Apart from performance gains
on tasks that allow for parallel execution, simple robots can also be smaller, enabling them
to reach areas that can not be accessed by a larger, more complex robot. Their ability to
cooperate means they can execute complex tasks while offering self-organised adaptation to
changing environments and robustness due to redundancy.
In order to keep individual robots simple, a control algorithm has to keep expensive
communication to a minimum and has to be able to act on little information to keep the
amount of sensors down. The number of sensors and actuators can be reduced even more
when necessary capabilities are spread out over different agents that then combine them by
cooperating. Self-organised differentiation within these heterogeneous groups has to take
the individual abilities of agents into account to improve group performance.
In this thesis it is shown that a homogeneous group of versatile agents can not be easily
replaced by a heterogeneous group, by separating the abilities of the versatile agents into
several specialists. It is shown that no composition of those specialists produces the same
outcome as a homogeneous group on a clustering task. In the second part of this work,
an adaptation mechanism for a group of foragers introduced by Labella et al. (2004) is
analysed in more detail. It does not require communication and needs only the information
on individual success or failure. The algorithm leads to self-organised regulation of group
activity depending on object availability in the environment by adjusting resting times in a
base. A possible variation of this algorithm is introduced which replaces the probabilistic
mechanism with which agents determine to leave the base. It is demonstrated that a direct
calculation of the resting times does not lead to differences in terms of differentiation and
speed of adaptation.
After investigating effects of different parameters on the system, it is shown that there
is no efficiency increase in static environments with constant object density when using a
homogeneous group of agents. Efficiency gains can nevertheless be achieved in dynamic
environments. The algorithm was also reported to lead to higher activity of agents which
have higher performance. It is shown that this leads to efficiency gains in heterogeneous
groups in static and dynamic environments. | en_US |
