Abstract
In this thesis, we propose an approach to achieve autonomous task partitioning in swarms of robots. Task partitioning is the process by which tasks are decomposed into sub-tasks and it is often an advantageous way of organizing work in groups of individuals. Therefore, it is interesting to study its application to swarm robotics, in which groups of robots are deployed to collectively carry out a mission. The capability of partitioning tasks autonomously can enhance the flexibility of swarm robotics systems because the robots can adapt the way they decompose and perform their work depending on specific environmental conditions and goals. So far, few studies have been presented on the topic of task partitioning in the context of swarm robotics. Additionally, in all the existing studies, there is no separation between the task partitioning methods and the behavior of the robots and often task partitioning relies on characteristics of the environments in which the robots operate. This limits the applicability of these methods to the specific contexts for which they have been built. The work presented in this thesis represents the first steps towards a general framework for autonomous task partitioning in swarms of robots. We study task partitioning in foraging, since foraging abstracts practical real-world problems. The approach we propose in this thesis is therefore studied in experiments in which the goal is to achieve autonomous task partitioning in foraging. However, in the proposed approach, the task partitioning process relies upon general, task-independent concepts and we are therefore confident that it is applicable in other contexts. We identify two main capabilities that the robots should have: i) being capable of selecting whether to employ task partitioning and ii) defining the sub-tasks of a given task. We propose and study algorithms that endow a swarm of robots with these capabilities.