In this thesis, an autonomous multi-robot system for indoor exploration in limited network environments is proposed. The specific use case is search and rescue where the operators must have access to the most up-to-date information, necessitating the requirement for communication maintenance. This requirement is satisfied in a novel way by using signal strength measurements collected by the robots to update the network model, thus reducing the overly conservative nature of current approaches. The network model chosen is an adaptation of existing path loss models as they balance complexity and performance given the information available during multi-robot exploration.

The proposed system consists of a central server with multiple robots which perform tasks that are readily distributable, whereas the central server performs complex iterative optimizations such as merging the local maps and running task assignment. While fully distributed architectures offer theoretical benefits such as scalability and robustness, in practice, these benefits are currently not achieved due to the complexity of splitting iterative optimizations such as mapping and task assignment among multiple nodes. This results in distributed architectures requiring more time and bandwidth to solve these problems, which is why this work implements a central architecture. While iterative optimizations are more efficient when performed centrally, scalability is limited due to the branching of possible options during task assignment. To combat this, an action space formulation, called an action graph, is proposed that is unique to each robot and reduces the number of actions by merging similar ones.

Both the dynamic network prediction model and the action graph formulation are shown to hold promise by experimentation. However, more work is needed before real-world use is feasible.