The objective of this thesis is to develop decentralized methods for building robust
multi-agent networks through self-organization. Multi-agent networks appear in a
large number of natural and engineered systems, including but not limited to, biological
networks, social networks, communication systems, transportation systems, power grids,
and robotic swarms. Networked systems typically consist of numerous components that
interact with each other to achieve some collaborative tasks such as flocking, coverage optimization,
load balancing, or distributed estimation, to name a few. Multi-agent networks
are often modeled via interaction graphs, where the nodes represent the agents and the
edges denote direct interactions between the corresponding agents. Interaction graphs play
a significant role in the overall behavior and performance of multi-agent networks. Therefore,
graph theoretic analysis of networked systems has received a considerable amount of
attention within the last decade.
In many applications, network components are likely to face various functional or structural
disturbances including, but not limited to, component failures, noise, or malicious
attacks. Hence, a desirable network property is robustness, which is the ability to perform
reasonably well even when the network is subjected to such perturbations.
In this thesis, robustness in multi-agent networks is pursued in two parts. The first part
presents a decentralized graph reconfiguration scheme for formation of robust interaction
graphs. Particularly, the proposed scheme transforms any interaction graph into a random
regular graph, which is robust to the perturbations of their nodes/links. The second part
presents a decentralized coverage control scheme for optimal protection of networks by
some mobile security resources. As such, the proposed scheme drives a group of arbitrarily
deployed resources to optimal locations on a network in a decentralized fashion. |