Interaction and topology in distributed multi-agent coordination

Abstract

Interconnected systems have become the recent focus of intense investigation, particularly in the context of autonomous coordination, yielding fundamental advantages in adaptability, scalability, and efficiency compared to single?agent solutions. In this thesis, we investigate the topological assumptions that underly distributed multi?agent coordination, i.e., those properties defining interaction between agents in a network. We focus specifically on the properties of network connectivity and graph rigidity, which exhibit strong influence on fundamental multi?agent behaviors, e.g., joint decision?making, cooperative estimation, formation control, and relative localization. These bases of coordination contribute to the construction of increasingly complex multi?agent systems, and in harnessing the underlying threads of topology, there is hope in solving the future challenges of coordination in a world of robotic ubiquity. Thus, this thesis aims to strike distributed autonomy at its core, by treating assumptions which render theoretical treatments feasible, but which leave implementation relegated to the laboratory. ? Motivated by a case study that illustrates the topological assumptions necessary to solve the probabilistic mapping and tracking problem, we extend the state of the art in mobility control by regulating topology through preemptive mobility, discriminating link addition and deletion to shape spatial interaction under topological constraints. Adopting realistic models of proximity?limited coordination, local controllers are constructed with discrete switching for link discrimination, and attract?repel potential fields which yield constraint satisfying motion. Our mobility scheme acts as a full generalization of classical swarm?like controllers, yielding decision?based, topology?driven coordination. When topological constraints are non?local, as is the case for both connectivity and rigidity, we illustrate how consensus?based decision?making can preserve preemption while maintaining the feasibility of topological constraints. To evaluate the specific constraint of network connectedness, we propose an inverse iteration algorithm that estimates the eigenpair associated with algebraic connectivity. Our solution is fully distributed, scalable, and it improves on the convergence rate issues of the state of the art. Finally, as a case study of heterogeneity, we introduce a hybrid architecture in which a robotic network is dynamically reconfigured to ensure high quality information flow between static nodes while preserving connectivity. In solving this problem, we propose components that couple connectivity?preserving robot?to?flow allocations, with distributed communication optimizing mobility